system

Abstract

The system according to this embodiment aims to efficiently provide users with appropriate learning resources for topics they wish to study. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a monitoring unit. The reception unit receives input from the user regarding the topic they wish to learn. The generation unit analyzes the information received from the reception unit and generates appropriate learning resources. The monitoring unit monitors the user's learning progress based on the resources generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to efficiently provide appropriate learning resources for topics that a user wanted to learn.

[0005] The system according to the embodiment aims to efficiently provide appropriate learning resources for topics that a user wants to learn.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a monitoring unit. The reception unit receives input from the user regarding the topic they wish to learn. The generation unit analyzes the information received from the reception unit and generates appropriate learning resources. The monitoring unit monitors the user's learning progress based on the resources generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently provide users with appropriate learning resources for the topics they wish to learn about. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 2� are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The learning support system according to an embodiment of the present invention is a system that generates a variety of resources, including not only text-based learning materials but also visual resources (infographics and videos) and auditory resources (podcasts and audio explanations), when a user inputs a topic or area they wish to learn into the app. This learning support system provides a variety of opportunities for people who want to learn to learn enjoyably and efficiently, regardless of their age or location. For example, a user inputs a topic or area they wish to learn into the app. For example, they might input a topic such as "Medieval European History" or "Basic Programming." This information is then input into the learning support system. Next, the learning support system analyzes the input information and generates the most suitable learning resources for the user. For example, it generates text-based learning materials, infographics, videos, and podcasts related to the history of medieval Europe. This allows the user to learn using a variety of resources. Furthermore, the learning support system monitors the user's learning progress and level of understanding and provides additional resources as needed. For example, if a user lacks understanding of a particular topic, it provides additional learning materials and explanations. This allows the user to learn efficiently. This mechanism provides opportunities for people who want to learn to learn enjoyably and efficiently, regardless of their age or location. For example, it provides learning resources that are easily accessible to everyone, from children to adults, and from urban to rural areas. This allows the learning support system to efficiently input the topics users want to learn, generate optimal learning resources, and monitor learning progress.

[0029] The learning support system according to this embodiment comprises a reception unit, a generation unit, and a monitoring unit. The reception unit receives input from the user on topics they wish to learn. Topics that users wish to learn include, but are not limited to, academic fields, skills, themes, etc. The reception unit allows users to input topics such as "Medieval European History" or "Basic Programming." The generation unit analyzes the information input by the reception unit and generates appropriate learning resources. The generation unit generates, for example, text-based learning materials, visual resources (infographics and videos), and auditory resources (podcasts and audio commentaries). For example, the generation unit can generate text-based learning materials on medieval European history. The generation unit can also generate infographics. Furthermore, the generation unit can generate videos. For example, the generation unit can generate videos on medieval European history. The generation unit can also generate podcasts. For example, the generation unit can generate podcasts on medieval European history. The monitoring unit monitors the user's learning progress based on the resources generated by the generation unit. The monitoring unit can, for example, evaluate the user's learning progress and provide additional learning resources. For example, the monitoring unit can provide additional learning materials or explanations if the user lacks understanding of a particular topic. This allows the learning support system according to the embodiment to efficiently input the topics the user wants to learn, generate optimal learning resources, and monitor learning progress.

[0030] The generation unit can generate text-based learning materials. These materials may include, but are not limited to, PDF format, ebooks, and web pages. For example, the generation unit can generate materials in PDF format. For instance, it can generate PDF materials on medieval European history. The generation unit can also generate ebooks. For example, it can generate ebooks on basic programming. Furthermore, the generation unit can generate web pages. For instance, it can generate web pages on medieval European history. This allows for the provision of learning resources tailored to the user by generating text-based materials. Some or all of the above-described processes in the generation unit may be performed, for example, using a generative AI, or without a generative AI. For example, the generation unit can prompt the generative AI based on a user-inputted topic, and the generative AI can generate text-based learning materials.

[0031] The generation unit can generate visual resources. These visual resources include, but are not limited to, videos, charts, and slides. For example, the generation unit can generate videos. For example, the generation unit can generate videos about medieval Europe. The generation unit can also generate charts. For example, the generation unit can generate charts about basic programming. The generation unit can also generate slides. For example, the generation unit can generate slides about medieval Europe. By generating visual resources, the user can be provided with multifaceted learning resources. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the generation unit can prompt the generative AI based on a topic entered by the user, and the generative AI can generate visual resources.

[0032] The generation unit can generate auditory resources. Auditory resources include, but are not limited to, audio files, podcasts, and audiobooks. For example, the generation unit can generate audio files. For example, the generation unit can generate audio files about medieval Europe. The generation unit can also generate podcasts. For example, the generation unit can generate podcasts about basic programming. The generation unit can also generate audiobooks. For example, the generation unit can generate audiobooks about medieval Europe. By generating auditory resources, the user can be provided with a variety of learning resources. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the generation unit can prompt the generative AI based on a topic entered by the user, and the generative AI can generate auditory resources.

[0033] The monitoring unit can evaluate the user's learning progress and provide additional learning resources. For example, the monitoring unit can evaluate the user's learning progress. This evaluation includes, but is not limited to, test results, study time, and assignment submission status. For example, the monitoring unit can evaluate the user's learning progress based on test results. It can also evaluate the user's learning progress based on study time. Furthermore, the monitoring unit can evaluate the user's learning progress based on assignment submission status. For example, if the user lacks understanding of a particular topic, the monitoring unit can provide additional learning materials or explanations. Additional learning resources include, but are not limited to, supplementary materials, additional practice problems, and reference materials. For example, the monitoring unit can provide supplementary materials. It can also provide additional practice problems. Furthermore, the monitoring unit can provide reference materials. This allows for efficient learning by evaluating the user's learning progress and providing additional resources as needed. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input user learning progress data into the AI, which can then suggest additional learning resources.

