system

Abstract

The system according to this embodiment aims to provide appropriate practice problems based on the user's learning progress. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects the user's learning data. The generation unit analyzes the data collected by the collection unit and generates practice problems based on the user's learning progress. The provision unit provides the practice problems 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, the method 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 conventional technology, it has not been fully achieved to provide appropriate simulation problems based on the learning progress of a user, and there is room for improvement.

[0005] The system according to an embodiment aims to provide appropriate simulation problems based on the learning progress of a user.

Means for Solving the Problems

[0006] The system according to an embodiment includes a collection unit, a generation unit, and a provision unit. The collection unit collects learning data of a user. The generation unit analyzes the data collected by the collection unit and generates a simulation problem based on the learning progress of the user. The provision unit provides the simulation problem generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment can provide appropriate practice problems based on the user's learning progress. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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) An online learning platform according to an embodiment of the present invention is a system that utilizes generative AI to generate practice problems tailored to the user's learning progress, enabling efficient learning of knowledge for cultural heritage preservation. When a user begins learning, the generative AI analyzes the user's learning progress and generates appropriate practice problems. Furthermore, the platform is designed to promote interactive learning among users, allowing users to share knowledge and engage in discussions with others. The generative AI also analyzes the user's learning data and evaluates their learning progress and understanding. For example, when a user begins learning, the generative AI analyzes the user's learning progress and generates appropriate practice problems. Next, the platform is designed to promote interactive learning among users, allowing users to share knowledge and engage in discussions with others. Furthermore, the generative AI analyzes the user's learning data and evaluates their learning progress and understanding. In this way, users can work on problems tailored to their learning progress and efficiently advance their learning. Thus, the online learning platform can provide practice problems tailored to the user's learning progress and support efficient learning.

[0029] The online learning platform according to this embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects user learning data. The collection unit can collect learning data in the form of, for example, text data, audio data, or image data. For example, the collection unit collects text data entered by the user. The collection unit can also collect user audio data. Furthermore, the collection unit can also collect image data uploaded by the user. The generation unit analyzes the data collected by the collection unit and generates practice problems based on the user's learning progress. The generation unit can analyze the data using, for example, statistical analysis or machine learning algorithms. For example, the generation unit can evaluate the user's learning progress using statistical analysis. Furthermore, the generation unit can also analyze the data using machine learning algorithms and evaluate the user's learning progress. Furthermore, the generation unit can also generate practice problems using a generation AI. For example, the generation AI generates practice problems using a text generation AI (e.g., LLM). Furthermore, the generation AI can also generate practice problems using a multimodal generation AI. The provision unit provides the practice problems generated by the generation unit. The provider can provide practice problems in various forms, such as online or printed materials. For example, the provider can provide practice problems through an online platform. The provider can also provide practice problems in printed form. Furthermore, the provider can send practice problems via email. This allows the online learning platform according to the embodiment to provide practice problems tailored to the user's learning progress, supporting efficient learning.

[0030] The data collection unit collects user learning data. This data can be collected in various formats, such as text, audio, and image data. Specifically, when collecting user-entered text data, it acquires the content entered through online forms or chatbots in real time. The data collection unit can also collect user audio data. Audio data collection involves recording using a microphone and converting it to text using speech recognition technology. Furthermore, the data collection unit can collect image data uploaded by users. Image data collection includes a function for users to upload photos they have taken or scanned documents, and this data is stored in cloud storage. The data collection unit centrally manages these diverse data formats and builds a user-specific database. Additionally, the data collection unit adjusts the frequency and timing of data collection to enable flexible data collection tailored to the user's learning progress. For example, it could set up automatic data collection after completing a specific learning module, or provide an interface that allows users to voluntarily upload data. This allows the data collection unit to closely monitor user learning activities and collect data that meets individual learning needs.

[0031] The generation unit analyzes the data collected by the collection unit and generates practice questions based on the user's learning progress. The generation unit can analyze the data using, for example, statistical analysis or machine learning algorithms. Specifically, when evaluating the user's learning progress using statistical analysis, it aggregates data such as learning time, correct answer rate, and error patterns to identify the user's strengths and weaknesses. The generation unit can also analyze the data using machine learning algorithms to evaluate the user's learning progress. Machine learning algorithms model the user's learning patterns based on past learning data and predict future learning outcomes. Furthermore, the generation unit can also generate practice questions using generative AI. The generative AI can generate practice questions using, for example, text generation AI (e.g., LLM). LLM uses natural language processing technology based on a large amount of learning data to generate question texts that are appropriate for the user's learning content. The generative AI can also generate practice questions using multimodal generative AI. Multimodal generative AI integrates and analyzes multiple data formats such as text, images, and audio to generate practice questions that include visual questions and audio instructions. This allows the generation unit to provide a variety of practice problems tailored to the user's learning progress, maximizing learning effectiveness. Furthermore, the generation unit can continuously optimize the difficulty level and content of the generated practice problems based on user feedback.

[0032] The provisioning unit provides practice problems generated by the generation unit. The provisioning unit can provide practice problems in various forms, such as online or printed materials. Specifically, when providing practice problems through an online platform, it designs an interface that is easy for users to access and displays the practice problems organized by category. The provisioning unit can also provide practice problems in printed form. For printed materials, the practice problems are made available for download in PDF format so that users can study at home and print them. Furthermore, the provisioning unit can also send practice problems via email. For email delivery, practice problems are sent periodically to the user's registered email address to support their learning progress. By combining these delivery methods, the provisioning unit can provide flexible learning support tailored to the user's learning style and environment. For example, by using both online platform delivery and email delivery, it can create an environment where users can study anytime, anywhere. In addition, the provisioning unit can adjust the frequency and content of practice problems according to the user's learning progress to meet individual learning needs. This allows the provisioning unit to provide users with optimal learning resources and support efficient learning.

