System

Abstract

An object of the system according to the embodiment is to provide an environment in which a learner can ask questions without hesitation and to create an educational program corresponding to needs of each learner.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, a provision unit, a tracking unit, and an evaluation unit. The reception unit receives a question. The generation unit analyzes the question received by the reception unit and generates an answer. The providing unit provides the answer generated by the generating unit. The tracking unit tracks progress of the learner based on the answer provided by the providing unit. The evaluation unit evaluates the understanding level of the learner based on the progress tracked by the tracking unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult for learners to ask questions during class, and educational programs that meet the needs of individual learners are not adequately provided.

[0005] The system according to the embodiment aims to provide an environment in which learners can ask questions without hesitation and to create an educational program that meets the needs of each individual learner. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, a providing unit, a tracking unit, and an evaluating unit. The receiving unit receives a question. The generating unit analyzes the question received by the receiving unit and generates an answer. The providing unit provides the answer generated by the generating unit. The tracking unit tracks the progress of the learner based on the answer provided by the providing unit. The evaluating unit evaluates the level of understanding of the learner based on the progress tracked by the tracking unit. [Effects of the Invention]

[0007] The system according to the embodiment provides an environment in which learners can ask questions without hesitation, and makes it possible to create educational programs that meet the needs of individual learners. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An educational program creation system according to an embodiment of the present invention uses a generative AI to create educational programs tailored to the needs of individual learners. This system provides an AI-based question-and-answer service for lessons. In Japan, many students find it difficult to ask questions to teachers during class due to embarrassment or other reasons, but they can consult with the AI ​​without hesitation. In the future, we envision tracking learners' progress and comprehension levels and preparing learning materials tailored to their abilities. For example, a learner inputs a question during class. The generative AI then analyzes the question and generates an appropriate answer. The generated answer is provided to the learner. This allows learners to easily ask questions during class and deepen their understanding. Furthermore, in the future, we will add a function to track learners' progress and comprehension levels. For example, we will record the questions asked by the learner and the answers received, and evaluate the learner's level of understanding. Based on this, we can provide optimal learning materials to the learner. For example, if a learner's understanding is lacking in a particular area, we will provide specialized materials for that area. In this way, personalized education tailored to each learner is realized. This allows the educational program creation system to meet learners' needs and support effective learning. For example, learners can progress at their own pace and deepen their understanding. It also makes it easier for teachers to understand the progress of students, enabling them to provide more effective instruction. For example, they can identify areas in which a particular student is struggling and provide appropriate support to that student.

[0029] An educational program creation system according to an embodiment includes a receiving unit, a generating unit, a providing unit, a tracking unit, and an evaluating unit. The receiving unit receives questions entered by a learner during class. For example, the receiving unit can receive questions in text, audio, image, or other formats. The generating unit uses a generation AI to analyze the questions received by the receiving unit and generate an appropriate answer. For example, the generating unit analyzes the questions and generates answers using natural language processing technology or a machine learning algorithm. The generating unit inputs a prompt to the generation AI, such as "Please generate an answer to this question," and the generation AI generates an answer. The providing unit provides the answers generated by the generating unit to the learner. For example, the providing unit can provide answers to the learner in text, audio, image, or other formats. The tracking unit tracks the learner's progress based on the answers provided by the providing unit. For example, the tracking unit records what questions the learner asked and what answers they received, and evaluates the learner's progress. The evaluating unit evaluates the learner's level of understanding based on the progress tracked by the tracking unit. For example, the evaluation unit evaluates the learner's level of understanding based on test results, feedback, etc. As a result, the educational program creation system according to the embodiment can provide appropriate answers to learners' questions, track and evaluate their progress and level of understanding, and provide an educational program tailored to each individual learner.