[0034] The monitoring unit can assess the user's level of understanding and provide additional learning materials or explanations as needed. For example, the monitoring unit can assess the user's level of understanding. This assessment may include, but is not limited to, quizzes, tests, or feedback. For example, the monitoring unit can assess the user's level of understanding based on quizzes. It can also assess the user's level of understanding based on tests. Furthermore, the monitoring unit can assess the user's level of understanding based on feedback. For example, if the user lacks understanding of a particular topic, the monitoring unit can provide additional learning materials or explanations. These additional materials or explanations may include, but are not limited to, detailed explanatory videos, supplementary materials, or individual tutoring. For example, the monitoring unit can provide detailed explanatory videos. It can also provide supplementary materials. Furthermore, the monitoring unit can provide individual tutoring. This improves the quality of learning by assessing the user's level of understanding and providing additional learning materials or explanations as needed. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input user comprehension data into the AI, which can then suggest additional learning materials and explanations.

[0035] The reception desk can analyze the user's past learning history and suggest the optimal topic input method. For example, the reception desk analyzes the user's past learning history. This analysis includes, but is not limited to, learning time, learning content, and test results. For example, the reception desk can analyze the user's past learning history based on learning time. It can also analyze the user's past learning history based on learning content. Furthermore, it can analyze the user's past learning history based on test results. For example, the reception desk can automatically display topics that the user has frequently studied in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest topics to study at a specific time based on the user's past learning history. In this way, by analyzing the user's past learning history, the optimal topic input method can be suggested. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's past learning history data into the AI, which can then suggest the optimal topic input method.

[0036] The reception system can filter learning topics based on the user's current learning status and areas of interest when the user enters a topic. For example, the reception system can filter based on the user's current learning status and areas of interest. The evaluation of the current learning status includes, but is not limited to, ongoing assignments, study time, and comprehension. For example, the reception system can evaluate the user's current learning status based on ongoing assignments. It can also evaluate the user's current learning status based on study time. Furthermore, it can evaluate the user's current learning status based on comprehension. Identifying areas of interest includes, but is not limited to, past learning history, survey results, and user profile information. For example, the reception system can identify the user's areas of interest based on past learning history. It can also identify the user's areas of interest based on survey results. Furthermore, it can identify the user's areas of interest based on user profile information. For example, the reception system can prioritize displaying new topics related to the topic the user is currently learning. It can also suggest highly relevant topics based on the user's areas of interest. Furthermore, the reception unit can filter and display topics that the user should learn next, based on their learning progress. This allows for the suggestion of highly relevant topics by filtering based on the user's current learning status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input data on the user's current learning status and areas of interest into the AI, which can then perform the filtering.

[0037] The reception desk can prioritize highly relevant topics when users input learning topics, taking into account their geographical location. For example, the reception desk considers the user's geographical location. This includes, but is not limited to, GPS data, IP addresses, and user input information. For example, the reception desk can obtain the user's geographical location based on GPS data. It can also obtain the user's geographical location based on an IP address. Furthermore, it can obtain the user's geographical location based on user input information. For example, if the user is in a specific region, the reception desk can prioritize displaying topics related to that region. If the user is traveling, the reception desk can suggest topics related to their travel destination. Furthermore, if the user is in a specific city, the reception desk can prioritize displaying topics related to the city's history and culture. This allows for the provision of highly relevant topics by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's geographical location data into the AI, which can then suggest highly relevant topics.

[0038] The reception desk can analyze the user's social media activity when a learning topic is entered and suggest relevant topics. For example, the reception desk can analyze the user's social media activity. This analysis includes, but is not limited to, posts, the number of likes, and the attributes of followers. For example, the reception desk can analyze the user's social media activity based on posts. It can also analyze the user's social media activity based on the number of likes. Furthermore, it can analyze the user's social media activity based on the attributes of followers. For example, the reception desk can prioritize displaying topics that the user frequently mentions on social media. It can also suggest topics that the user's social media followers are interested in. Furthermore, the reception desk can suggest relevant learning topics based on the content the user has shared on social media. In this way, relevant topics can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's social media activity data into AI, and the AI ​​can suggest relevant topics.

[0039] The generation unit can adjust the level of detail of resources based on the importance of the learning topic when generating resources. For example, the generation unit evaluates the importance of learning topics. This evaluation includes, but is not limited to, factors such as relevance to learning objectives, user interest, and consistency with the educational curriculum. For example, the generation unit can evaluate the importance of learning topics based on their relevance to learning objectives. It can also evaluate the importance of learning topics based on user interest. Furthermore, it can evaluate the importance of learning topics based on their consistency with the educational curriculum. For example, the generation unit can generate detailed resources for high-importance topics. It can also generate concise resources for low-importance topics. Furthermore, the generation unit can adjust the length and depth of content of resources according to their importance. This allows for the provision of appropriate learning resources by adjusting the level of detail of resources based on the importance of learning topics. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input importance data of learning topics into the AI, allowing the AI ​​to adjust the level of detail of the resources.

[0040] The generation unit can apply different generation algorithms depending on the category of the learning topic when generating resources. For example, the generation unit can categorize learning topics. This categorization includes, but is not limited to, academic disciplines, skill sets, and themes. For example, the generation unit can categorize learning topics based on academic disciplines. It can also categorize learning topics based on skill sets. Furthermore, it can categorize learning topics based on themes. For example, in the case of history topics, the generation unit can generate resources organized chronologically. In the case of science topics, it can also generate resources including experimental data and graphs. Furthermore, in the case of language learning topics, it can generate resources combining audio and text. This allows for the provision of appropriate learning resources by applying different generation algorithms depending on the category of the learning topic. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input learning topic category data into a generation AI, which can then apply different generation algorithms.

