system

Abstract

The system according to the embodiment aims to generate questions that meet the individual needs of learners and manage their progress. [Solution] A system according to an embodiment includes a receiving unit, a generating unit, a providing unit, an analyzing unit, and a managing unit. The receiving unit receives input of the field or level that a learner wishes to study. The generating unit generates questions based on the information received by the receiving unit. The providing unit provides the questions generated by the generating unit to the learner. The analyzing unit analyzes the learner's answers to the questions provided by the providing unit and provides feedback. The managing unit accumulates the answer data analyzed by the analyzing unit and manages the progress of learning.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently generate questions that meet the individual needs of learners and to manage their progress.

[0005] The system according to the embodiment aims to generate questions that meet the individual needs of learners and manage their progress. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, a providing unit, an analyzing unit, and a managing unit. The receiving unit receives input of the field or level that the learner wishes to study. The generating unit generates questions based on the information received by the receiving unit. The providing unit provides the questions generated by the generating unit to the learner. The analyzing unit analyzes the learner's answers to the questions provided by the providing unit and provides feedback. The managing unit accumulates the answer data analyzed by the analyzing unit and manages the progress of the learner's learning. [Effects of the Invention]

[0007] The system according to the embodiment can generate questions that meet the individual needs of the learner and manage the progress. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The learning support system according to an embodiment of the present invention uses a generation AI to generate questions from a wide range of subjects. This learning support system allows learners to select and answer questions tailored to their learning needs. Specifically, it consists of the following steps: First, the learner inputs the subject and level they wish to study. Next, the generation AI analyzes the input information and generates appropriate questions. The generated questions are provided to the learner, who answers them. Once the answer is complete, the generation AI analyzes the answers and provides feedback. Furthermore, the generation AI accumulates the learner's answer data and manages their learning progress. This allows learners to progress at their own pace and constantly challenge themselves with new questions. The system also takes learners' privacy into consideration. For example, when learners input the subject and level they wish to study, the system anonymizes their personal information to protect their privacy. Furthermore, the generation AI securely stores the learner's answer data and implements security measures to prevent it from being leaked to third parties. This allows learners to proceed with their studies with peace of mind. The learning support system allows learners to progress at their own pace and constantly challenge themselves with new questions.

[0029] A learning support system according to an embodiment includes a receiving unit, a generating unit, a providing unit, an analyzing unit, and a managing unit. The receiving unit receives input of a field or level that a learner wishes to study. Examples of the field or level that a learner wishes to study include, but are not limited to, mathematics, science, beginner, intermediate, and advanced. The receiving unit, for example, analyzes the information input by the learner and transmits it to the generating unit in an appropriate format. The generating unit generates questions based on the information received by the receiving unit using a generation AI. The generating unit, for example, automatically creates questions according to the learner's level and learning content. The generating unit can also adjust the difficulty and format of the questions using the generation AI. For example, if a learner is feeling stressed, the generating unit may prioritize generating easy questions. Alternatively, if a learner is relaxed, the generating unit may generate more difficult questions. The providing unit provides the questions generated by the generating unit to the learner. The providing unit can provide the questions through an online platform, for example. Alternatively, the providing unit can provide the questions through printed materials or an application. The providing unit can also select the optimal presentation format taking into account the learner's device information. For example, if the learner is using a smartphone, the providing unit selects a presentation format that matches the screen size. The analysis unit analyzes the learner's answers to the questions provided by the providing unit and provides feedback. For example, the analysis unit analyzes the learner's answers and determines whether they are correct or incorrect. The analysis unit can also provide explanations for the answers and additional learning materials. The management unit accumulates the answer data analyzed by the analysis unit and manages the learner's progress. For example, the management unit accumulates the learner's answer data and evaluates the learner's progress. If the learner's understanding in a particular area is lacking, the management unit can also focus on providing questions in that area. This allows the learning support system according to the embodiment to allow learners to progress through their studies at their own pace and constantly challenge themselves with new questions.