[0033] The discussion unit can promote interactive learning among users. The discussion unit can promote interactive learning through methods such as chat or video conferencing. The discussion unit can facilitate dialogue among users through chat, for example. The discussion unit can also facilitate dialogue among users through video conferencing. Furthermore, the discussion unit can also facilitate dialogue among users through forums. This allows users to share knowledge and engage in discussions. Some or all of the above-described processes in the discussion unit may be performed using, for example, generative AI, or without generative AI. For example, the discussion unit can input user dialogue data into a generative AI and have the generative AI manage the flow of the dialogue.

[0034] The generation unit can analyze the user's learning progress and generate appropriate practice questions. For example, the generation unit can analyze the user's learning progress and generate appropriate practice questions. For example, the generation unit can analyze the user's learning progress and generate appropriate practice questions. For example, the generation unit can analyze the user's learning progress and generate appropriate practice questions. This improves learning effectiveness by generating appropriate practice questions based on the user's learning progress. 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 the user's learning data into a generation AI and have the generation AI generate practice questions.

[0035] The provisioning unit can provide the user with the generated practice problems. The provisioning unit can, for example, provide the user with the generated practice problems. The provisioning unit can, for example, provide the user with the generated practice problems. The provisioning unit can, for example, provide the user with the generated practice problems. This supports the user's learning progress by providing them with the generated practice problems. Some or all of the above processing in the provisioning unit may be performed using a generational AI, for example, or without a generational AI. For example, the provisioning unit can input the generated practice problems into a generational AI and have the generational AI perform the task of providing the practice problems.

[0036] The discussion unit allows users to share knowledge and engage in discussions. The discussion unit allows users to share knowledge and engage in discussions, for example. The discussion unit allows users to share knowledge and engage in discussions, for example. The discussion unit allows users to share knowledge and engage in discussions, for example. This improves learning effectiveness by allowing users to share knowledge and engage in discussions. Some or all of the above-described processes in the discussion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discussion unit can input user dialogue data into a generative AI and have the generative AI manage the flow of the dialogue.

[0037] The data collection unit can analyze the user's past learning history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data using learning methods that were effective for the user in the past. For example, the data collection unit can focus on collecting data in areas where the user struggled in the past. For example, the data collection unit can analyze the user's learning patterns and collect data at the most efficient timing. This allows the optimal data collection method to be selected by analyzing the user's past learning history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's learning history data into a generative AI and have the generative AI select the optimal data collection method.

[0038] The data collection unit can filter learning data based on the user's current learning environment and areas of interest. For example, if the user is learning in a quiet environment, the data collection unit can collect data to enhance concentration. For example, if the user is interested in a particular field, the data collection unit can prioritize collecting data related to that field. For example, if the user is learning in a group, the data collection unit can collect data useful for collaborative work. By filtering the data based on the user's learning environment and areas of interest, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input the user's learning environment data into a generative AI and have the generative AI perform the filtering.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting training data. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to cultural heritage in that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to cultural heritage in the destination. For example, if the user is at home, the data collection unit can prioritize the collection of data related to cultural heritage that is accessible online. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI and have the generative AI perform the collection of highly relevant data.

[0040] The data collection unit can analyze the user's social media activity and collect relevant data when collecting training data. For example, the data collection unit can collect data based on the user's interests shared on social media. For example, the data collection unit can analyze the content of posts from accounts the user follows and collect relevant data. For example, the data collection unit can collect data based on the topics of online communities the user participates in. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media data into a generative AI and have the generative AI perform the collection of relevant data.

[0041] The generation unit can customize the content of the practice questions based on the user's learning history during the generation process. For example, the generation unit can generate questions that focus on areas the user has struggled with in the past. For example, the generation unit can generate application questions in areas the user excels at. For example, the generation unit can generate questions that allow the user to move on to the next step according to their learning progress. This allows for more effective learning by customizing the content of the practice questions based on the user's learning history. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's learning history data into a generation AI and have the generation AI perform the customization of the practice questions.

[0042] The generation unit can apply different generation algorithms during generation according to the user's learning style. For example, if the user is a visual learner, the generation unit can generate problems that make extensive use of images and diagrams. For example, if the user is an auditory learner, the generation unit can generate problems that include audio explanations. For example, if the user is an experiential learner, the generation unit can generate problems that include practical scenarios. By applying a generation algorithm according to the user's learning style, more effective problems can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's learning style data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0043] The generation unit can determine the priority of practice problems based on the user's learning progress during generation. For example, the generation unit can prioritize generating problems in areas where the user struggles. For example, the generation unit can postpone generating application problems in areas where the user excels. For example, the generation unit can prioritize generating problems that will help the user progress to the next step, according to their learning progress. This allows for more effective learning by determining the priority of practice problems based on the user's learning progress. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's learning progress data into a generation AI and have the generation AI determine the priority of practice problems.

[0044] The generation unit can adjust the content of the practice questions by referring to the user's relevant learning materials during generation. For example, the generation unit can generate questions based on the learning materials the user has referenced. For example, the generation unit can generate questions by referring to learning materials the user has used in the past. For example, the generation unit can generate questions related to the materials the user is currently studying. This allows for the generation of more relevant questions by referring to the user's relevant learning materials. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's learning material data into a generation AI and have the generation AI adjust the content of the practice questions.