[0041] The generation unit can prioritize resources based on the submission timing of learning topics when generating resources. For example, the generation unit evaluates the submission timing of learning topics. This evaluation includes, but is not limited to, the start of the semester, project deadlines, and the user's learning schedule. For example, the generation unit can evaluate the submission timing of learning topics based on the start of the semester. It can also evaluate the submission timing of learning topics based on project deadlines. Furthermore, the generation unit can evaluate the submission timing of learning topics based on the user's learning schedule. For example, the generation unit can prioritize generating resources for topics with approaching deadlines. It can also postpone generating resources for topics with distant submission deadlines. Furthermore, the generation unit can adjust the resource generation order according to the submission timing. This allows for the provision of appropriate learning resources by prioritizing resources based on the submission timing of learning topics. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input learning topic submission timing data into AI, and the AI ​​can determine the resource priority.

[0042] The generation unit can adjust the order of resources based on the relevance of learning topics when generating resources. For example, the generation unit evaluates the relevance of learning topics. This evaluation includes, but is not limited to, commonalities between topics, the user's learning history, and the degree of agreement with the educational curriculum. For example, the generation unit can evaluate the relevance of learning topics based on commonalities between topics. It can also evaluate the relevance of learning topics based on the user's learning history. Furthermore, the generation unit can evaluate the relevance of learning topics based on the degree of agreement with the educational curriculum. For example, the generation unit can prioritize generating resources for highly relevant topics. It can also postpone generating resources for less relevant topics. Furthermore, the generation unit can adjust the order of resource generation according to relevance. This allows for the provision of appropriate learning resources by adjusting the order of resources based on the relevance of learning topics. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input learning topic relevance data into AI, and the AI ​​can adjust the order of resources.

[0043] The monitoring unit can optimize the evaluation algorithm by referring to past learning data when monitoring learning progress. For example, the monitoring unit can refer to past learning data. This includes, but is not limited to, learning time, test results, and assignment submission status. For example, the monitoring unit can refer to past learning data based on learning time. It can also refer to past learning data based on test results. Furthermore, it can refer to past learning data based on assignment submission status. For example, the monitoring unit can apply the optimal evaluation algorithm based on the user's past learning data. The monitoring unit can also analyze the user's learning patterns and adjust the evaluation algorithm. Furthermore, the monitoring unit can optimize the evaluation criteria by referring to the user's past learning outcomes. This allows for optimization of the evaluation algorithm by referring to past learning data, enabling more accurate evaluation. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input past learning data into AI, which can then optimize the evaluation algorithm.

[0044] The monitoring unit can apply different evaluation methods to each category of learning topic when monitoring learning progress. For example, the monitoring unit classifies learning topic categories. Classification of learning topic categories includes, but is not limited to, academic fields, skill sets, and themes. For example, the monitoring unit can classify learning topic categories based on academic fields. It can also classify learning topic categories based on skill sets. Furthermore, the monitoring unit can classify learning topic categories based on themes. For example, in the case of history topics, the monitoring unit can apply evaluation methods organized chronologically. In the case of science topics, the monitoring unit can also apply evaluation methods based on experimental data and graphs. Furthermore, in the case of language learning topics, the monitoring unit can apply evaluation methods that combine audio and text. This allows for appropriate evaluation by applying different evaluation methods to each category of learning topic. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input learning topic category data into AI, and the AI ​​can apply different evaluation methods.

[0045] The monitoring unit can analyze changes in learning progress based on the submission timing of learning topics when monitoring learning progress. The monitoring unit evaluates the submission timing of learning topics, for example. The evaluation of submission timing includes, but is not limited to, the start of the semester, project deadlines, and the user's learning schedule. The monitoring unit can evaluate the submission timing of learning topics based on the start of the semester, for example. The monitoring unit can also evaluate the submission timing of learning topics based on project deadlines. Furthermore, the monitoring unit can evaluate the submission timing of learning topics based on the user's learning schedule. For example, the monitoring unit can analyze changes in progress in detail for topics with upcoming submission dates. The monitoring unit can also analyze changes in progress concisely for topics with distant submission dates. Furthermore, the monitoring unit can adjust the frequency of analyzing changes in progress depending on the submission date. This allows for appropriate progress management by analyzing changes in progress based on the submission timing of learning topics. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input data on the submission timing of learning topics into the AI, which can then analyze changes in progress.

[0046] The monitoring unit can analyze learning progress by referring to relevant market data for the learning topic when monitoring learning progress. For example, the monitoring unit can refer to relevant market data for the learning topic. This includes, but is not limited to, industry reports, market trends, and competitive analysis. For example, the monitoring unit can refer to relevant market data for the learning topic based on industry reports. It can also refer to relevant market data for the learning topic based on market trends. Furthermore, it can refer to relevant market data for the learning topic based on competitive analysis. For example, the monitoring unit can analyze progress based on market data related to the learning topic. It can also evaluate learning progress by referring to market trends. Furthermore, the monitoring unit can analyze progress by considering the market value of the learning topic. This allows for a more accurate analysis of progress by referring to relevant market data for the learning topic. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input relevant market data for the learning topic into AI, and the AI ​​can analyze the progress.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The reception unit can analyze the user's learning style and suggest the most suitable resource format. For example, if the user prefers visual learning, the generation unit can prioritize generating infographics and videos. If the user prefers auditory learning, the generation unit can prioritize generating podcasts and audio explanations. Furthermore, if the user prefers text-based learning, the generation unit can prioritize generating learning materials in PDF or e-book format. This allows for the provision of the most suitable resource format according to the user's learning style, thereby enhancing learning effectiveness.

[0049] The generation unit can prioritize generating resource formats that have been effective in the past, based on the user's learning history. For example, if a user has demonstrated high comprehension using infographics in the past, the generation unit can prioritize generating infographics for new topics. Similarly, if a user has learned effectively using podcasts, the generation unit can prioritize generating podcasts for new topics. Furthermore, if a user has achieved high learning effectiveness using text-based materials, the generation unit can prioritize generating text-based materials for new topics. This maximizes learning effectiveness by providing the most suitable resource format based on the user's past learning history.