[0030] The generation unit can automatically create questions according to the learner's level or learning content. For example, the generation unit automatically creates questions according to the learner's level or learning content. For example, if a learner wants to learn beginner-level mathematics, the generation unit can generate basic calculation questions. If a learner wants to learn intermediate-level science, the generation unit can also generate questions about chemical reactions. Furthermore, if a learner wants to learn advanced-level history, the generation unit can generate questions about historical events. This allows the generation unit to provide optimal questions for the learner. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs prompts for generating questions to the generation AI based on the learner's level or learning content, and the generation AI generates the questions. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0031] The providing unit can provide the generated questions to the learner. The providing unit can provide the questions through, for example, an online platform. For example, the providing unit allows the learner to access the generated questions by logging in. The providing unit can also provide the questions through printed materials or an application. For example, the providing unit allows the learner to access the generated questions by receiving printed materials. The providing unit can also select the optimal presentation format taking into account the learner's device information. For example, if the learner is using a smartphone, the providing unit can select a presentation format that matches the screen size. Also, if the learner is using a tablet, the providing unit can select a presentation format optimized for a large screen. In this way, the providing unit allows the learner to access the generated questions. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs prompts for providing the generated questions to the generation AI, and the generation AI selects the optimal presentation format. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0032] The analysis unit can analyze the learner's answer and determine whether it is correct. For example, the analysis unit analyzes the learner's answer and determines whether it is correct or incorrect. For example, the analysis unit determines whether the answer to a math problem answered by the learner is correct. The analysis unit can also determine whether the answer to a science problem answered by the learner is correct. Furthermore, the analysis unit can determine whether the answer to a history problem answered by the learner is correct. This allows the analysis unit to evaluate the accuracy of the learner's answer. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs a prompt to the generation AI to analyze the learner's answer, and the generation AI analyzes the answer. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0033] The analysis unit can provide an explanation for the answer or additional learning materials. The analysis unit, for example, provides an explanation for the learner's answer. For example, the analysis unit can provide an explanation for the math problem answered by the learner. The analysis unit can also provide an explanation for the science problem answered by the learner. The analysis unit can also provide an explanation for the history problem answered by the learner. The analysis unit can also provide additional learning materials. For example, the analysis unit provides learning materials related to the problem answered by the learner. This allows the learner to deepen their understanding of the answer. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs prompts to the generation AI to provide an explanation for the answer or additional learning materials, and the generation AI generates the explanation or learning materials. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0034] The management unit can accumulate the learner's answer data and manage the learner's learning progress. For example, the management unit accumulates the learner's answer data and manages the learner's learning progress. For example, the management unit saves data on questions answered by the learner in a database. The management unit can also set criteria for evaluating the learner's progress. Furthermore, if the learner's understanding in a particular area is lacking, the management unit can focus on questions in that area. This allows the management unit to effectively manage the learner's learning progress. Some or all of the above-mentioned processing in the management unit is performed using a generation AI. For example, the management unit accumulates the learner's answer data and inputs prompts for progress management to the generation AI, which then accumulates the data and manages the progress. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0035] If a learner lacks understanding in a particular field, the management unit can prioritize questions in that field. For example, the management unit analyzes the learner's answer data, and if a learner lacks understanding in a particular field, the management unit prioritizes questions in that field. For example, if a learner lacks understanding in mathematics, the management unit can prioritize questions in mathematics. Furthermore, if a learner lacks understanding in science, the management unit can prioritize questions in science. Furthermore, if a learner lacks understanding in history, the management unit can prioritize questions in history. This allows the management unit to strengthen the learner's weak points. Some or all of the above-mentioned processing in the management unit is performed using a generation AI. For example, if a learner lacks understanding in a particular field, the management unit inputs prompts to the generation AI to prioritize questions in that field, and the generation AI generates the questions. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0036] The reception unit can analyze the learner's past learning history and suggest the optimal input method. The reception unit, for example, analyzes the learner's past learning history and suggests the optimal input method. For example, the reception unit automatically displays as candidates learning fields and levels that the learner has frequently selected in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the learner has used in the past. Furthermore, the reception unit can predict and suggest the learning field and level to be used in a specific time period based on the learner's past learning history. This allows the reception unit to provide the optimal input method based on the learner's past learning history. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs a prompt to the generation AI to analyze the learner's past learning history, and the generation AI analyzes the history. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0037] The reception unit can customize the input fields based on the learner's current learning situation and areas of interest. The reception unit customizes the input fields based on, for example, the learner's current learning situation and areas of interest. For example, the reception unit preferentially displays input fields related to the learning area the learner is currently working on. The reception unit can also automatically suggest related learning areas and levels based on the learner's areas of interest. Furthermore, the reception unit can customize appropriate input fields according to the learner's current learning progress. In this way, the reception unit can provide optimal input fields according to the learner's current learning situation and areas of interest. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs prompts for analyzing the learner's current learning situation and areas of interest to the generation AI, and the generation AI analyzes the situation and areas of interest. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0038] The reception unit can prioritize input of highly relevant learning fields taking into account the learner's geographical location information. For example, the reception unit prioritizes input of highly relevant learning fields taking into account the learner's geographical location information. For example, if the learner is in a specific region, the reception unit can prioritize suggesting learning fields related to that region. The reception unit can also automatically display learning fields specific to the region based on the learner's geographical location information. Furthermore, if the learner is traveling, the reception unit can also suggest related learning fields based on the learner's current location. This allows the reception unit to provide the optimal learning field based on the learner's geographical location information. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit acquires the learner's geographical location information and inputs a prompt to the generation AI to suggest related learning fields, and the generation AI analyzes the information. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0039] The reception unit can analyze the learner's social media activity and suggest related learning fields. For example, the reception unit can analyze the learner's social media activity and suggest related learning fields. For example, the reception unit can suggest related learning fields based on topics in which the learner has shown interest on social media. The reception unit can also analyze the learner's social media activity and prioritize displaying fields of interest. Furthermore, the reception unit can suggest related learning fields based on experts or groups the learner follows on social media. This allows the reception unit to provide the optimal learning field based on the learner's social media activity. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit inputs a prompt to analyze the learner's social media activity to the generation AI, and the generation AI analyzes the activity. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0040] The generation unit can apply an optimal question generation algorithm by referring to the learner's past answer data. The generation unit can apply an optimal question generation algorithm by referring to the learner's past answer data, for example. For example, the generation unit can analyze patterns of questions that the learner has answered correctly in the past and generate similar questions. The generation unit can also analyze patterns of questions that the learner has answered incorrectly in the past and generate review questions. Furthermore, the generation unit can generate questions according to the learner's learning progress based on the learner's past answer data. This allows the generation unit to provide optimal questions based on the learner's past answer data. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can refer to the learner's past answer data and input prompts to the generation AI to apply the optimal question generation algorithm, and the generation AI analyzes the data. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0041] The generation unit can customize the frequency and content of questions according to the learner's learning progress. The generation unit customizes the frequency and content of questions according to the learner's learning progress, for example. For example, if a learner is lagging behind in a particular area, the generation unit can frequently present questions in that area. Also, if a learner is making rapid progress in a particular area, the generation unit can present more difficult questions. Furthermore, the generation unit can adjust the frequency and content of questions according to the learner's learning progress. This allows the generation unit to provide optimal questions according to the learner's learning progress. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit evaluates the learner's learning progress and inputs prompts to the generation AI to customize the frequency and content of questions, and the generation AI analyzes the data. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0042] The generation unit can generate region-specific questions taking into account the learner's geographic location information. The generation unit generates region-specific questions taking into account the learner's geographic location information, for example. For example, if the learner is in a specific region, the generation unit generates questions related to that region. The generation unit can also automatically generate region-specific questions based on the learner's geographic location information. Furthermore, if the learner is traveling, the generation unit can generate relevant questions based on the learner's current location. This allows the generation unit to provide optimal questions based on the learner's geographic location information. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit obtains the learner's geographic location information and inputs prompts for generating region-specific questions to the generation AI, which then analyzes the information. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0043] The generator can analyze the learner's social media activity and generate relevant questions. For example, the generator can analyze the learner's social media activity and generate relevant questions. For example, the generator can generate relevant questions based on topics the learner has shown interest in on social media. The generator can also analyze the learner's social media activity and generate questions related to fields of interest. Furthermore, the generator can generate relevant questions based on experts or groups the learner follows on social media. This allows the generator to provide optimal questions based on the learner's social media activity. Some or all of the above-described processing in the generator can be performed using a generation AI. For example, the generator inputs prompts for analyzing the learner's social media activity to the generation AI, which then analyzes the activity. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0044] The providing unit can select the optimal timing for providing questions by referring to the learner's past answer history. The providing unit selects the optimal timing for providing questions by referring to, for example, the learner's past answer history. For example, the providing unit analyzes the time periods in which the learner previously answered questions and provides questions at those time periods. The providing unit can also predict the optimal timing for providing questions based on the learner's past answer history and provide the questions. Furthermore, if the learner tends to concentrate on studying during a specific time period, the providing unit can provide questions during that time period. This allows the providing unit to provide the optimal timing for providing questions based on the learner's past answer history. Some or all of the above-described processing in the providing unit is performed using a generation AI. For example, the providing unit refers to the learner's past answer history and inputs prompts for selecting the optimal timing for providing questions to the generation AI, which then analyzes the data. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0045] The providing unit can select the optimal delivery format by taking into account the learner's device information. For example, the providing unit selects the optimal delivery format by taking into account the learner's device information. For example, if the learner is using a smartphone, the providing unit selects a delivery format that matches the screen size. Also, if the learner is using a tablet, the providing unit can select a delivery format optimized for a large screen. Furthermore, if the learner is using a personal computer, the providing unit can select a delivery format that includes detailed information. This allows the providing unit to provide the optimal delivery format based on the learner's device information. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit acquires the learner's device information and inputs a prompt to the generation AI to select the optimal delivery format, and the generation AI analyzes the information. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0046] The providing unit can prioritize providing highly relevant questions by taking into account the learner's geographical location information. For example, the providing unit prioritizes providing highly relevant questions by taking into account the learner's geographical location information. For example, if the learner is in a specific region, the providing unit prioritizes providing questions related to that region. The providing unit can also automatically provide region-specific questions based on the learner's geographical location information. Furthermore, if the learner is traveling, the providing unit can provide relevant questions based on the learner's current location. This allows the providing unit to provide optimal questions based on the learner's geographical location information. Some or all of the above-described processing in the providing unit is performed using a generation AI. For example, the providing unit acquires the learner's geographical location information and inputs prompts to the generation AI to provide relevant questions, and the generation AI analyzes the information. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0047] The providing unit can analyze the learner's social media activity and provide relevant questions. For example, the providing unit can analyze the learner's social media activity and provide relevant questions. For example, the providing unit can provide relevant questions based on topics the learner has shown interest in on social media. The providing unit can also analyze the learner's social media activity and provide questions related to fields of interest. Furthermore, the providing unit can provide relevant questions based on experts or groups the learner follows on social media. This allows the providing unit to provide optimal questions based on the learner's social media activity. Some or all of the above-described processing in the providing unit is performed using a generation AI. For example, the providing unit inputs prompts for analyzing the learner's social media activity to the generation AI, and the generation AI analyzes the activity. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0048] The analysis unit can apply an optimal analysis algorithm by referring to the learner's past answer data. The analysis unit can apply an optimal analysis algorithm by referring to, for example, the learner's past answer data. For example, the analysis unit can analyze patterns of questions that the learner has answered correctly in the past and analyze answers to similar questions. The analysis unit can also analyze patterns of questions that the learner has answered incorrectly in the past and analyze answers for review. Furthermore, the analysis unit can perform answer analysis according to the learner's learning progress based on the learner's past answer data. This allows the analysis unit to provide an optimal analysis algorithm based on the learner's past answer data. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can refer to the learner's past answer data and input prompts to the generation AI to apply the optimal analysis algorithm, and the generation AI analyzes the data. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0049] The analysis unit can customize the content and format of the feedback according to the learner's learning progress. The analysis unit customizes the content and format of the feedback according to, for example, the learner's learning progress. For example, if the learner is lagging behind in a particular area, the analysis unit can provide detailed feedback on that area. In addition, if the learner is making rapid progress in a particular area, the analysis unit can provide concise feedback. Furthermore, the analysis unit can adjust the content and format of the feedback according to the learner's learning progress. This allows the analysis unit to provide optimal feedback according to the learner's learning progress. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit evaluates the learner's learning progress and inputs prompts to the generation AI to customize the content and format of the feedback, and the generation AI analyzes the data. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0050] The analysis unit can provide region-specific feedback by taking into account the learner's geographic location information. The analysis unit can provide region-specific feedback by taking into account the learner's geographic location information, for example. For example, if the learner is in a specific region, the analysis unit can provide feedback related to that region. The analysis unit can also automatically provide region-specific feedback based on the learner's geographic location information. Furthermore, if the learner is traveling, the analysis unit can provide relevant feedback based on the learner's current location. This allows the analysis unit to provide optimal feedback based on the learner's geographic location information. Some or all of the above-described processing in the analysis unit is performed using a generation AI. For example, the analysis unit obtains the learner's geographic location information and inputs prompts for providing region-specific feedback to the generation AI, which then analyzes the information. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0051] The analysis unit can analyze the learner's social media activity and provide relevant feedback. For example, the analysis unit can analyze the learner's social media activity and provide relevant feedback. For example, the analysis unit can provide relevant feedback based on topics the learner has shown interest in on social media. The analysis unit can also analyze the learner's social media activity and provide feedback related to areas of interest. Furthermore, the analysis unit can provide relevant feedback based on experts or groups the learner follows on social media. This allows the analysis unit to provide optimal feedback based on the learner's social media activity. Some or all of the above-described processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs prompts for analyzing the learner's social media activity to the generation AI, and the generation AI analyzes the activity. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0052] The management unit can apply an optimal management algorithm by referring to the learner's past learning data. For example, the management unit can apply an optimal management algorithm by referring to the learner's past learning data. For example, the management unit can provide optimal progress management based on the learner's past learning data. The management unit can also analyze the learner's past learning data and provide a management method according to the learner's learning progress. Furthermore, the management unit can also focus on managing progress in a specific area based on the learner's past learning data. This allows the management unit to provide an optimal management algorithm based on the learner's past learning data. Some or all of the above-mentioned processing in the management unit is performed using a generation AI. For example, the management unit can refer to the learner's past learning data and input prompts to the generation AI to apply the optimal management algorithm, and the generation AI analyzes the data. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0053] The management unit can customize the frequency and content of the monitoring according to the learner's learning progress. The management unit customizes the frequency and content of the monitoring according to, for example, the learner's learning progress. For example, if the learner is lagging behind in a particular area, the management unit can monitor the progress in that area more frequently. In addition, if the learner is making rapid progress in a particular area, the management unit can reduce the frequency of monitoring. Furthermore, the management unit can adjust the frequency and content of the monitoring according to the learner's learning progress. This allows the management unit to provide an optimal management method according to the learner's learning progress. Some or all of the above-mentioned processing in the management unit is performed using a generation AI. For example, the management unit evaluates the learner's learning progress and inputs prompts to the generation AI to customize the frequency and content of the monitoring, and the generation AI analyzes the data. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0054] The management unit can perform region-specific learning progress management by taking into account the learner's geographic location information. The management unit, for example, performs region-specific learning progress management by taking into account the learner's geographic location information. For example, if the learner is in a specific region, the management unit performs progress management related to that region. The management unit can also automatically perform region-specific progress management based on the learner's geographic location information. Furthermore, if the learner is traveling, the management unit can also perform related progress management based on the learner's current location. This allows the management unit to provide optimal learning progress management based on the learner's geographic location information. Some or all of the above-mentioned processing in the management unit is performed using a generation AI. For example, the management unit obtains the learner's geographic location information and inputs prompts for region-specific learning progress management to the generation AI, which then analyzes the information. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0055] The management unit can analyze the learner's social media activity and perform related learning progress management. For example, the management unit analyzes the learner's social media activity and performs related learning progress management. For example, the management unit performs related progress management based on topics in which the learner has shown interest on social media. The management unit can also analyze the learner's social media activity and perform progress management related to fields of interest. Furthermore, the management unit can perform related progress management based on experts and groups the learner follows on social media. This allows the management unit to provide optimal learning progress management based on the learner's social media activity. Some or all of the above-mentioned processing in the management unit is performed using a generation AI. For example, the management unit inputs prompts for analyzing the learner's social media activity to the generation AI, and the generation AI analyzes the activity. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The reception unit can analyze the learner's past learning history and automatically suggest the most appropriate learning field and level. For example, the reception unit can suggest the next learning field and level based on the learning fields and levels that the learner has frequently selected in the past. The reception unit can also identify areas in which the learner was weak in the past and make suggestions to encourage relearning that field from the basics. Furthermore, the reception unit can suggest new fields that the learner may be interested in based on the learner's past learning history. This allows the reception unit to provide the learner with an optimal learning plan based on the learner's past learning history.