[0045] The service provider can select the optimal service method by referring to the user's past answer history of practice problems when providing the service. For example, the service provider can select a service method by referring to the format of problems the user has answered correctly in the past. For example, the service provider can select a service method that avoids the format of problems the user has answered incorrectly in the past. For example, the service provider can analyze the user's answer history and select the most effective service method. In this way, the optimal service method can be selected by referring to the user's past answer history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without using a generation AI. For example, the service provider can input the user's answer history data into a generation AI and have the generation AI select the optimal service method.

[0046] The provisioning unit can adjust the timing of the provision of practice problems based on the user's current learning status. For example, if the user is focused, the provisioning unit can provide practice problems immediately. If the user is tired, the provisioning unit can provide practice problems after a break. If the user has just started learning, the provisioning unit can provide easy problems first. By adjusting the timing of provision based on the user's learning status, more effective learning becomes possible. Some or all of the above processing in the provisioning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the provisioning unit can input the user's learning status data into a generative AI and have the generative AI adjust the timing of the provision of practice problems.

[0047] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can select a delivery method that matches the screen size. For example, if the user is using a tablet, the delivery unit can select a delivery method optimized for a larger screen. For example, if the user is using a desktop, the delivery unit can select a delivery method that includes detailed information. In this way, the optimal delivery method can be selected by taking into account the user's device information. Some or all of the above processing in the delivery unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the delivery unit can input the user's device information into a generative AI and have the generative AI select the optimal delivery method.

[0048] The service provider can adjust the format of the practice questions based on the user's learning environment at the time of delivery. For example, if the user is learning in a quiet environment, the service provider may select a format that includes audio explanations. For example, if the user is on the go, the service provider may provide the practice questions in a concise text format. For example, if the user is engaged in group learning, the service provider may select a format suitable for collaborative work. By adjusting the format based on the user's learning environment, more effective learning becomes possible. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider may input the user's learning environment data into a generative AI and have the generative AI perform the adjustment of the format of the practice questions.

[0049] The discussion unit can select the optimal method of conducting a discussion by referring to the user's past discussion history. For example, the discussion unit can analyze the content of discussions the user has participated in in the past and select the optimal method of conducting the discussion. For example, the discussion unit can select a method of conducting the discussion by prioritizing the discussion format that the user has preferred in the past. For example, the discussion unit can select an effective method of conducting the discussion from the user's past discussion history. In this way, the optimal method of conducting the discussion can be selected by referring to the user's past discussion history. Some or all of the above processing in the discussion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the discussion unit can input the user's discussion history data into a generative AI and have the generative AI perform the selection of the optimal method of conducting the discussion.

[0050] The discussion unit can select discussion topics based on the user's current learning theme during a discussion. For example, the discussion unit can select topics related to the theme the user is currently learning. For example, the discussion unit can select discussion topics based on themes the user is interested in. For example, the discussion unit can select topics to help the user move to the next step, depending on their learning progress. This allows for more relevant discussions by selecting topics based on the user's current learning theme. Some or all of the above processing in the discussion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discussion unit can input the user's learning theme data into a generative AI and have the generative AI select discussion topics.

[0051] The discussion unit can prioritize selecting highly relevant participants during a discussion by considering the user's geographical location. For example, if the user is in a specific region, the discussion unit can prioritize selecting participants related to that region. For example, if the user is traveling, the discussion unit can prioritize selecting participants related to the region they are visiting. For example, if the user is at home, the discussion unit can prioritize selecting participants who are online and accessible. This allows for the prioritization of highly relevant participants by considering the user's geographical location. Some or all of the above processing in the discussion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discussion unit can input the user's geographical location information into a generative AI and have the generative AI perform the selection of highly relevant participants.

[0052] The discussion unit can analyze the user's social media activity during a discussion and select relevant topics. For example, the discussion unit can select topics based on the user's interests shared on social media. For example, the discussion unit can analyze the content of posts from accounts the user follows and select relevant topics. For example, the discussion unit can select discussion topics based on topics in online communities the user participates in. In this way, relevant topics can be selected by analyzing the user's social media activity. Some or all of the above processing in the discussion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discussion unit can input the user's social media data into a generative AI and have the generative AI perform the selection of relevant topics.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] Online learning platforms can implement reward systems to maintain user motivation based on their learning progress. For example, a data collection unit collects user learning data, and a data generation unit analyzes this data to evaluate the user's learning progress. A data provision unit can provide rewards such as badges or points when users achieve certain learning goals. Furthermore, the data provision unit can also provide a ranking system for users to compete with other users. This allows users to increase their motivation to learn by receiving rewards according to their learning progress.

[0055] The generation unit not only analyzes the user's learning progress and generates appropriate practice problems, but can also customize the problem format according to the user's learning style. For example, it can generate problems that heavily utilize images and diagrams for visual learners, problems that include audio explanations for auditory learners, and problems that include practical scenarios for experiential learners. By providing problems tailored to the user's learning style, the learning effect can be improved.

[0056] The discussion section can select the optimal method of conducting a discussion by referring to the user's past discussion history. For example, it can analyze the content of discussions the user has participated in in the past and select the most suitable method. It can also prioritize discussion formats that the user has preferred in the past when selecting a method of conducting a discussion. Furthermore, it can select an effective method of conducting a discussion based on the user's past discussion history. In this way, the optimal method of conducting a discussion can be selected by referring to the user's past discussion history.