[0050] The generation unit can customize the content of resources based on the user's learning objectives. For example, if a user's goal is to pass a specific exam, the generation unit can prioritize generating resources related to that exam. Similarly, if a user's goal is to acquire a specific skill, the generation unit can prioritize generating resources related to that skill. Furthermore, if a user's goal is to complete a specific project, the generation unit can prioritize generating resources related to that project. This allows for the provision of optimal resources tailored to the user's learning objectives, thereby increasing learning efficiency.

[0051] The monitoring unit can provide real-time feedback on the user's learning progress and immediately offer additional resources. For example, if a user lacks understanding of a particular topic, the monitoring unit can immediately provide supplementary materials. Similarly, if a user is struggling with a specific task, the monitoring unit can immediately provide additional practice problems. Furthermore, if a user is taking longer than expected to acquire a particular skill, the monitoring unit can immediately provide reference materials. This allows for immediate feedback based on the user's learning progress, supporting efficient learning.

[0052] The reception desk can analyze the user's learning environment and suggest the most suitable learning topics. For example, if the user is learning in a quiet environment, the reception desk can suggest topics that require concentration. If the user is learning while on the go, the reception desk can suggest topics that can be learned in a short amount of time. Furthermore, if the user is learning in a relaxed environment, the reception desk can suggest topics that allow for relaxed learning. In this way, by suggesting learning topics that are optimal for the user's learning environment, the efficiency of learning can be improved.

[0053] The reception desk can suggest new topics highly relevant to the user's past learning history, based on the user's learning history. For example, if the user has previously studied medieval Europe, the reception desk can suggest topics related to the Renaissance. Similarly, if the user has previously studied basic programming, the reception desk can suggest topics related to data structures and algorithms. Furthermore, if the user has previously studied biology, the reception desk can suggest topics related to genetics and ecology. This approach enhances learning continuity by suggesting relevant new topics based on the user's learning history.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The reception desk receives the topic the user wants to learn. This topic can include academic fields, skills, themes, etc. For example, the user can enter topics such as "Medieval European History" or "Basic Programming." Step 2: The generation unit analyzes the information entered by the reception unit and generates appropriate learning resources. The generation unit generates text-based learning materials, visual resources (infographics and videos), and auditory resources (podcasts and audio commentaries). For example, the generation unit can generate text-based learning materials, videos, and podcasts about medieval European history. Step 3: The monitoring unit monitors the user's learning progress based on the resources generated by the generation unit. The monitoring unit can evaluate the user's learning progress and provide additional learning resources. For example, if the user lacks understanding of a particular topic, it can provide additional learning materials or explanations.

[0056] (Example of form 2) The learning support system according to an embodiment of the present invention is a system that generates a variety of resources, including not only text-based learning materials but also visual resources (infographics and videos) and auditory resources (podcasts and audio explanations), when a user inputs a topic or area they wish to learn into the app. This learning support system provides a variety of opportunities for people who want to learn to learn enjoyably and efficiently, regardless of their age or location. For example, a user inputs a topic or area they wish to learn into the app. For example, they might input a topic such as "Medieval European History" or "Basic Programming." This information is then input into the learning support system. Next, the learning support system analyzes the input information and generates the most suitable learning resources for the user. For example, it generates text-based learning materials, infographics, videos, and podcasts related to the history of medieval Europe. This allows the user to learn using a variety of resources. Furthermore, the learning support system monitors the user's learning progress and level of understanding and provides additional resources as needed. For example, if a user lacks understanding of a particular topic, it provides additional learning materials and explanations. This allows the user to learn efficiently. This mechanism provides opportunities for people who want to learn to learn enjoyably and efficiently, regardless of their age or location. For example, it provides learning resources that are easily accessible to everyone, from children to adults, and from urban to rural areas. This allows the learning support system to efficiently input the topics users want to learn, generate optimal learning resources, and monitor learning progress.

[0057] The learning support system according to this embodiment comprises a reception unit, a generation unit, and a monitoring unit. The reception unit receives input from the user on topics they wish to learn. Topics that users wish to learn include, but are not limited to, academic fields, skills, themes, etc. The reception unit allows users to input topics such as "Medieval European History" or "Basic Programming." The generation unit analyzes the information input by the reception unit and generates appropriate learning resources. The generation unit generates, for example, text-based learning materials, visual resources (infographics and videos), and auditory resources (podcasts and audio commentaries). For example, the generation unit can generate text-based learning materials on medieval European history. The generation unit can also generate infographics. Furthermore, the generation unit can generate videos. For example, the generation unit can generate videos on medieval European history. The generation unit can also generate podcasts. For example, the generation unit can generate podcasts on medieval European history. The monitoring unit monitors the user's learning progress based on the resources generated by the generation unit. The monitoring unit can, for example, evaluate the user's learning progress and provide additional learning resources. For example, the monitoring unit can provide additional learning materials or explanations if the user lacks understanding of a particular topic. This allows the learning support system according to the embodiment to efficiently input the topics the user wants to learn, generate optimal learning resources, and monitor learning progress.

[0058] The generation unit can generate text-based learning materials. These materials may include, but are not limited to, PDF format, ebooks, and web pages. For example, the generation unit can generate materials in PDF format. For instance, it can generate PDF materials on medieval European history. The generation unit can also generate ebooks. For example, it can generate ebooks on basic programming. Furthermore, the generation unit can generate web pages. For instance, it can generate web pages on medieval European history. This allows for the provision of learning resources tailored to the user by generating text-based materials. Some or all of the above-described processes in the generation unit may be performed, for example, using a generative AI, or without a generative AI. For example, the generation unit can prompt the generative AI based on a user-inputted topic, and the generative AI can generate text-based learning materials.