[0058] The generator can customize the question format according to the learner's learning style. For example, the generator can generate questions that make extensive use of diagrams and graphs for learners who prefer visual learning. The generator can also present questions by audio for learners who prefer auditory learning. Furthermore, the generator can generate questions that include simulations and experiments for learners who prefer practical learning. In this way, the generator can provide optimal questions according to the learner's learning style.

[0059] The analysis unit can evaluate the learner's level of understanding based on the learner's answer data and suggest what content they should study next. For example, if the learner demonstrates a high level of understanding in a particular field, the analysis unit can suggest application problems in that field. In addition, if the learner demonstrates a low level of understanding in a particular field, the analysis unit can also suggest relearning that field from the basics. Furthermore, the analysis unit can also suggest new fields that the learner may be interested in based on the learner's answer data. This allows the analysis unit to provide the optimal study plan according to the learner's level of understanding.

[0060] The reception unit can prioritize input of area-specific learning fields by taking into consideration the learner's geographical location information. For example, if the learner is in a specific area, the reception unit can prioritize suggesting area-specific learning fields related to that area. The reception unit can also automatically display area-specific learning fields based on the learner's geographical location information. Furthermore, if the learner is traveling, the reception unit can also suggest related area-specific learning fields based on the learner's current location. In this way, the reception unit can provide the optimal area of ​​learning based on the learner's geographical location information.

[0061] The providing unit can select the optimal timing for providing questions by referring to the learner's past answer history. For example, the providing unit can analyze the time periods in which the learner previously answered questions and provide questions at those time periods. The providing unit can also predict the optimal timing for providing questions based on the learner's past answer history and provide questions. Furthermore, if the learner tends to concentrate on studying during a specific time period, the providing unit can also provide questions during that time period. This allows the providing unit to provide the optimal timing for providing questions based on the learner's past answer history.

[0062] The management unit can analyze the learner's social media activities and perform related learning progress management. For example, the management unit can perform related progress management based on topics in which the learner has shown interest on social media. The management unit can also analyze the learner's social media activities and perform progress management related to fields in which the learner is interested. Furthermore, the management unit can perform related progress management based on experts and groups that the learner follows on social media. This allows the management unit to provide optimal learning progress management based on the learner's social media activities.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit receives input of the field or level that the learner wants to study. The field or level that the learner wants to study can include, for example, mathematics, science, beginner, intermediate, advanced, etc. The reception unit analyzes the information that the learner has input and sends it to the generation unit in an appropriate format. Step 2: The generator generates questions based on the information received by the receiver. Using a generation AI, the generator automatically creates questions based on the learner's level and learning content, and can also adjust the difficulty and format of the questions. For example, if the learner is feeling stressed, it will prioritize generating easy questions, and if the learner is relaxed, it will generate more difficult questions. Step 3: The provider provides the questions generated by the generator to the learner. The provider provides the questions via an online platform, printed materials, an application, etc., and selects the optimal format for providing the questions taking into account the learner's device information. For example, if the learner is using a smartphone, the provider selects a format that matches the screen size. Step 4: The analysis unit analyzes the learner's answers to the questions provided by the provision unit and provides feedback. The analysis unit analyzes the learner's answers, determines whether they are correct or incorrect, and can also provide explanations of the answers and additional learning materials. Step 5: The management unit accumulates the answer data analyzed by the analysis unit and manages the progress of learning. The management unit accumulates the learner's answer data, evaluates the progress of learning, and if there is a lack of understanding in a particular area, it can focus questions on that area.

[0065] (Example 2) The learning support system according to an embodiment of the present invention uses a generation AI to generate questions from a wide range of subjects. This learning support system allows learners to select and answer questions tailored to their learning needs. Specifically, it consists of the following steps: First, the learner inputs the subject and level they wish to study. Next, the generation AI analyzes the input information and generates appropriate questions. The generated questions are provided to the learner, who answers them. Once the answer is complete, the generation AI analyzes the answers and provides feedback. Furthermore, the generation AI accumulates the learner's answer data and manages their learning progress. This allows learners to progress at their own pace and constantly challenge themselves with new questions. The system also takes learners' privacy into consideration. For example, when learners input the subject and level they wish to study, the system anonymizes their personal information to protect their privacy. Furthermore, the generation AI securely stores the learner's answer data and implements security measures to prevent it from being leaked to third parties. This allows learners to proceed with their studies with peace of mind. The learning support system allows learners to progress at their own pace and constantly challenge themselves with new questions.

[0066] A learning support system according to an embodiment includes a receiving unit, a generating unit, a providing unit, an analyzing unit, and a managing unit. The receiving unit receives input of a field or level that a learner wishes to study. Examples of the field or level that a learner wishes to study include, but are not limited to, mathematics, science, beginner, intermediate, and advanced. The receiving unit, for example, analyzes the information input by the learner and transmits it to the generating unit in an appropriate format. The generating unit generates questions based on the information received by the receiving unit using a generation AI. The generating unit, for example, automatically creates questions according to the learner's level and learning content. The generating unit can also adjust the difficulty and format of the questions using the generation AI. For example, if a learner is feeling stressed, the generating unit may prioritize generating easy questions. Alternatively, if a learner is relaxed, the generating unit may generate more difficult questions. The providing unit provides the questions generated by the generating unit to the learner. The providing unit can provide the questions through an online platform, for example. Alternatively, the providing unit can provide the questions through printed materials or an application. The providing unit can also select the optimal presentation format taking into account the learner's device information. For example, if the learner is using a smartphone, the providing unit selects a presentation format that matches the screen size. The analysis unit analyzes the learner's answers to the questions provided by the providing unit and provides feedback. For example, the analysis unit analyzes the learner's answers and determines whether they are correct or incorrect. The analysis unit can also provide explanations for the answers and additional learning materials. The management unit accumulates the answer data analyzed by the analysis unit and manages the learner's progress. For example, the management unit accumulates the learner's answer data and evaluates the learner's progress. If the learner's understanding in a particular area is lacking, the management unit can also focus on providing questions in that area. This allows the learning support system according to the embodiment to allow learners to progress through their studies at their own pace and constantly challenge themselves with new questions.