[0030] The reception unit can analyze the learner's past question history and select the optimal reception method. For example, the reception unit preferentially suggests question formats that the learner has frequently used in the past. The reception unit can also automatically suggest related questions based on the content of questions asked by the learner in the past. The reception unit can also select the most effective question reception method from the learner's past question history. In this way, by analyzing the past question history, it is possible to provide the learner with the optimal question reception method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the learner's past question history data into the generation AI and have the generation AI select the optimal reception method.

[0031] When receiving questions, the reception unit can filter the questions based on the learner's current learning situation and areas of interest. For example, the reception unit can only accept questions related to the topic the learner is currently studying. The reception unit can also preferentially accept questions that are highly relevant based on the learner's areas of interest. The reception unit can also filter questions of an appropriate level of difficulty according to the learner's progress. In this way, by filtering questions based on the learner's current learning situation and areas of interest, it is possible to preferentially accept questions that are highly relevant. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the learner's learning situation data to a generation AI and have the generation AI perform question filtering.

[0032] When receiving a question, the reception unit can select an appropriate reception means depending on the learner's input method. For example, if the learner uses voice input, the reception unit can receive the question using voice recognition technology. If the learner uses text input, the reception unit can also receive the question using text analysis technology. If the learner uses an image to ask a question, the reception unit can also receive the question using image recognition technology. This allows questions to be received smoothly by selecting the optimal reception means depending on the learner's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the learner's input data into a generation AI and have the generation AI select the optimal reception means.

[0033] When receiving questions, the reception unit can prioritize receiving highly relevant questions by taking into account the learner's geographical location information. For example, if the learner is in a specific area, the reception unit can prioritize receiving questions related to that area. If the learner is traveling, the reception unit can also prioritize receiving questions related to the travel destination. If the learner is at home, the reception unit can also prioritize receiving questions related to studying at home. In this way, by taking the learner's geographical location information into account, highly relevant questions can be prioritized. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the learner's geographical location information data to the generation AI and cause the generation AI to select highly relevant questions.

[0034] When receiving a question, the reception unit can analyze the learner's social media activity and receive related questions. For example, the reception unit can receive related questions based on content shared by the learner on social media. The reception unit can also suggest questions that the learner is likely to be interested in based on the learner's social media activity. The reception unit can also receive related questions by referring to the activity of the learner's friends on social media. In this way, by analyzing the learner's social media activity, highly relevant questions can be received. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the learner's social media data into a generation AI and cause the generation AI to select related questions.

[0035] When accepting a question, the acceptance unit can customize the acceptance method by reflecting the learner's past feedback. For example, the acceptance unit preferentially suggests question formats that the learner has previously preferred. The acceptance unit can also select the optimal question acceptance method based on the learner's past feedback. The acceptance unit can also customize the question acceptance interface by referring to the learner's past feedback. In this way, the optimal question acceptance method can be provided by reflecting the learner's past feedback. Some or all of the above-mentioned processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the learner's past feedback data into the generation AI and have the generation AI customize the acceptance method.

[0036] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. For example, the generation unit generates a detailed answer for an important question. The generation unit can also generate a concise answer for a general question. The generation unit can also adjust the level of detail of the answer according to the learner's level of understanding. In this way, by adjusting the level of detail of the answer based on the importance of the question, it is possible to provide an answer with an appropriate level of detail. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0037] When generating an answer, the generation unit can apply different generation algorithms depending on the question category. For example, for a math question, the generation unit can apply an algorithm that generates an answer that includes a mathematical formula. For a history question, the generation unit can also apply an algorithm that generates an answer that includes dates and events. For a science question, the generation unit can also apply an algorithm that generates an answer that includes experimental results and theories. In this way, by applying different generation algorithms depending on the question category, more appropriate answers can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question category data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0038] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the learner's past answer results. The generation unit generates a more accurate answer, for example, based on answers received by the learner in the past. The generation unit can also add supplementary information to avoid misunderstandings from the learner's past answer results. The generation unit can also analyze the learner's past answer results and generate an optimal answer. In this way, the accuracy of the answer can be improved by referring to the learner's past answer results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the learner's past answer result data into the generation AI and cause the generation AI to improve the accuracy of the answer.