[0059] The generation unit can generate visual resources. These visual resources include, but are not limited to, videos, charts, and slides. For example, the generation unit can generate videos. For example, the generation unit can generate videos about medieval Europe. The generation unit can also generate charts. For example, the generation unit can generate charts about basic programming. The generation unit can also generate slides. For example, the generation unit can generate slides about medieval Europe. By generating visual resources, the user can be provided with multifaceted learning resources. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the generation unit can prompt the generative AI based on a topic entered by the user, and the generative AI can generate visual resources.

[0060] The generation unit can generate auditory resources. Auditory resources include, but are not limited to, audio files, podcasts, and audiobooks. For example, the generation unit can generate audio files. For example, the generation unit can generate audio files about medieval Europe. The generation unit can also generate podcasts. For example, the generation unit can generate podcasts about basic programming. The generation unit can also generate audiobooks. For example, the generation unit can generate audiobooks about medieval Europe. By generating auditory resources, the user can be provided with a variety of learning resources. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the generation unit can prompt the generative AI based on a topic entered by the user, and the generative AI can generate auditory resources.

[0061] The monitoring unit can evaluate the user's learning progress and provide additional learning resources. For example, the monitoring unit can evaluate the user's learning progress. This evaluation includes, but is not limited to, test results, study time, and assignment submission status. For example, the monitoring unit can evaluate the user's learning progress based on test results. It can also evaluate the user's learning progress based on study time. Furthermore, the monitoring unit can evaluate the user's learning progress based on assignment submission status. For example, if the user lacks understanding of a particular topic, the monitoring unit can provide additional learning materials or explanations. Additional learning resources include, but are not limited to, supplementary materials, additional practice problems, and reference materials. For example, the monitoring unit can provide supplementary materials. It can also provide additional practice problems. Furthermore, the monitoring unit can provide reference materials. This allows for efficient learning by evaluating the user's learning progress and providing additional resources as needed. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input user learning progress data into the AI, which can then suggest additional learning resources.

[0062] The monitoring unit can assess the user's level of understanding and provide additional learning materials or explanations as needed. For example, the monitoring unit can assess the user's level of understanding. This assessment may include, but is not limited to, quizzes, tests, or feedback. For example, the monitoring unit can assess the user's level of understanding based on quizzes. It can also assess the user's level of understanding based on tests. Furthermore, the monitoring unit can assess the user's level of understanding based on feedback. For example, if the user lacks understanding of a particular topic, the monitoring unit can provide additional learning materials or explanations. These additional materials or explanations may include, but are not limited to, detailed explanatory videos, supplementary materials, or individual tutoring. For example, the monitoring unit can provide detailed explanatory videos. It can also provide supplementary materials. Furthermore, the monitoring unit can provide individual tutoring. This improves the quality of learning by assessing the user's level of understanding and providing additional learning materials or explanations as needed. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input user comprehension data into the AI, which can then suggest additional learning materials and explanations.

[0063] The reception desk can estimate the user's emotions and adjust the input method for learning topics based on the estimated emotions. For example, the reception desk can estimate the user's emotions. This estimation includes, but is not limited to, facial recognition, voice analysis, and survey results. For example, the reception desk can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions based on survey results. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of learning topics. This allows for the provision of more appropriate input methods by adjusting the input method for learning topics according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the reception unit may be performed using AI, or not using AI. For example, the reception unit may input user emotion data into the generation AI, which can then adjust the input method based on the emotion.

[0064] The reception desk can analyze the user's past learning history and suggest the optimal topic input method. For example, the reception desk analyzes the user's past learning history. This analysis includes, but is not limited to, learning time, learning content, and test results. For example, the reception desk can analyze the user's past learning history based on learning time. It can also analyze the user's past learning history based on learning content. Furthermore, it can analyze the user's past learning history based on test results. For example, the reception desk can automatically display topics that the user has frequently studied in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest topics to study at a specific time based on the user's past learning history. In this way, by analyzing the user's past learning history, the optimal topic input method can be suggested. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's past learning history data into the AI, which can then suggest the optimal topic input method.

[0065] The reception system can filter learning topics based on the user's current learning status and areas of interest when the user enters a topic. For example, the reception system can filter based on the user's current learning status and areas of interest. The evaluation of the current learning status includes, but is not limited to, ongoing assignments, study time, and comprehension. For example, the reception system can evaluate the user's current learning status based on ongoing assignments. It can also evaluate the user's current learning status based on study time. Furthermore, it can evaluate the user's current learning status based on comprehension. Identifying areas of interest includes, but is not limited to, past learning history, survey results, and user profile information. For example, the reception system can identify the user's areas of interest based on past learning history. It can also identify the user's areas of interest based on survey results. Furthermore, it can identify the user's areas of interest based on user profile information. For example, the reception system can prioritize displaying new topics related to the topic the user is currently learning. It can also suggest highly relevant topics based on the user's areas of interest. Furthermore, the reception unit can filter and display topics that the user should learn next, based on their learning progress. This allows for the suggestion of highly relevant topics by filtering based on the user's current learning status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input data on the user's current learning status and areas of interest into the AI, which can then perform the filtering.

[0066] The reception system can estimate the user's emotions and prioritize the input topics based on the estimated emotions. For example, the reception system can estimate the user's emotions. This estimation includes, but is not limited to, facial recognition, voice analysis, and survey results. For example, the reception system can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions based on survey results. For example, if the user is excited, the reception system can prioritize displaying difficult topics. If the user is tired, it can prioritize displaying easy topics. Furthermore, if the user is relaxed, it can prioritize displaying interesting topics. This allows for the provision of more appropriate learning resources by prioritizing topics according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input user sentiment data into a generating AI, which can then determine topic priorities based on the sentiment.

[0067] The reception desk can prioritize highly relevant topics when users input learning topics, taking into account their geographical location. For example, the reception desk considers the user's geographical location. This includes, but is not limited to, GPS data, IP addresses, and user input information. For example, the reception desk can obtain the user's geographical location based on GPS data. It can also obtain the user's geographical location based on an IP address. Furthermore, it can obtain the user's geographical location based on user input information. For example, if the user is in a specific region, the reception desk can prioritize displaying topics related to that region. If the user is traveling, the reception desk can suggest topics related to their travel destination. Furthermore, if the user is in a specific city, the reception desk can prioritize displaying topics related to the city's history and culture. This allows for the provision of highly relevant topics by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's geographical location data into the AI, which can then suggest highly relevant topics.