[0057] The data collection unit can analyze the user's past learning history and select the optimal data collection method. For example, it can prioritize data collection using learning methods that were effective for the user in the past. It can also focus on collecting data in areas where the user struggled in the past. Furthermore, it can analyze the user's learning patterns and collect data at the most efficient timing. In this way, by analyzing the user's past learning history, the optimal data collection method can be selected.

[0058] The data collection unit can filter learning data based on the user's current learning environment and areas of interest. For example, if a user is studying in a quiet environment, it can collect data to improve concentration. If a user is interested in a particular field, it can prioritize collecting data related to that field. Furthermore, if a user is engaged in group learning, it can collect data that is useful for collaborative work. In this way, by filtering data based on the user's learning environment and areas of interest, more relevant data can be collected.

[0059] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting training data. For example, if the user is in a specific region, it can prioritize the collection of data related to cultural heritage in that region. If the user is traveling, it can prioritize the collection of data related to cultural heritage in the destination. Furthermore, if the user is at home, it can prioritize the collection of data on cultural heritage that is accessible online. In this way, by considering the user's geographical location, it can prioritize the collection of highly relevant data.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection unit collects user learning data. The collection unit can collect learning data in various formats, such as text data, audio data, and image data. For example, it collects text data entered by the user, audio data, and image data uploaded by the user. Step 2: The generation unit analyzes the data collected by the collection unit and generates practice problems based on the user's learning progress. The generation unit analyzes the data using statistical analysis and machine learning algorithms to evaluate the user's learning progress. Furthermore, it is also possible to generate practice problems using generation AI. For example, practice problems can be generated using text generation AI (LLM) or multimodal generation AI. Step 3: The provider provides the practice questions generated by the generator. The provider can provide the practice questions in various forms, such as online or printed. For example, they can provide the practice questions through an online platform, print them out, or send them via email.

[0062] (Example of form 2) An online learning platform according to an embodiment of the present invention is a system that utilizes generative AI to generate practice problems tailored to the user's learning progress, enabling efficient learning of knowledge for cultural heritage preservation. When a user begins learning, the generative AI analyzes the user's learning progress and generates appropriate practice problems. Furthermore, the platform is designed to promote interactive learning among users, allowing users to share knowledge and engage in discussions with others. The generative AI also analyzes the user's learning data and evaluates their learning progress and understanding. For example, when a user begins learning, the generative AI analyzes the user's learning progress and generates appropriate practice problems. Next, the platform is designed to promote interactive learning among users, allowing users to share knowledge and engage in discussions with others. Furthermore, the generative AI analyzes the user's learning data and evaluates their learning progress and understanding. In this way, users can work on problems tailored to their learning progress and efficiently advance their learning. Thus, the online learning platform can provide practice problems tailored to the user's learning progress and support efficient learning.

[0063] The online learning platform according to this embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit collects user learning data. The collection unit can collect learning data in the form of, for example, text data, audio data, or image data. For example, the collection unit collects text data entered by the user. The collection unit can also collect user audio data. Furthermore, the collection unit can also collect image data uploaded by the user. The generation unit analyzes the data collected by the collection unit and generates practice problems based on the user's learning progress. The generation unit can analyze the data using, for example, statistical analysis or machine learning algorithms. For example, the generation unit can evaluate the user's learning progress using statistical analysis. Furthermore, the generation unit can also analyze the data using machine learning algorithms and evaluate the user's learning progress. Furthermore, the generation unit can also generate practice problems using a generation AI. For example, the generation AI generates practice problems using a text generation AI (e.g., LLM). Furthermore, the generation AI can also generate practice problems using a multimodal generation AI. The provision unit provides the practice problems generated by the generation unit. The provider can provide practice problems in various forms, such as online or printed materials. For example, the provider can provide practice problems through an online platform. The provider can also provide practice problems in printed form. Furthermore, the provider can send practice problems via email. This allows the online learning platform according to the embodiment to provide practice problems tailored to the user's learning progress, supporting efficient learning.

[0064] The data collection unit collects user learning data. This data can be collected in various formats, such as text, audio, and image data. Specifically, when collecting user-entered text data, it acquires the content entered through online forms or chatbots in real time. The data collection unit can also collect user audio data. Audio data collection involves recording using a microphone and converting it to text using speech recognition technology. Furthermore, the data collection unit can collect image data uploaded by users. Image data collection includes a function for users to upload photos they have taken or scanned documents, and this data is stored in cloud storage. The data collection unit centrally manages these diverse data formats and builds a user-specific database. Additionally, the data collection unit adjusts the frequency and timing of data collection to enable flexible data collection tailored to the user's learning progress. For example, it could set up automatic data collection after completing a specific learning module, or provide an interface that allows users to voluntarily upload data. This allows the data collection unit to closely monitor user learning activities and collect data that meets individual learning needs.

[0065] The generation unit analyzes the data collected by the collection unit and generates practice questions based on the user's learning progress. The generation unit can analyze the data using, for example, statistical analysis or machine learning algorithms. Specifically, when evaluating the user's learning progress using statistical analysis, it aggregates data such as learning time, correct answer rate, and error patterns to identify the user's strengths and weaknesses. The generation unit can also analyze the data using machine learning algorithms to evaluate the user's learning progress. Machine learning algorithms model the user's learning patterns based on past learning data and predict future learning outcomes. Furthermore, the generation unit can also generate practice questions using generative AI. The generative AI can generate practice questions using, for example, text generation AI (e.g., LLM). LLM uses natural language processing technology based on a large amount of learning data to generate question texts that are appropriate for the user's learning content. The generative AI can also generate practice questions using multimodal generative AI. Multimodal generative AI integrates and analyzes multiple data formats such as text, images, and audio to generate practice questions that include visual questions and audio instructions. This allows the generation unit to provide a variety of practice problems tailored to the user's learning progress, maximizing learning effectiveness. Furthermore, the generation unit can continuously optimize the difficulty level and content of the generated practice problems based on user feedback.