[0067] The generation unit can automatically create questions according to the learner's level or learning content. For example, the generation unit automatically creates questions according to the learner's level or learning content. For example, if a learner wants to learn beginner-level mathematics, the generation unit can generate basic calculation questions. If a learner wants to learn intermediate-level science, the generation unit can also generate questions about chemical reactions. Furthermore, if a learner wants to learn advanced-level history, the generation unit can generate questions about historical events. This allows the generation unit to provide optimal questions for the learner. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs prompts for generating questions to the generation AI based on the learner's level or learning content, and the generation AI generates the questions. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0068] The providing unit can provide the generated questions to the learner. The providing unit can provide the questions through, for example, an online platform. For example, the providing unit allows the learner to access the generated questions by logging in. The providing unit can also provide the questions through printed materials or an application. For example, the providing unit allows the learner to access the generated questions by receiving printed materials. The providing unit can also select the optimal presentation format taking into account the learner's device information. For example, if the learner is using a smartphone, the providing unit can select a presentation format that matches the screen size. Also, if the learner is using a tablet, the providing unit can select a presentation format optimized for a large screen. In this way, the providing unit allows the learner to access the generated questions. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs prompts for providing the generated questions to the generation AI, and the generation AI selects the optimal presentation format. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0069] The analysis unit can analyze the learner's answer and determine whether it is correct. For example, the analysis unit analyzes the learner's answer and determines whether it is correct or incorrect. For example, the analysis unit determines whether the answer to a math problem answered by the learner is correct. The analysis unit can also determine whether the answer to a science problem answered by the learner is correct. Furthermore, the analysis unit can determine whether the answer to a history problem answered by the learner is correct. This allows the analysis unit to evaluate the accuracy of the learner's answer. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs a prompt to the generation AI to analyze the learner's answer, and the generation AI analyzes the answer. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0070] The analysis unit can provide an explanation for the answer or additional learning materials. The analysis unit, for example, provides an explanation for the learner's answer. For example, the analysis unit can provide an explanation for the math problem answered by the learner. The analysis unit can also provide an explanation for the science problem answered by the learner. The analysis unit can also provide an explanation for the history problem answered by the learner. The analysis unit can also provide additional learning materials. For example, the analysis unit provides learning materials related to the problem answered by the learner. This allows the learner to deepen their understanding of the answer. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs prompts to the generation AI to provide an explanation for the answer or additional learning materials, and the generation AI generates the explanation or learning materials. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0071] The management unit can accumulate the learner's answer data and manage the learner's learning progress. For example, the management unit accumulates the learner's answer data and manages the learner's learning progress. For example, the management unit saves data on questions answered by the learner in a database. The management unit can also set criteria for evaluating the learner's progress. Furthermore, if the learner's understanding in a particular area is lacking, the management unit can focus on questions in that area. This allows the management unit to effectively manage the learner's learning progress. Some or all of the above-mentioned processing in the management unit is performed using a generation AI. For example, the management unit accumulates the learner's answer data and inputs prompts for progress management to the generation AI, which then accumulates the data and manages the progress. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0072] If a learner lacks understanding in a particular field, the management unit can prioritize questions in that field. For example, the management unit analyzes the learner's answer data, and if a learner lacks understanding in a particular field, the management unit prioritizes questions in that field. For example, if a learner lacks understanding in mathematics, the management unit can prioritize questions in mathematics. Furthermore, if a learner lacks understanding in science, the management unit can prioritize questions in science. Furthermore, if a learner lacks understanding in history, the management unit can prioritize questions in history. This allows the management unit to strengthen the learner's weak points. Some or all of the above-mentioned processing in the management unit is performed using a generation AI. For example, if a learner lacks understanding in a particular field, the management unit inputs prompts to the generation AI to prioritize questions in that field, and the generation AI generates the questions. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0073] The reception unit can estimate the learner's emotions and adjust the input method for the learning field and level based on the estimated learner's emotions. For example, the reception unit can estimate the learner's emotions and adjust the input method for the learning field and level based on the estimated learner's emotions. For example, if the learner is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Also, if the learner is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the learner is in a hurry, the reception unit can prioritize voice input to enable the learner to quickly input the learning field and level. This allows the reception unit to provide the optimal input method according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit is performed using the generation AI. For example, the reception unit inputs a prompt to estimate the learner's emotions to the generation AI, and the generation AI estimates the emotions. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0074] The reception unit can analyze the learner's past learning history and suggest the optimal input method. The reception unit, for example, analyzes the learner's past learning history and suggests the optimal input method. For example, the reception unit automatically displays as candidates learning fields and levels that the learner has frequently selected in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the learner has used in the past. Furthermore, the reception unit can predict and suggest the learning field and level to be used in a specific time period based on the learner's past learning history. This allows the reception unit to provide the optimal input method based on the learner's past learning history. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs a prompt to the generation AI to analyze the learner's past learning history, and the generation AI analyzes the history. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0075] The reception unit can customize the input fields based on the learner's current learning situation and areas of interest. The reception unit customizes the input fields based on, for example, the learner's current learning situation and areas of interest. For example, the reception unit preferentially displays input fields related to the learning area the learner is currently working on. The reception unit can also automatically suggest related learning areas and levels based on the learner's areas of interest. Furthermore, the reception unit can customize appropriate input fields according to the learner's current learning progress. In this way, the reception unit can provide optimal input fields according to the learner's current learning situation and areas of interest. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs prompts for analyzing the learner's current learning situation and areas of interest to the generation AI, and the generation AI analyzes the situation and areas of interest. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0076] The reception unit can estimate the learner's emotions and prioritize input based on the estimated learner's emotions. For example, the reception unit estimates the learner's emotions and prioritizes input based on the estimated learner's emotions. For example, if the learner is feeling stressed, the reception unit can prioritize displaying important input items and simplifying other items. Furthermore, if the learner is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the learner is in a hurry, the reception unit can prioritize displaying the most important input items to enable quick input. This allows the reception unit to provide optimal input priorities according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit is performed using the generation AI. For example, the reception unit inputs a prompt for estimating the learner's emotions to the generation AI, and the generation AI estimates the emotions. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0077] The reception unit can prioritize input of highly relevant learning fields taking into account the learner's geographical location information. For example, the reception unit prioritizes input of highly relevant learning fields taking into account the learner's geographical location information. For example, if the learner is in a specific region, the reception unit can prioritize suggesting learning fields related to that region. The reception unit can also automatically display learning fields specific to the region based on the learner's geographical location information. Furthermore, if the learner is traveling, the reception unit can also suggest related learning fields based on the learner's current location. This allows the reception unit to provide the optimal learning field based on the learner's geographical location information. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit acquires the learner's geographical location information and inputs a prompt to the generation AI to suggest related learning fields, and the generation AI analyzes the information. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0078] The reception unit can analyze the learner's social media activity and suggest related learning fields. For example, the reception unit can analyze the learner's social media activity and suggest related learning fields. For example, the reception unit can suggest related learning fields based on topics in which the learner has shown interest on social media. The reception unit can also analyze the learner's social media activity and prioritize displaying fields of interest. Furthermore, the reception unit can suggest related learning fields based on experts or groups the learner follows on social media. This allows the reception unit to provide the optimal learning field based on the learner's social media activity. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit inputs a prompt to analyze the learner's social media activity to the generation AI, and the generation AI analyzes the activity. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0079] The generation unit can estimate the learner's emotions and adjust the difficulty and format of questions based on the estimated learner's emotions. The generation unit, for example, estimates the learner's emotions and adjusts the difficulty and format of questions based on the estimated learner's emotions. For example, the generation unit preferentially generates easy questions when the learner is stressed. The generation unit can also generate difficult questions when the learner is relaxed. Furthermore, the generation unit can generate challenging questions when the learner is excited. This allows the generation unit to provide optimal questions according to the learner's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs a prompt to the generation AI to estimate the learner's emotions, and the generation AI estimates the emotions. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0080] The generation unit can apply an optimal question generation algorithm by referring to the learner's past answer data. The generation unit can apply an optimal question generation algorithm by referring to the learner's past answer data, for example. For example, the generation unit can analyze patterns of questions that the learner has answered correctly in the past and generate similar questions. The generation unit can also analyze patterns of questions that the learner has answered incorrectly in the past and generate review questions. Furthermore, the generation unit can generate questions according to the learner's learning progress based on the learner's past answer data. This allows the generation unit to provide optimal questions based on the learner's past answer data. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can refer to the learner's past answer data and input prompts to the generation AI to apply the optimal question generation algorithm, and the generation AI analyzes the data. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] The generation unit can customize the frequency and content of questions according to the learner's learning progress. The generation unit customizes the frequency and content of questions according to the learner's learning progress, for example. For example, if a learner is lagging behind in a particular area, the generation unit can frequently present questions in that area. Also, if a learner is making rapid progress in a particular area, the generation unit can present more difficult questions. Furthermore, the generation unit can adjust the frequency and content of questions according to the learner's learning progress. This allows the generation unit to provide optimal questions according to the learner's learning progress. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit evaluates the learner's learning progress and inputs prompts to the generation AI to customize the frequency and content of questions, and the generation AI analyzes the data. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0082] The generation unit can estimate the learner's emotions and adjust the order in which questions are presented based on the estimated learner's emotions. For example, the generation unit estimates the learner's emotions and adjusts the order in which questions are presented based on the estimated learner's emotions. For example, if the learner is feeling stressed, the generation unit presents questions in order from easier to less difficult. Furthermore, if the learner is relaxed, the generation unit can present more difficult questions first. Furthermore, if the learner is excited, the generation unit can present more challenging questions first. This allows the generation unit to provide an optimal order in which questions are presented based on the learner's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit is performed using the generation AI. For example, the generation unit inputs a prompt for estimating the learner's emotions into the generation AI, and the generation AI estimates the emotions. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0083] The generation unit can generate region-specific questions taking into account the learner's geographic location information. The generation unit generates region-specific questions taking into account the learner's geographic location information, for example. For example, if the learner is in a specific region, the generation unit generates questions related to that region. The generation unit can also automatically generate region-specific questions based on the learner's geographic location information. Furthermore, if the learner is traveling, the generation unit can generate relevant questions based on the learner's current location. This allows the generation unit to provide optimal questions based on the learner's geographic location information. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit obtains the learner's geographic location information and inputs prompts for generating region-specific questions to the generation AI, which then analyzes the information. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] The generator can analyze the learner's social media activity and generate relevant questions. For example, the generator can analyze the learner's social media activity and generate relevant questions. For example, the generator can generate relevant questions based on topics the learner has shown interest in on social media. The generator can also analyze the learner's social media activity and generate questions related to fields of interest. Furthermore, the generator can generate relevant questions based on experts or groups the learner follows on social media. This allows the generator to provide optimal questions based on the learner's social media activity. Some or all of the above-described processing in the generator can be performed using a generation AI. For example, the generator inputs prompts for analyzing the learner's social media activity to the generation AI, which then analyzes the activity. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] The providing unit can estimate the learner's emotions and adjust the way questions are presented based on the estimated learner's emotions. For example, the providing unit estimates the learner's emotions and adjusts the way questions are presented based on the estimated learner's emotions. For example, if the learner is feeling stressed, the providing unit can provide questions using a simple interface. If the learner is relaxed, the providing unit can provide questions using an interface with detailed explanations. Furthermore, if the learner is in a hurry, the providing unit can provide questions via audio. This allows the providing unit to provide the optimal way to present questions according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit is performed using the generation AI. For example, the providing unit inputs a prompt to the generation AI to estimate the learner's emotions, and the generation AI estimates the emotions. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0086] The providing unit can select the optimal timing for providing questions by referring to the learner's past answer history. The providing unit selects the optimal timing for providing questions by referring to, for example, the learner's past answer history. For example, the providing unit analyzes the time periods in which the learner previously answered questions and provides questions at those time periods. The providing unit can also predict the optimal timing for providing questions based on the learner's past answer history and provide the questions. Furthermore, if the learner tends to concentrate on studying during a specific time period, the providing unit can provide questions during that time period. This allows the providing unit to provide the optimal timing for providing questions based on the learner's past answer history. Some or all of the above-described processing in the providing unit is performed using a generation AI. For example, the providing unit refers to the learner's past answer history and inputs prompts for selecting the optimal timing for providing questions to the generation AI, which then analyzes the data. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] The providing unit can select the optimal delivery format by taking into account the learner's device information. For example, the providing unit selects the optimal delivery format by taking into account the learner's device information. For example, if the learner is using a smartphone, the providing unit selects a delivery format that matches the screen size. Also, if the learner is using a tablet, the providing unit can select a delivery format optimized for a large screen. Furthermore, if the learner is using a personal computer, the providing unit can select a delivery format that includes detailed information. This allows the providing unit to provide the optimal delivery format based on the learner's device information. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit acquires the learner's device information and inputs a prompt to the generation AI to select the optimal delivery format, and the generation AI analyzes the information. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0088] The providing unit can estimate the learner's emotions and adjust the order in which questions are presented based on the estimated learner's emotions. The providing unit, for example, estimates the learner's emotions and adjusts the order in which questions are presented based on the estimated learner's emotions. For example, if the learner is feeling stressed, the providing unit can present questions in order starting with easier questions. Furthermore, if the learner is relaxed, the providing unit can present more difficult questions first. Furthermore, if the learner is excited, the providing unit can present more challenging questions first. This allows the providing unit to provide an optimal order in which questions are presented according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit is performed using the generation AI. For example, the providing unit inputs a prompt to the generation AI to estimate the learner's emotions, and the generation AI estimates the emotions. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] The providing unit can prioritize providing highly relevant questions by taking into account the learner's geographical location information. For example, the providing unit prioritizes providing highly relevant questions by taking into account the learner's geographical location information. For example, if the learner is in a specific region, the providing unit prioritizes providing questions related to that region. The providing unit can also automatically provide region-specific questions based on the learner's geographical location information. Furthermore, if the learner is traveling, the providing unit can provide relevant questions based on the learner's current location. This allows the providing unit to provide optimal questions based on the learner's geographical location information. Some or all of the above-described processing in the providing unit is performed using a generation AI. For example, the providing unit acquires the learner's geographical location information and inputs prompts to the generation AI to provide relevant questions, and the generation AI analyzes the information. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] The providing unit can analyze the learner's social media activity and provide relevant questions. For example, the providing unit can analyze the learner's social media activity and provide relevant questions. For example, the providing unit can provide relevant questions based on topics the learner has shown interest in on social media. The providing unit can also analyze the learner's social media activity and provide questions related to fields of interest. Furthermore, the providing unit can provide relevant questions based on experts or groups the learner follows on social media. This allows the providing unit to provide optimal questions based on the learner's social media activity. Some or all of the above-described processing in the providing unit is performed using a generation AI. For example, the providing unit inputs prompts for analyzing the learner's social media activity to the generation AI, and the generation AI analyzes the activity. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0091] The analysis unit can estimate the learner's emotions and adjust the answer analysis method based on the estimated learner's emotions. For example, the analysis unit estimates the learner's emotions and adjusts the answer analysis method based on the estimated learner's emotions. For example, if the learner is stressed, the analysis unit provides a concise and easy-to-understand answer analysis. Furthermore, if the learner is relaxed, the analysis unit can provide a detailed answer analysis. Furthermore, if the learner is excited, the analysis unit can provide a visually stimulating answer analysis. This allows the analysis unit to provide an optimal answer analysis method according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs a prompt to the generation AI to estimate the learner's emotions, and the generation AI estimates the emotions. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0092] The analysis unit can apply an optimal analysis algorithm by referring to the learner's past answer data. The analysis unit can apply an optimal analysis algorithm by referring to, for example, the learner's past answer data. For example, the analysis unit can analyze patterns of questions that the learner has answered correctly in the past and analyze answers to similar questions. The analysis unit can also analyze patterns of questions that the learner has answered incorrectly in the past and analyze answers for review. Furthermore, the analysis unit can perform answer analysis according to the learner's learning progress based on the learner's past answer data. This allows the analysis unit to provide an optimal analysis algorithm based on the learner's past answer data. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can refer to the learner's past answer data and input prompts to the generation AI to apply the optimal analysis algorithm, and the generation AI analyzes the data. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] The analysis unit can customize the content and format of the feedback according to the learner's learning progress. The analysis unit customizes the content and format of the feedback according to, for example, the learner's learning progress. For example, if the learner is lagging behind in a particular area, the analysis unit can provide detailed feedback on that area. In addition, if the learner is making rapid progress in a particular area, the analysis unit can provide concise feedback. Furthermore, the analysis unit can adjust the content and format of the feedback according to the learner's learning progress. This allows the analysis unit to provide optimal feedback according to the learner's learning progress. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit evaluates the learner's learning progress and inputs prompts to the generation AI to customize the content and format of the feedback, and the generation AI analyzes the data. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] The analysis unit can estimate the learner's emotions and determine the priority of feedback based on the estimated learner's emotions. For example, the analysis unit estimates the learner's emotions and determines the priority of feedback based on the estimated learner's emotions. For example, if the learner is feeling stressed, the analysis unit can prioritize important feedback. The analysis unit can also provide detailed feedback if the learner is relaxed. Furthermore, if the learner is in a hurry, the analysis unit can provide feedback that focuses on the main points. This allows the analysis unit to provide optimal feedback priorities according to the learner's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs a prompt to the generation AI to estimate the learner's emotions, and the generation AI estimates the emotions. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0095] The analysis unit can provide region-specific feedback by taking into account the learner's geographic location information. The analysis unit can provide region-specific feedback by taking into account the learner's geographic location information, for example. For example, if the learner is in a specific region, the analysis unit can provide feedback related to that region. The analysis unit can also automatically provide region-specific feedback based on the learner's geographic location information. Furthermore, if the learner is traveling, the analysis unit can provide relevant feedback based on the learner's current location. This allows the analysis unit to provide optimal feedback based on the learner's geographic location information. Some or all of the above-described processing in the analysis unit is performed using a generation AI. For example, the analysis unit obtains the learner's geographic location information and inputs prompts for providing region-specific feedback to the generation AI, which then analyzes the information. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] The analysis unit can analyze the learner's social media activity and provide relevant feedback. For example, the analysis unit can analyze the learner's social media activity and provide relevant feedback. For example, the analysis unit can provide relevant feedback based on topics the learner has shown interest in on social media. The analysis unit can also analyze the learner's social media activity and provide feedback related to areas of interest. Furthermore, the analysis unit can provide relevant feedback based on experts or groups the learner follows on social media. This allows the analysis unit to provide optimal feedback based on the learner's social media activity. Some or all of the above-described processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs prompts for analyzing the learner's social media activity to the generation AI, and the generation AI analyzes the activity. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0097] The management unit can estimate the learner's emotions and adjust the learning progress management method based on the estimated learner's emotions. For example, the management unit estimates the learner's emotions and adjusts the learning progress management method based on the estimated learner's emotions. For example, if the learner is feeling stressed, the management unit can provide concise and easy-to-understand progress management. Furthermore, if the learner is relaxed, the management unit can provide detailed progress management. Furthermore, if the learner is excited, the management unit can provide visually stimulating progress management. This allows the management unit to provide an optimal learning progress management method according to the learner's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the management unit is performed using the generation AI. For example, the management unit inputs a prompt to the generation AI to estimate the learner's emotions, and the generation AI estimates the emotions. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0098] The management unit can apply an optimal management algorithm by referring to the learner's past learning data. For example, the management unit can apply an optimal management algorithm by referring to the learner's past learning data. For example, the management unit can provide optimal progress management based on the learner's past learning data. The management unit can also analyze the learner's past learning data and provide a management method according to the learner's learning progress. Furthermore, the management unit can also focus on managing progress in a specific area based on the learner's past learning data. This allows the management unit to provide an optimal management algorithm based on the learner's past learning data. Some or all of the above-mentioned processing in the management unit is performed using a generation AI. For example, the management unit can refer to the learner's past learning data and input prompts to the generation AI to apply the optimal management algorithm, and the generation AI analyzes the data. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0099] The management unit can customize the frequency and content of the monitoring according to the learner's learning progress. The management unit customizes the frequency and content of the monitoring according to, for example, the learner's learning progress. For example, if the learner is lagging behind in a particular area, the management unit can monitor the progress in that area more frequently. In addition, if the learner is making rapid progress in a particular area, the management unit can reduce the frequency of monitoring. Furthermore, the management unit can adjust the frequency and content of the monitoring according to the learner's learning progress. This allows the management unit to provide an optimal management method according to the learner's learning progress. Some or all of the above-mentioned processing in the management unit is performed using a generation AI. For example, the management unit evaluates the learner's learning progress and inputs prompts to the generation AI to customize the frequency and content of the monitoring, and the generation AI analyzes the data. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0100] The management unit can estimate the learner's emotions and determine the priority of learning progress based on the estimated learner's emotions. For example, the management unit estimates the learner's emotions and determines the priority of learning progress based on the estimated learner's emotions. For example, if the learner is feeling stressed, the management unit can prioritize important progress management. Furthermore, if the learner is relaxed, the management unit can also perform detailed progress management. Furthermore, if the learner is in a hurry, the management unit can perform progress management that focuses on the main points. This allows the management unit to provide optimal learning progress priorities according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the management unit is performed using the generation AI. For example, the management unit inputs a prompt to the generation AI to estimate the learner's emotions, and the generation AI estimates the emotions. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0101] The management unit can perform region-specific learning progress management by taking into account the learner's geographic location information. The management unit, for example, performs region-specific learning progress management by taking into account the learner's geographic location information. For example, if the learner is in a specific region, the management unit performs progress management related to that region. The management unit can also automatically perform region-specific progress management based on the learner's geographic location information. Furthermore, if the learner is traveling, the management unit can also perform related progress management based on the learner's current location. This allows the management unit to provide optimal learning progress management based on the learner's geographic location information. Some or all of the above-mentioned processing in the management unit is performed using a generation AI. For example, the management unit obtains the learner's geographic location information and inputs prompts for region-specific learning progress management to the generation AI, which then analyzes the information. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0102] The management unit can analyze the learner's social media activity and perform related learning progress management. For example, the management unit analyzes the learner's social media activity and performs related learning progress management. For example, the management unit performs related progress management based on topics in which the learner has shown interest on social media. The management unit can also analyze the learner's social media activity and perform progress management related to fields of interest. Furthermore, the management unit can perform related progress management based on experts and groups the learner follows on social media. This allows the management unit to provide optimal learning progress management based on the learner's social media activity. Some or all of the above-mentioned processing in the management unit is performed using a generation AI. For example, the management unit inputs prompts for analyzing the learner's social media activity to the generation AI, and the generation AI analyzes the activity. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, provision unit, analysis unit, and management unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and inputs the field and level the learner wants to study. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates questions using a generation AI. The provision unit is realized, for example, by the output device 40 of the smart device 14, and provides the generated questions to the learner. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the learner's answers and provides feedback. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and accumulates the learner's answer data and manages the learner's progress. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, provision unit, analysis unit, and management unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and the learner inputs the field and level they wish to study by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates questions using a generation AI. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides the generated questions to the learner. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the learner's answers and provides feedback. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and accumulates the learner's answer data and manages the learner's progress. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, analysis unit, and management unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, and the learner voice-inputs the field and level they want to study. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates questions using a generation AI. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314, and provides the generated questions to the learner. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the learner's answers and provides feedback. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and accumulates the learner's answer data and manages the learner's progress. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, analysis unit, and management unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and the learner inputs the field and level they want to study by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates questions using a generation AI. The provision unit is realized, for example, by the speaker 240 of the robot 414, and provides the generated questions to the learner. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the learner's answers and provides feedback. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and accumulates the learner's answer data and manages the learner's progress.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The reception unit can analyze the learner's past learning history and automatically suggest the most appropriate learning field and level. For example, the reception unit can suggest the next learning field and level based on the learning fields and levels that the learner has frequently selected in the past. The reception unit can also identify areas in which the learner was weak in the past and make suggestions to encourage relearning that field from the basics. Furthermore, the reception unit can suggest new fields that the learner may be interested in based on the learner's past learning history. This allows the reception unit to provide the learner with an optimal learning plan based on the learner's past learning history.