[0068] The reception desk can analyze the user's social media activity when a learning topic is entered and suggest relevant topics. For example, the reception desk can analyze the user's social media activity. This analysis includes, but is not limited to, posts, the number of likes, and the attributes of followers. For example, the reception desk can analyze the user's social media activity based on posts. It can also analyze the user's social media activity based on the number of likes. Furthermore, it can analyze the user's social media activity based on the attributes of followers. For example, the reception desk can prioritize displaying topics that the user frequently mentions on social media. It can also suggest topics that the user's social media followers are interested in. Furthermore, the reception desk can suggest relevant learning topics based on the content the user has shared on social media. In this way, relevant topics can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's social media activity data into AI, and the AI ​​can suggest relevant topics.

[0069] The generation unit can estimate the user's emotions and adjust the representation of the generated resources based on the estimated user emotions. For example, the generation unit can estimate the user's emotions. This estimation includes, but is not limited to, facial recognition, voice analysis, and survey results. For example, the generation unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, the generation unit can estimate the user's emotions based on survey results. For example, if the user is relaxed, the generation unit can generate visually calming resources. If the user is excited, the generation unit can generate visually stimulating resources. Furthermore, if the user is tired, the generation unit can generate simple and highly visible resources. This allows for the provision of more appropriate learning resources by adjusting the representation of resources according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into a generation AI, which can then adjust how resources are represented based on the emotion.

[0070] The generation unit can adjust the level of detail of resources based on the importance of the learning topic when generating resources. For example, the generation unit evaluates the importance of learning topics. This evaluation includes, but is not limited to, factors such as relevance to learning objectives, user interest, and consistency with the educational curriculum. For example, the generation unit can evaluate the importance of learning topics based on their relevance to learning objectives. It can also evaluate the importance of learning topics based on user interest. Furthermore, it can evaluate the importance of learning topics based on their consistency with the educational curriculum. For example, the generation unit can generate detailed resources for high-importance topics. It can also generate concise resources for low-importance topics. Furthermore, the generation unit can adjust the length and depth of content of resources according to their importance. This allows for the provision of appropriate learning resources by adjusting the level of detail of resources based on the importance of learning topics. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input importance data of learning topics into the AI, allowing the AI ​​to adjust the level of detail of the resources.

[0071] The generation unit can apply different generation algorithms depending on the category of the learning topic when generating resources. For example, the generation unit can categorize learning topics. This categorization includes, but is not limited to, academic disciplines, skill sets, and themes. For example, the generation unit can categorize learning topics based on academic disciplines. It can also categorize learning topics based on skill sets. Furthermore, it can categorize learning topics based on themes. For example, in the case of history topics, the generation unit can generate resources organized chronologically. In the case of science topics, it can also generate resources including experimental data and graphs. Furthermore, in the case of language learning topics, it can generate resources combining audio and text. This allows for the provision of appropriate learning resources by applying different generation algorithms depending on the category of the learning topic. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input learning topic category data into a generation AI, which can then apply different generation algorithms.

[0072] The generation unit can estimate the user's emotions and adjust the length of the resources it generates based on the estimated emotions. For example, the generation unit can estimate the user's emotions. This estimation includes, but is not limited to, facial recognition, voice analysis, and survey results. For example, the generation unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, the generation unit can estimate the user's emotions based on survey results. For example, if the user is in a hurry, the generation unit can generate short, concise resources. If the user is relaxed, the generation unit can generate longer resources with detailed explanations. Furthermore, if the user is excited, the generation unit can generate resources with visually stimulating effects. This allows for the provision of more appropriate learning resources by adjusting the length of resources according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into a generation AI, which can then adjust the length of the resources based on the emotion.

[0073] The generation unit can prioritize resources based on the submission timing of learning topics when generating resources. For example, the generation unit evaluates the submission timing of learning topics. This evaluation includes, but is not limited to, the start of the semester, project deadlines, and the user's learning schedule. For example, the generation unit can evaluate the submission timing of learning topics based on the start of the semester. It can also evaluate the submission timing of learning topics based on project deadlines. Furthermore, the generation unit can evaluate the submission timing of learning topics based on the user's learning schedule. For example, the generation unit can prioritize generating resources for topics with approaching deadlines. It can also postpone generating resources for topics with distant submission deadlines. Furthermore, the generation unit can adjust the resource generation order according to the submission timing. This allows for the provision of appropriate learning resources by prioritizing resources based on the submission timing of learning topics. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input learning topic submission timing data into AI, and the AI ​​can determine the resource priority.

[0074] The generation unit can adjust the order of resources based on the relevance of learning topics when generating resources. For example, the generation unit evaluates the relevance of learning topics. This evaluation includes, but is not limited to, commonalities between topics, the user's learning history, and the degree of agreement with the educational curriculum. For example, the generation unit can evaluate the relevance of learning topics based on commonalities between topics. It can also evaluate the relevance of learning topics based on the user's learning history. Furthermore, the generation unit can evaluate the relevance of learning topics based on the degree of agreement with the educational curriculum. For example, the generation unit can prioritize generating resources for highly relevant topics. It can also postpone generating resources for less relevant topics. Furthermore, the generation unit can adjust the order of resource generation according to relevance. This allows for the provision of appropriate learning resources by adjusting the order of resources based on the relevance of learning topics. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input learning topic relevance data into AI, and the AI ​​can adjust the order of resources.