[0066] The provisioning unit provides practice problems generated by the generation unit. The provisioning unit can provide practice problems in various forms, such as online or printed materials. Specifically, when providing practice problems through an online platform, it designs an interface that is easy for users to access and displays the practice problems organized by category. The provisioning unit can also provide practice problems in printed form. For printed materials, the practice problems are made available for download in PDF format so that users can study at home and print them. Furthermore, the provisioning unit can also send practice problems via email. For email delivery, practice problems are sent periodically to the user's registered email address to support their learning progress. By combining these delivery methods, the provisioning unit can provide flexible learning support tailored to the user's learning style and environment. For example, by using both online platform delivery and email delivery, it can create an environment where users can study anytime, anywhere. In addition, the provisioning unit can adjust the frequency and content of practice problems according to the user's learning progress to meet individual learning needs. This allows the provisioning unit to provide users with optimal learning resources and support efficient learning.

[0067] The discussion unit can promote interactive learning among users. The discussion unit can promote interactive learning through methods such as chat or video conferencing. The discussion unit can facilitate dialogue among users through chat, for example. The discussion unit can also facilitate dialogue among users through video conferencing. Furthermore, the discussion unit can also facilitate dialogue among users through forums. This allows users to share knowledge and engage in discussions. Some or all of the above-described processes in the discussion unit may be performed using, for example, generative AI, or without generative AI. For example, the discussion unit can input user dialogue data into a generative AI and have the generative AI manage the flow of the dialogue.

[0068] The generation unit can analyze the user's learning progress and generate appropriate practice questions. For example, the generation unit can analyze the user's learning progress and generate appropriate practice questions. For example, the generation unit can analyze the user's learning progress and generate appropriate practice questions. For example, the generation unit can analyze the user's learning progress and generate appropriate practice questions. This improves learning effectiveness by generating appropriate practice questions based on the user's learning progress. 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 the user's learning data into a generation AI and have the generation AI generate practice questions.

[0069] The provisioning unit can provide the user with the generated practice problems. The provisioning unit can, for example, provide the user with the generated practice problems. The provisioning unit can, for example, provide the user with the generated practice problems. The provisioning unit can, for example, provide the user with the generated practice problems. This supports the user's learning progress by providing them with the generated practice problems. Some or all of the above processing in the provisioning unit may be performed using a generational AI, for example, or without a generational AI. For example, the provisioning unit can input the generated practice problems into a generational AI and have the generational AI perform the task of providing the practice problems.

[0070] The discussion unit allows users to share knowledge and engage in discussions. The discussion unit allows users to share knowledge and engage in discussions, for example. The discussion unit allows users to share knowledge and engage in discussions, for example. The discussion unit allows users to share knowledge and engage in discussions, for example. This improves learning effectiveness by allowing users to share knowledge and engage in discussions. Some or all of the above-described processes in the discussion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discussion unit can input user dialogue data into a generative AI and have the generative AI manage the flow of the dialogue.

[0071] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect data when the user is relaxed. For example, if the user is focused, the data collection unit can collect data immediately to avoid interrupting the learning process. For example, if the user is tired, the data collection unit can adjust the timing to collect data after a break. This allows for more appropriate data collection by adjusting the timing of data collection 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0072] The data collection unit can analyze the user's past learning history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data using learning methods that were effective for the user in the past. For example, the data collection unit can focus on collecting data in areas where the user struggled in the past. For example, the data collection unit can analyze the user's learning patterns and collect data at the most efficient timing. This allows the optimal data collection method to be selected by analyzing the user's past learning history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's learning history data into a generative AI and have the generative AI select the optimal data collection method.

[0073] The data collection unit can filter learning data based on the user's current learning environment and areas of interest. For example, if the user is learning in a quiet environment, the data collection unit can collect data to enhance concentration. For example, if the user is interested in a particular field, the data collection unit can prioritize collecting data related to that field. For example, if the user is learning in a group, the data collection unit can collect data useful for collaborative work. By filtering the data based on the user's learning environment and areas of interest, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input the user's learning environment data into a generative AI and have the generative AI perform the filtering.

[0074] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is excited, the data collection unit may prioritize collecting data that is of interest. For example, if the user is relaxed, the data collection unit may prioritize collecting data that promotes deeper understanding. For example, if the user is tired, the data collection unit may prioritize collecting data that is simple and easy to understand. This allows for more effective data collection by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0075] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting training data. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to cultural heritage in that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to cultural heritage in the destination. For example, if the user is at home, the data collection unit can prioritize the collection of data related to cultural heritage that is accessible online. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI and have the generative AI perform the collection of highly relevant data.

[0076] The data collection unit can analyze the user's social media activity and collect relevant data when collecting training data. For example, the data collection unit can collect data based on the user's interests shared on social media. For example, the data collection unit can analyze the content of posts from accounts the user follows and collect relevant data. For example, the data collection unit can collect data based on the topics of online communities the user participates in. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media data into a generative AI and have the generative AI perform the collection of relevant data.

[0077] The generation unit can estimate the user's emotions and adjust the difficulty level of the practice questions based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate slightly difficult questions. For example, if the user is stressed, the generation unit can generate easy questions. For example, if the user is focused, the generation unit can generate questions of moderate difficulty. By adjusting the difficulty level of the practice questions based on the user's emotions, more appropriate questions can be provided. 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 processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the difficulty level of the practice questions.