[0039] When providing an answer, the providing unit can select an appropriate providing method by referring to the learner's past question history. For example, the providing unit preferentially uses a providing method that the learner has previously preferred. The providing unit can also select the optimal providing method from the learner's past question history. The providing unit can also analyze the learner's past question history and select the most effective providing method. In this way, the optimal providing method can be selected by referring to the learner's past question history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the learner's past question history data into the generation AI and have the generation AI select the providing method.

[0040] When providing an answer, the providing unit can customize the provided content based on the learner's current learning situation. The providing unit, for example, provides an answer related to a topic the learner is currently learning. The providing unit can also provide an answer of an appropriate level of difficulty depending on the learner's progress. The providing unit can also provide highly relevant answers based on the learner's areas of interest. In this way, by customizing the provided content based on the learner's current learning situation, more appropriate answers can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the learner's learning situation data into a generation AI and cause the generation AI to customize the provided content.

[0041] The providing unit can improve the answer providing method by reflecting the learner's feedback when providing an answer. The providing unit, for example, customizes the answer providing method based on the learner's feedback. The providing unit can also select the optimal answer providing method by referring to the learner's feedback. The providing unit can also analyze the learner's feedback and improve the answer providing method. In this way, the answer providing method can be improved by reflecting the learner's feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the learner's feedback data into the generating AI and cause the generating AI to improve the answer providing method.

[0042] When tracking progress, the tracking unit can evaluate current progress by referring to past tracking data. The tracking unit evaluates current progress based on, for example, the learner's past progress data. The tracking unit can also analyze progress trends from the learner's past tracking data. The tracking unit can also compare and evaluate current progress by referring to the learner's past tracking data. This allows current progress to be accurately evaluated by referring to the past tracking data. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the learner's past tracking data into the generation AI and have the generation AI evaluate current progress.

[0043] The tracking unit can apply different tracking methods to different learner categories when tracking progress. For example, the tracking unit applies a basic progress tracking method to beginner learners. The tracking unit can also apply a detailed progress tracking method to intermediate learners. The tracking unit can also apply a specialized progress tracking method to advanced learners. In this way, applying different tracking methods to different learner categories enables more appropriate progress tracking. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input learner category data to the generation AI and cause the generation AI to apply an appropriate tracking method.

[0044] The tracking unit can track progress while taking into account the learner's attribute information. The tracking unit selects an appropriate progress tracking method, for example, depending on the learner's age. The tracking unit can also apply an optimal progress tracking method depending on the learner's learning style. The tracking unit can also customize the progress tracking method based on the learner's learning history. This enables more appropriate progress tracking by taking the learner's attribute information into consideration. Some or all of the above-mentioned processing in the tracking unit can be performed, for example, using AI, or can be performed without using AI. For example, the tracking unit can input the learner's attribute information data into the generation AI and cause the generation AI to select a tracking method.

[0045] When tracking progress, the tracking unit can analyze changes in progress based on the learner's submission time. The tracking unit analyzes changes in progress based on, for example, the time of assignment submission by the learner. The tracking unit can also analyze progress trends from the learner's submission time. The tracking unit can also evaluate changes in progress by referring to the learner's submission time. In this way, progress trends can be grasped by analyzing changes in progress based on the learner's submission time. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input data on the learner's submission time into the generation AI and cause the generation AI to analyze changes in progress.

[0046] When tracking progress, the tracking unit can analyze the progress by referring to the learner's related data. The tracking unit, for example, analyzes the progress based on the learner's related data. The tracking unit can also analyze the progress trend from the learner's related data. The tracking unit can also evaluate the progress by referring to the learner's related data. In this way, the progress trend can be grasped by referring to the learner's related data. Some or all of the above-mentioned processing in the tracking unit may be performed using AI, for example, or may be performed without using AI. For example, the tracking unit can input the learner's related data into the generation AI and cause the generation AI to perform an analysis of the progress.