[0075] The monitoring unit can estimate the user's emotions and adjust the evaluation method for learning progress based on the estimated user emotions. For example, the monitoring unit can estimate the user's emotions. This estimation includes, but is not limited to, facial expression recognition, voice analysis, and survey results. For example, the monitoring unit can estimate the user's emotions using facial expression recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, the monitoring unit can estimate the user's emotions based on survey results. For example, if the user is tense, the monitoring unit can provide a simple and highly visual evaluation method. If the user is relaxed, the monitoring unit can provide a more detailed evaluation method. Furthermore, if the user is in a hurry, the monitoring unit can provide a concise evaluation method. This allows for more appropriate evaluation by adjusting the evaluation method for learning progress according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generating AI, which can then adjust the evaluation method based on the emotion.

[0076] The monitoring unit can optimize the evaluation algorithm by referring to past learning data when monitoring learning progress. For example, the monitoring unit can refer to past learning data. This includes, but is not limited to, learning time, test results, and assignment submission status. For example, the monitoring unit can refer to past learning data based on learning time. It can also refer to past learning data based on test results. Furthermore, it can refer to past learning data based on assignment submission status. For example, the monitoring unit can apply the optimal evaluation algorithm based on the user's past learning data. The monitoring unit can also analyze the user's learning patterns and adjust the evaluation algorithm. Furthermore, the monitoring unit can optimize the evaluation criteria by referring to the user's past learning outcomes. This allows for optimization of the evaluation algorithm by referring to past learning data, enabling more accurate evaluation. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input past learning data into AI, which can then optimize the evaluation algorithm.

[0077] The monitoring unit can apply different evaluation methods to each category of learning topic when monitoring learning progress. For example, the monitoring unit classifies learning topic categories. Classification of learning topic categories includes, but is not limited to, academic fields, skill sets, and themes. For example, the monitoring unit can classify learning topic categories based on academic fields. It can also classify learning topic categories based on skill sets. Furthermore, the monitoring unit can classify learning topic categories based on themes. For example, in the case of history topics, the monitoring unit can apply evaluation methods organized chronologically. In the case of science topics, the monitoring unit can also apply evaluation methods based on experimental data and graphs. Furthermore, in the case of language learning topics, the monitoring unit can apply evaluation methods that combine audio and text. This allows for appropriate evaluation by applying different evaluation methods to each category of learning topic. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input learning topic category data into AI, and the AI ​​can apply different evaluation methods.

[0078] The monitoring unit can estimate the user's emotions and adjust the display method of learning progress based on the estimated user emotions. For example, the monitoring unit can estimate the user's emotions. This estimation includes, but is not limited to, facial expression recognition, voice analysis, and survey results. For example, the monitoring unit can estimate the user's emotions using facial expression recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, the monitoring unit can estimate the user's emotions based on survey results. For example, if the user is tense, the monitoring unit can provide a simple and highly visible display method. If the user is relaxed, the monitoring unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can provide a concise display method. This allows for more appropriate display by adjusting the learning progress display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generating AI, which can then adjust the display method based on the emotion.

[0079] The monitoring unit can analyze changes in learning progress based on the submission timing of learning topics when monitoring learning progress. The monitoring unit evaluates the submission timing of learning topics, for example. The evaluation of submission timing includes, but is not limited to, the start of the semester, project deadlines, and the user's learning schedule. The monitoring unit can evaluate the submission timing of learning topics based on the start of the semester, for example. The monitoring unit can also evaluate the submission timing of learning topics based on project deadlines. Furthermore, the monitoring unit can evaluate the submission timing of learning topics based on the user's learning schedule. For example, the monitoring unit can analyze changes in progress in detail for topics with upcoming submission dates. The monitoring unit can also analyze changes in progress concisely for topics with distant submission dates. Furthermore, the monitoring unit can adjust the frequency of analyzing changes in progress depending on the submission date. This allows for appropriate progress management by analyzing changes in progress based on the submission timing of learning topics. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input data on the submission timing of learning topics into the AI, which can then analyze changes in progress.

[0080] The monitoring unit can analyze learning progress by referring to relevant market data for the learning topic when monitoring learning progress. For example, the monitoring unit can refer to relevant market data for the learning topic. This includes, but is not limited to, industry reports, market trends, and competitive analysis. For example, the monitoring unit can refer to relevant market data for the learning topic based on industry reports. It can also refer to relevant market data for the learning topic based on market trends. Furthermore, it can refer to relevant market data for the learning topic based on competitive analysis. For example, the monitoring unit can analyze progress based on market data related to the learning topic. It can also evaluate learning progress by referring to market trends. Furthermore, the monitoring unit can analyze progress by considering the market value of the learning topic. This allows for a more accurate analysis of progress by referring to relevant market data for the learning topic. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input relevant market data for the learning topic into AI, and the AI ​​can analyze the progress. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, generation unit, and monitoring unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, where the user can input the topic they wish to learn. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the input information and generates appropriate learning resources. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which monitors the user's learning progress and provides additional resources as needed. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, generation unit, and monitoring unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which allows the user to input the topic they wish to learn by voice. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the input information and generates appropriate learning resources. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which monitors the user's learning progress and provides additional resources as needed. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, generation unit, and monitoring unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input the topic they wish to learn by voice. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the input information and generates appropriate learning resources. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which monitors the user's learning progress and provides additional resources as needed. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, generation unit, and monitoring unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, which allows the user to input the topic they wish to learn by voice. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the input information and generates appropriate learning resources. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which monitors the user's learning progress and provides additional resources as needed.

[0081] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0082] The reception unit can analyze the user's learning style and suggest the most suitable resource format. For example, if the user prefers visual learning, the generation unit can prioritize generating infographics and videos. If the user prefers auditory learning, the generation unit can prioritize generating podcasts and audio explanations. Furthermore, if the user prefers text-based learning, the generation unit can prioritize generating learning materials in PDF or e-book format. This allows for the provision of the most suitable resource format according to the user's learning style, thereby enhancing learning effectiveness.