[0078] The generation unit can customize the content of the practice questions based on the user's learning history during the generation process. For example, the generation unit can generate questions that focus on areas the user has struggled with in the past. For example, the generation unit can generate application questions in areas the user excels at. For example, the generation unit can generate questions that allow the user to move on to the next step according to their learning progress. This allows for more effective learning by customizing the content of the practice questions based on the user's learning history. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's learning history data into a generation AI and have the generation AI perform the customization of the practice questions.

[0079] The generation unit can apply different generation algorithms during generation according to the user's learning style. For example, if the user is a visual learner, the generation unit can generate problems that make extensive use of images and diagrams. For example, if the user is an auditory learner, the generation unit can generate problems that include audio explanations. For example, if the user is an experiential learner, the generation unit can generate problems that include practical scenarios. By applying a generation algorithm according to the user's learning style, more effective problems can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's learning style data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0080] The generation unit can estimate the user's emotions and adjust the format of the practice questions based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate questions with detailed explanations. For example, if the user is in a hurry, the generation unit can generate questions in a concise format. For example, if the user is excited, the generation unit can generate questions in a visually stimulating format. This allows for the provision of more appropriate questions by adjusting the format of the practice questions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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 a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI and have the generative AI adjust the format of the practice questions.

[0081] The generation unit can determine the priority of practice problems based on the user's learning progress during generation. For example, the generation unit can prioritize generating problems in areas where the user struggles. For example, the generation unit can postpone generating application problems in areas where the user excels. For example, the generation unit can prioritize generating problems that will help the user progress to the next step, according to their learning progress. This allows for more effective learning by determining the priority of practice problems based on the user's learning progress. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's learning progress data into a generation AI and have the generation AI determine the priority of practice problems.

[0082] The generation unit can adjust the content of the practice questions by referring to the user's relevant learning materials during generation. For example, the generation unit can generate questions based on the learning materials the user has referenced. For example, the generation unit can generate questions by referring to learning materials the user has used in the past. For example, the generation unit can generate questions related to the materials the user is currently studying. This allows for the generation of more relevant questions by referring to the user's relevant learning materials. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's learning material data into a generation AI and have the generation AI adjust the content of the practice questions.

[0083] The service provider can estimate the user's emotions and adjust the way the practice questions are presented based on the estimated emotions. For example, if the user is relaxed, the service provider may select a presentation method that includes detailed explanations. For example, if the user is in a hurry, the service provider may present the practice questions in a concise format. For example, if the user is excited, the service provider may present the practice questions in a visually stimulating format. This allows the service provider to present the questions in a more appropriate way by adjusting the presentation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 service provider may be performed using a generative AI, or not. For example, the service provider may input user emotion data into a generative AI and have the generative AI adjust the presentation method of the practice questions.

[0084] The service provider can select the optimal service method by referring to the user's past answer history of practice problems when providing the service. For example, the service provider can select a service method by referring to the format of problems the user has answered correctly in the past. For example, the service provider can select a service method that avoids the format of problems the user has answered incorrectly in the past. For example, the service provider can analyze the user's answer history and select the most effective service method. In this way, the optimal service method can be selected by referring to the user's past answer history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without using a generation AI. For example, the service provider can input the user's answer history data into a generation AI and have the generation AI select the optimal service method.

[0085] The provisioning unit can adjust the timing of the provision of practice problems based on the user's current learning status. For example, if the user is focused, the provisioning unit can provide practice problems immediately. If the user is tired, the provisioning unit can provide practice problems after a break. If the user has just started learning, the provisioning unit can provide easy problems first. By adjusting the timing of provision based on the user's learning status, more effective learning becomes possible. Some or all of the above processing in the provisioning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the provisioning unit can input the user's learning status data into a generative AI and have the generative AI adjust the timing of the provision of practice problems.

[0086] The service provider can estimate the user's emotions and determine the order in which the practice problems are presented based on the estimated emotions. For example, if the user is relaxed, the service provider may present more difficult problems first. If the user is stressed, the service provider may present easier problems first. If the user is focused, the service provider may present problems of appropriate difficulty in a logical order. This allows for more effective learning by determining the order of presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the order in which the practice problems are presented.

[0087] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can select a delivery method that matches the screen size. For example, if the user is using a tablet, the delivery unit can select a delivery method optimized for a larger screen. For example, if the user is using a desktop, the delivery unit can select a delivery method that includes detailed information. In this way, the optimal delivery method can be selected by taking into account the user's device information. Some or all of the above processing in the delivery unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the delivery unit can input the user's device information into a generative AI and have the generative AI select the optimal delivery method.

[0088] The service provider can adjust the format of the practice questions based on the user's learning environment at the time of delivery. For example, if the user is learning in a quiet environment, the service provider may select a format that includes audio explanations. For example, if the user is on the go, the service provider may provide the practice questions in a concise text format. For example, if the user is engaged in group learning, the service provider may select a format suitable for collaborative work. By adjusting the format based on the user's learning environment, more effective learning becomes possible. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider may input the user's learning environment data into a generative AI and have the generative AI perform the adjustment of the format of the practice questions.

[0089] The discussion unit can estimate the user's emotions and adjust the discussion process based on those emotions. For example, if the user is nervous, the discussion unit can select a method that helps them relax. If the user is relaxed, the discussion unit can select a method that encourages active discussion. If the user is excited, the discussion unit can select a method that allows for calm discussion. By adjusting the discussion process based on the user's emotions, more effective discussions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, 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 discussion unit may be performed using a generative AI, or not. For example, the discussion unit can input user emotion data into a generative AI and have the generative AI adjust the discussion process.