[0105] The generator can customize the question format according to the learner's learning style. For example, the generator can generate questions that make extensive use of diagrams and graphs for learners who prefer visual learning. The generator can also present questions by audio for learners who prefer auditory learning. Furthermore, the generator can generate questions that include simulations and experiments for learners who prefer practical learning. In this way, the generator can provide optimal questions according to the learner's learning style.

[0106] The providing unit can estimate the learner's emotions and adjust the method of providing questions based on the estimated learner's emotions. For example, if the learner is feeling stressed, the providing unit can provide questions using a simple interface. If the learner is relaxed, the providing unit can also provide questions using an interface including detailed explanations. Furthermore, if the learner is in a hurry, the providing unit can also provide questions by voice. In this way, the providing unit can provide the optimal method of providing questions according to the learner's emotions.

[0107] The analysis unit can evaluate the learner's level of understanding based on the learner's answer data and suggest what content they should study next. For example, if the learner demonstrates a high level of understanding in a particular field, the analysis unit can suggest application problems in that field. In addition, if the learner demonstrates a low level of understanding in a particular field, the analysis unit can also suggest relearning that field from the basics. Furthermore, the analysis unit can also suggest new fields that the learner may be interested in based on the learner's answer data. This allows the analysis unit to provide the optimal study plan according to the learner's level of understanding.

[0108] The management unit can estimate the learner's emotions and adjust the learning progress management method based on the estimated learner's emotions. For example, if the learner is feeling stressed, the management unit can provide simple and easy-to-understand progress management. If the learner is relaxed, the management unit can also provide detailed progress management. Furthermore, if the learner is excited, the management unit can also provide visually stimulating progress management. In this way, the management unit can provide the optimal learning progress management method according to the learner's emotions.

[0109] The reception unit can prioritize input of area-specific learning fields by taking into consideration the learner's geographical location information. For example, if the learner is in a specific area, the reception unit can prioritize suggesting area-specific learning fields related to that area. The reception unit can also automatically display area-specific learning fields based on the learner's geographical location information. Furthermore, if the learner is traveling, the reception unit can also suggest related area-specific learning fields based on the learner's current location. In this way, the reception unit can provide the optimal area of ​​learning based on the learner's geographical location information.

[0110] The generation unit can estimate the learner's emotions and adjust the difficulty and format of the questions based on the estimated learner's emotions. For example, if the learner is feeling stressed, the generation unit can preferentially generate easy questions. Also, if the learner is relaxed, the generation unit can generate difficult questions. Furthermore, if the learner is excited, the generation unit can generate challenging questions. In this way, the generation unit can provide optimal questions according to the learner's emotions.

[0111] The providing unit can select the optimal timing for providing questions by referring to the learner's past answer history. For example, the providing unit can analyze the time periods in which the learner previously answered questions and provide questions at those time periods. The providing unit can also predict the optimal timing for providing questions based on the learner's past answer history and provide questions. Furthermore, if the learner tends to concentrate on studying during a specific time period, the providing unit can also provide questions during that time period. This allows the providing unit to provide the optimal timing for providing questions based on the learner's past answer history.

[0112] The analysis unit can estimate the learner's emotions and determine the priority of feedback based on the estimated learner's emotions. For example, if the learner is feeling stressed, the analysis unit can provide important feedback with priority. If the learner is relaxed, the analysis unit can also provide detailed feedback. Furthermore, if the learner is in a hurry, the analysis unit can also provide feedback that focuses on the main points. In this way, the analysis unit can provide optimal feedback priority according to the learner's emotions.

[0113] The management unit can analyze the learner's social media activities and perform related learning progress management. For example, the management unit can perform related progress management based on topics in which the learner has shown interest on social media. The management unit can also analyze the learner's social media activities and perform progress management related to fields in which the learner is interested. Furthermore, the management unit can perform related progress management based on experts and groups that the learner follows on social media. This allows the management unit to provide optimal learning progress management based on the learner's social media activities.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The reception unit receives input of the field or level that the learner wants to study. The field or level that the learner wants to study can include, for example, mathematics, science, beginner, intermediate, advanced, etc. The reception unit analyzes the information that the learner has input and sends it to the generation unit in an appropriate format. Step 2: The generator generates questions based on the information received by the receiver. Using a generation AI, the generator automatically creates questions based on the learner's level and learning content, and can also adjust the difficulty and format of the questions. For example, if the learner is feeling stressed, it will prioritize generating easy questions, and if the learner is relaxed, it will generate more difficult questions. Step 3: The provider provides the questions generated by the generator to the learner. The provider provides the questions via an online platform, printed materials, an application, etc., and selects the optimal format for providing the questions taking into account the learner's device information. For example, if the learner is using a smartphone, the provider selects a format that matches the screen size. Step 4: The analysis unit analyzes the learner's answers to the questions provided by the provision unit and provides feedback. The analysis unit analyzes the learner's answers, determines whether they are correct or incorrect, and can also provide explanations of the answers and additional learning materials. Step 5: The management unit accumulates the answer data analyzed by the analysis unit and manages the progress of learning. The management unit accumulates the learner's answer data, evaluates the progress of learning, and if there is a lack of understanding in a particular area, it can focus questions on that area.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0118] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0150] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0159] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0160] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0167] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0171] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0172] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0173] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0177] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0178] 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.

[0179] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0180] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0181] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0182] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0184] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0185] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0186] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0187] [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a reception unit that receives input of the field or level that a learner wishes to study; a generation unit that generates questions based on the information received by the reception unit; a provision unit that provides the learner with the questions generated by the generation unit; an analysis unit that analyzes the learner's answers to the questions provided by the provision unit and provides feedback; and a management unit that accumulates the answer data analyzed by the analysis unit and manages the progress of learning.

2. The system according to claim 1 , wherein the generation unit automatically generates questions according to the level of the learner or the content of the study.

3. The providing unit Provide generated questions to learners 2. The system of claim 1.

4. 2. The system according to claim 1, wherein the analysis unit analyzes the learner's answer and determines whether the answer is correct or not.

5. The analysis unit Provides answer explanations and additional study resources 2. The system of claim 1.

6. The management unit Accumulates learners' answer data and manages their learning progress 2. The system of claim 1.

7. The management unit If you lack understanding in a particular area, questions will be focused on that area.

2. The system of claim 1.

8. The reception unit Estimate the learner's emotions and adjust the input method for the learning field and level based on the estimated learner's emotions 2. The system of claim 1.

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