[0083] The generation unit can prioritize generating resource formats that have been effective in the past, based on the user's learning history. For example, if a user has demonstrated high comprehension using infographics in the past, the generation unit can prioritize generating infographics for new topics. Similarly, if a user has learned effectively using podcasts, the generation unit can prioritize generating podcasts for new topics. Furthermore, if a user has achieved high learning effectiveness using text-based materials, the generation unit can prioritize generating text-based materials for new topics. This maximizes learning effectiveness by providing the most suitable resource format based on the user's past learning history.

[0084] The generation unit can estimate the user's emotions and adjust the difficulty of resources based on those emotions. For example, if the user is stressed, the generation unit can generate easy resources. If the user is relaxed, the generation unit can generate more difficult resources. Furthermore, if the user is excited, the generation unit can generate challenging resources. By adjusting the difficulty of resources according to the user's emotions, it becomes easier to maintain learning motivation.

[0085] The generation unit can customize the content of resources based on the user's learning objectives. For example, if a user's goal is to pass a specific exam, the generation unit can prioritize generating resources related to that exam. Similarly, if a user's goal is to acquire a specific skill, the generation unit can prioritize generating resources related to that skill. Furthermore, if a user's goal is to complete a specific project, the generation unit can prioritize generating resources related to that project. This allows for the provision of optimal resources tailored to the user's learning objectives, thereby increasing learning efficiency.

[0086] The monitoring unit can provide real-time feedback on the user's learning progress and immediately offer additional resources. For example, if a user lacks understanding of a particular topic, the monitoring unit can immediately provide supplementary materials. Similarly, if a user is struggling with a specific task, the monitoring unit can immediately provide additional practice problems. Furthermore, if a user is taking longer than expected to acquire a particular skill, the monitoring unit can immediately provide reference materials. This allows for immediate feedback based on the user's learning progress, supporting efficient learning.

[0087] The monitoring unit can estimate the user's emotions and adjust the feedback method for learning progress based on the estimated emotions. For example, if the user is nervous, the monitoring unit can provide simple, positive feedback. If the user is relaxed, the monitoring unit can provide detailed feedback. Furthermore, if the user is in a hurry, the monitoring unit can provide concise feedback. In this way, by adjusting the feedback method according to the user's emotions, more appropriate feedback can be provided.

[0088] The reception desk can analyze the user's learning environment and suggest the most suitable learning topics. For example, if the user is learning in a quiet environment, the reception desk can suggest topics that require concentration. If the user is learning while on the go, the reception desk can suggest topics that can be learned in a short amount of time. Furthermore, if the user is learning in a relaxed environment, the reception desk can suggest topics that allow for relaxed learning. In this way, by suggesting learning topics that are optimal for the user's learning environment, the efficiency of learning can be improved.

[0089] The reception desk can estimate the user's emotions and adjust how learning topics are suggested based on those estimates. For example, if the user is stressed, the reception desk can suggest relaxing topics. If the user is relaxed, the reception desk can suggest challenging topics. Furthermore, if the user is excited, the reception desk can suggest interesting topics. By adjusting how learning topics are suggested according to the user's emotions, more appropriate learning topics can be provided.

[0090] The reception desk can suggest new topics highly relevant to the user's past learning history, based on the user's learning history. For example, if the user has previously studied medieval Europe, the reception desk can suggest topics related to the Renaissance. Similarly, if the user has previously studied basic programming, the reception desk can suggest topics related to data structures and algorithms. Furthermore, if the user has previously studied biology, the reception desk can suggest topics related to genetics and ecology. This approach enhances learning continuity by suggesting relevant new topics based on the user's learning history.

[0091] The reception desk can estimate the user's emotions and adjust the difficulty level of learning topics based on those estimates. For example, if the user is stressed, the reception desk can suggest easy topics. If the user is relaxed, the reception desk can suggest more difficult topics. Furthermore, if the user is excited, the reception desk can suggest challenging topics. By adjusting the difficulty level of learning topics according to the user's emotions, it becomes easier to maintain motivation for learning.

[0092] The following briefly describes the processing flow for example form 2.

[0093] Step 1: The reception desk receives the topic the user wants to learn. This topic can include academic fields, skills, themes, etc. For example, the user can enter topics such as "Medieval European History" or "Basic Programming." Step 2: The generation unit analyzes the information entered by the reception unit and generates appropriate learning resources. The generation unit generates text-based learning materials, visual resources (infographics and videos), and auditory resources (podcasts and audio commentaries). For example, the generation unit can generate text-based learning materials, videos, and podcasts about medieval European history. Step 3: The monitoring unit monitors the user's learning progress based on the resources generated by the generation unit. The monitoring unit can evaluate the user's learning progress and provide additional learning resources. For example, if the user lacks understanding of a particular topic, it can provide additional learning materials or explanations.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0096] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0098] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0099] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0104] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0105] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0106] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0107] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0108] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0114] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0115] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0130] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0131] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0138] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0149] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0150] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0151] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0155] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0156] 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.

[0157] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0158] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0159] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0160] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0162] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0163] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0164] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users input the topics they want to learn, A generation unit analyzes the information input by the reception unit and generates appropriate learning resources, The system includes a monitoring unit that monitors the user's learning progress based on the resources generated by the generation unit. A system characterized by the following features.

2. The generating unit is Generate text-based learning materials The system according to feature 1.

3. The generating unit is Generate visual resources The system according to feature 1.

4. The generating unit is Generate auditory resources The system according to feature 1.

5. The monitoring unit, Evaluate the user's learning progress and provide additional learning resources. The system according to feature 1.

6. The monitoring unit, Evaluate the user's level of understanding and provide additional learning materials or explanations as needed. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how learning topics are entered based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past learning history and suggests appropriate topic input methods. The system according to feature 1.

9. The aforementioned reception unit is When users enter learning topics, the system filters them based on their current learning status and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's sentiment and prioritizes the input topics based on the estimated user sentiment. The system according to feature 1.

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