[0090] The discussion unit can select the optimal method of conducting a discussion by referring to the user's past discussion history. For example, the discussion unit can analyze the content of discussions the user has participated in in the past and select the optimal method of conducting the discussion. For example, the discussion unit can select a method of conducting the discussion by prioritizing the discussion format that the user has preferred in the past. For example, the discussion unit can select an effective method of conducting the discussion from the user's past discussion history. In this way, the optimal method of conducting the discussion can be selected by referring to the user's past discussion history. Some or all of the above processing in the discussion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the discussion unit can input the user's discussion history data into a generative AI and have the generative AI perform the selection of the optimal method of conducting the discussion.

[0091] The discussion unit can select discussion topics based on the user's current learning theme during a discussion. For example, the discussion unit can select topics related to the theme the user is currently learning. For example, the discussion unit can select discussion topics based on themes the user is interested in. For example, the discussion unit can select topics to help the user move to the next step, depending on their learning progress. This allows for more relevant discussions by selecting topics based on the user's current learning theme. Some or all of the above processing in the discussion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discussion unit can input the user's learning theme data into a generative AI and have the generative AI select discussion topics.

[0092] The discussion unit can estimate the user's emotions and select discussion participants based on the estimated emotions. For example, if the user is relaxed, the discussion unit will select participants who are also relaxed. For example, if the user is tense, the discussion unit can select relaxed participants to alleviate the tension. For example, if the user is excited, the discussion unit can select calm participants to maintain balance. This allows for more effective discussions by selecting participants based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the discussion unit may be performed using a generative AI, or not. For example, the discussion unit can input user emotion data into a generative AI and have the generative AI select discussion participants.

[0093] The discussion unit can prioritize selecting highly relevant participants during a discussion by considering the user's geographical location. For example, if the user is in a specific region, the discussion unit can prioritize selecting participants related to that region. For example, if the user is traveling, the discussion unit can prioritize selecting participants related to the region they are visiting. For example, if the user is at home, the discussion unit can prioritize selecting participants who are online and accessible. This allows for the prioritization of highly relevant participants by considering the user's geographical location. Some or all of the above processing in the discussion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discussion unit can input the user's geographical location information into a generative AI and have the generative AI perform the selection of highly relevant participants.

[0094] The discussion unit can analyze the user's social media activity during a discussion and select relevant topics. For example, the discussion unit can select topics based on the user's interests shared on social media. For example, the discussion unit can analyze the content of posts from accounts the user follows and select relevant topics. For example, the discussion unit can select discussion topics based on topics in online communities the user participates in. In this way, relevant topics can be selected by analyzing the user's social media activity. Some or all of the above processing in the discussion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discussion unit can input the user's social media data into a generative AI and have the generative AI perform the selection of relevant topics.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] Online learning platforms can implement reward systems to maintain user motivation based on their learning progress. For example, a data collection unit collects user learning data, and a data generation unit analyzes this data to evaluate the user's learning progress. A data provision unit can provide rewards such as badges or points when users achieve certain learning goals. Furthermore, the data provision unit can also provide a ranking system for users to compete with other users. This allows users to increase their motivation to learn by receiving rewards according to their learning progress.

[0097] The discussion unit can estimate the user's emotions and adjust the discussion topic based on those emotions. For example, if the user is excited, the discussion unit can select a more stimulating and interesting topic. If the user is relaxed, the discussion unit can select a topic that allows for deeper discussion. Furthermore, if the user is stressed, the discussion unit can select a lighter topic that helps them relax. By adjusting the discussion topic according to the user's emotions, more effective interactive learning becomes possible.

[0098] The generation unit not only analyzes the user's learning progress and generates appropriate practice problems, but can also customize the problem format according to the user's learning style. For example, it can generate problems that heavily utilize images and diagrams for visual learners, problems that include audio explanations for auditory learners, and problems that include practical scenarios for experiential learners. By providing problems tailored to the user's learning style, the learning effect can be improved.

[0099] The system can estimate the user's emotions when providing generated practice questions and adjust the delivery method based on those emotions. For example, if the user is relaxed, it can select a delivery method that includes detailed explanations. If the user is in a hurry, it can provide the practice questions in a concise format. Furthermore, if the user is excited, it can provide the practice questions in a visually stimulating format. By adjusting the delivery method based on the user's emotions, more effective learning becomes possible.

[0100] The discussion section can select the optimal method of conducting a discussion by referring to the user's past discussion history. For example, it can analyze the content of discussions the user has participated in in the past and select the most suitable method. It can also prioritize discussion formats that the user has preferred in the past when selecting a method of conducting a discussion. Furthermore, it can select an effective method of conducting a discussion based on the user's past discussion history. In this way, the optimal method of conducting a discussion can be selected by referring to the user's past discussion history.

[0101] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those estimates. For example, if the user is stressed, the data collection timing can be delayed to allow the user to relax. If the user is focused, data can be collected immediately to avoid interrupting the learning process. Furthermore, if the user is tired, the timing can be adjusted to collect data after a break. By adjusting the timing of data collection according to the user's emotions, more appropriate data collection becomes possible.

[0102] The data collection unit can analyze the user's past learning history and select the optimal data collection method. For example, it can prioritize data collection using learning methods that were effective for the user in the past. It can also focus on collecting data in areas where the user struggled in the past. Furthermore, it can analyze the user's learning patterns and collect data at the most efficient timing. In this way, by analyzing the user's past learning history, the optimal data collection method can be selected.