[0047] The tracking unit can track progress while taking into account the learner's learning style. For example, the tracking unit selects an appropriate progress tracking method according to the learner's learning style. The tracking unit can also apply an optimal progress tracking method based on the learner's learning style. The tracking unit can also customize the progress tracking method by taking into account the learner's learning style. This enables more appropriate progress tracking by taking the learner's learning style into consideration. Some or all of the above-mentioned processing in the tracking unit can be performed using, for example, AI, or can be performed without using AI. For example, the tracking unit can input the learner's learning style data into the generation AI and cause the generation AI to select a tracking method.

[0048] When assessing the level of understanding, the evaluation unit can evaluate the current level of understanding by referring to past evaluation data. The evaluation unit evaluates the current level of understanding based on, for example, the learner's past evaluation data. The evaluation unit can also analyze trends in the level of understanding from the learner's past evaluation data. The evaluation unit can also compare and evaluate the current level of understanding by referring to the learner's past evaluation data. This makes it possible to accurately evaluate the current level of understanding by referring to the past evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the learner's past evaluation data into the generation AI and have the generation AI evaluate the current level of understanding.

[0049] The evaluation unit can apply different evaluation methods to different learner categories when evaluating comprehension. For example, the evaluation unit applies a basic comprehension evaluation method to beginner learners. The evaluation unit can also apply a detailed comprehension evaluation method to intermediate learners. The evaluation unit can also apply a specialized comprehension evaluation method to advanced learners. In this way, applying different evaluation methods to different learner categories enables more appropriate comprehension evaluation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input learner category data into the generation AI and cause the generation AI to apply an appropriate evaluation method.

[0050] The evaluation unit can perform comprehension assessment by taking into account the learner's attribute information. The evaluation unit selects an appropriate comprehension assessment method, for example, depending on the learner's age. The evaluation unit can also apply the optimal comprehension assessment method depending on the learner's learning style. The evaluation unit can also customize the comprehension assessment method based on the learner's learning history. This enables more appropriate comprehension assessment by taking into account the learner's attribute information. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the learner's attribute information data into the generation AI and have the generation AI select the assessment method.

[0051] During comprehension assessment, the evaluation unit can analyze changes in the learner's level of understanding based on the time of submission. The evaluation unit analyzes changes in the learner's level of understanding based on, for example, the time of submission of the assignment by the learner. The evaluation unit can also analyze trends in the learner's level of understanding from the time of submission. The evaluation unit can also evaluate changes in the learner's level of understanding by referring to the time of submission of the learner. In this way, trends in the learner's level of understanding can be grasped by analyzing changes in the learner's level of understanding based on the time of submission. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the learner's submission time into the generation AI and cause the generation AI to analyze changes in the learner's level of understanding.

[0052] The evaluation unit can analyze the level of comprehension by referring to the learner's related data when evaluating the level of comprehension. The evaluation unit, for example, analyzes the level of comprehension based on the learner's related data. The evaluation unit can also analyze the trend of the level of comprehension from the learner's related data. The evaluation unit can also evaluate the level of comprehension by referring to the learner's related data. In this way, the trend of the level of comprehension can be grasped by referring to the learner's related data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the learner's related data into the generation AI and have the generation AI perform an analysis of the level of comprehension.

[0053] The evaluation unit can perform comprehension assessment taking into account the learner's learning style. For example, the evaluation unit selects an appropriate comprehension assessment method according to the learner's learning style. The evaluation unit can also apply the optimal comprehension assessment method based on the learner's learning style. The evaluation unit can also customize the comprehension assessment method taking into account the learner's learning style. This enables more appropriate comprehension assessment by taking the learner's learning style into consideration. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the learner's learning style data into the generation AI and have the generation AI select the assessment method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The generation unit can analyze the learner's past learning patterns and select the optimal answer format. For example, it can provide answer formats that the learner has preferred in the past with priority. It can also generate relevant answers based on answer formats that the learner has found easy to understand in the past. It can also select the most effective answer format from the learner's past learning patterns. In this way, it is possible to provide the learner with the optimal answer format by analyzing their past learning patterns.