[0103] The data collection unit can filter learning data based on the user's current learning environment and areas of interest. For example, if a user is studying in a quiet environment, it can collect data to improve concentration. If a user is interested in a particular field, it can prioritize collecting data related to that field. Furthermore, if a user is engaged in group learning, it can collect data that is useful for collaborative work. In this way, by filtering data based on the user's learning environment and areas of interest, more relevant data can be collected.

[0104] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is excited, it can prioritize collecting data that will pique their interest. If the user is relaxed, it can prioritize collecting data that promotes deeper understanding. Furthermore, if the user is tired, it can prioritize collecting data that is simple and easy to understand. By prioritizing data based on the user's emotions, more effective data collection becomes possible.

[0105] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting training data. For example, if the user is in a specific region, it can prioritize the collection of data related to cultural heritage in that region. If the user is traveling, it can prioritize the collection of data related to cultural heritage in the destination. Furthermore, if the user is at home, it can prioritize the collection of data on cultural heritage that is accessible online. In this way, by considering the user's geographical location, it can prioritize the collection of highly relevant data.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The collection unit collects user learning data. The collection unit can collect learning data in various formats, such as text data, audio data, and image data. For example, it collects text data entered by the user, audio data, and image data uploaded by the user. Step 2: The generation unit analyzes the data collected by the collection unit and generates practice problems based on the user's learning progress. The generation unit analyzes the data using statistical analysis and machine learning algorithms to evaluate the user's learning progress. Furthermore, it is also possible to generate practice problems using generation AI. For example, practice problems can be generated using text generation AI (LLM) or multimodal generation AI. Step 3: The provider provides the practice questions generated by the generator. The provider can provide the practice questions in various forms, such as online or printed. For example, they can provide the practice questions through an online platform, print them out, or send them via email.

[0108] 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.

[0109] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0110] 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.

[0111] Each of the multiple elements described above, including the collection unit, generation unit, provision unit, and discussion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user learning data using the camera 42 and microphone 38B of the smart device 14. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and generates practice problems. The provision unit is implemented in the control unit 46A of the smart device 14, which provides the generated practice problems to the user. The discussion unit facilitates interaction between users using the communication I / F 44 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.).

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] 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.

[0127] Each of the multiple elements described above, including the collection unit, generation unit, provision unit, and discussion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user learning data using the camera 42 and microphone 238 of the smart glasses 214. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and generates practice problems. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides the generated practice problems to the user. The discussion unit facilitates interaction between users using, for example, the communication I / F 44 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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.

[0143] Each of the multiple elements described above, including the collection unit, generation unit, provision unit, and discussion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user learning data using the camera 42 and microphone 238 of the headset terminal 314. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and generates practice problems. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, which provides the generated practice problems to the user. The discussion unit facilitates interaction between users using, for example, the communication I / F 44 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, generation unit, provision unit, and discussion unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user learning data using the camera 42 and microphone 238 of the robot 414. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and generates practice problems. The provision unit is implemented, for example, by the control unit 46A of the robot 414, which provides the generated practice problems to the user. The discussion unit facilitates interaction between users, for example, using the communication I / F 44 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] (Note 1) A data collection unit that collects user learning data, A generation unit analyzes the data collected by the collection unit and generates practice problems based on the user's learning progress, The system includes a providing unit that provides the simulated problems generated by the generation unit. A system characterized by the following features. (Note 2) It features a discussion section that promotes interactive learning among users. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Analyze the user's learning progress and generate appropriate practice questions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provides users with generated practice problems. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned discussion section is, Users can share knowledge and engage in discussions with each other. The system described in Appendix 2, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of training data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past learning history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting training data, filtering is performed based on the user's current learning environment and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting training data, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting training data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is The system estimates the user's emotions and adjusts the difficulty level of the practice questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, the content of the practice questions is customized based on the user's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, different generation algorithms are applied depending on the user's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is The system estimates the user's emotions and adjusts the format of the simulated questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, the priority of practice questions is determined based on the user's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the content of the practice questions is adjusted by referring to the user's relevant learning materials. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how the simulated questions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the content, the system will refer to the user's past answer history for practice problems to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the content, the timing of providing the practice questions will be adjusted based on the user's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which the simulated problems are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the materials, the format of the practice questions will be adjusted based on the user's learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned discussion section is, It estimates the user's emotions and adjusts the discussion's progression based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned discussion section is, During discussions, the system selects the optimal approach by referring to the user's past discussion history. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned discussion section is, During discussions, the discussion topic is selected based on the user's current learning theme. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned discussion section is, The system estimates the user's emotions and selects discussion participants based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned discussion section is, During discussions, the system prioritizes selecting highly relevant participants by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned discussion section is, During discussions, we analyze users' social media activity and select relevant topics. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0180] 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 data collection unit that collects user learning data, A generation unit analyzes the data collected by the collection unit and generates practice problems based on the user's learning progress, The system includes a providing unit that provides the simulated problems generated by the generation unit. A system characterized by the following features.

2. It features a discussion section that promotes interactive learning among users. The system according to feature 1.

3. The generating unit is Analyze the user's learning progress and generate appropriate practice questions. The system according to feature 1.

4. The aforementioned supply unit is, Provides users with generated practice problems. The system according to feature 1.

5. The aforementioned discussion section is, Users can share knowledge and engage in discussions with each other. The system according to feature 2.

6. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of training data collection based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past learning history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting training data, filtering is performed based on the user's current learning environment and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting training data, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system according to feature 1.

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