[0056] The tracking unit can monitor the learner's learning environment and adjust the progress tracking method. For example, if the learner is studying in a quiet environment, detailed progress tracking can be performed. If the learner is studying in a noisy environment, progress tracking can be performed more slowly. If the learner is on the move, progress tracking can also be temporarily stopped. In this way, more appropriate progress tracking can be achieved by adjusting the progress tracking method according to the learner's learning environment.

[0057] The reception unit can analyze the learner's social media activity and receive relevant questions. For example, it can receive relevant questions based on the content the learner has shared on social media. It can also suggest questions that the learner might be interested in based on the learner's social media activity. It can also receive relevant questions based on the activity of the learner's friends on social media. In this way, it is possible to receive highly relevant questions by analyzing the learner's social media activity.

[0058] The provision unit can select an appropriate provision method by referring to the learner's past question history. For example, it can preferentially use a provision method that the learner has previously preferred. It can also select the optimal provision method from the learner's past question history. It can also analyze the learner's past question history and select the most effective provision method. In this way, the optimal provision method can be selected by referring to the learner's past question history.

[0059] When assessing comprehension, the assessment unit can analyze changes in comprehension based on the time of submission by the learner. For example, changes in comprehension can be analyzed based on the time of submission by the learner. Trends in comprehension can also be analyzed from the time of submission by the learner. Changes in comprehension can also be evaluated by referring to the time of submission by the learner. In this way, trends in comprehension can be grasped by analyzing changes in comprehension based on the time of submission by the learner.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives questions entered by learners during lessons. For example, the reception unit can receive questions in text format, audio format, image format, etc. Step 2: The generation unit uses the generation AI to analyze the question received by the reception unit and generate an appropriate answer. For example, the generation unit analyzes the question using natural language processing technology or a machine learning algorithm and generates an answer. The generation unit inputs a prompt to the generation AI saying, "Please generate an answer to this question," and the generation AI generates an answer. Step 3: The providing unit provides the answer generated by the generating unit to the learner. For example, the providing unit can provide the answer to the learner in a text format, an audio format, an image format, or the like. Step 4: The tracking unit tracks the progress of the learner based on the answers provided by the providing unit. For example, the tracking unit records what questions the learner asked and what answers they received, and evaluates the learner's progress. Step 5: The assessor evaluates the learner's level of understanding based on the progress tracked by the tracker. For example, the assessor evaluates the learner's level of understanding based on test results, feedback, etc.

[0062] (Example 2) An educational program creation system according to an embodiment of the present invention uses a generative AI to create educational programs tailored to the needs of individual learners. This system provides an AI-based question-and-answer service for lessons. In Japan, many students find it difficult to ask questions to teachers during class due to embarrassment or other reasons, but they can consult with the AI ​​without hesitation. In the future, we envision tracking learners' progress and comprehension levels and preparing learning materials tailored to their abilities. For example, a learner inputs a question during class. The generative AI then analyzes the question and generates an appropriate answer. The generated answer is provided to the learner. This allows learners to easily ask questions during class and deepen their understanding. Furthermore, in the future, we will add a function to track learners' progress and comprehension levels. For example, we will record the questions asked by the learner and the answers received, and evaluate the learner's level of understanding. Based on this, we can provide optimal learning materials to the learner. For example, if a learner's understanding is lacking in a particular area, we will provide specialized materials for that area. In this way, personalized education tailored to each learner is realized. This allows the educational program creation system to meet learners' needs and support effective learning. For example, learners can progress at their own pace and deepen their understanding. It also makes it easier for teachers to understand the progress of students, enabling them to provide more effective instruction. For example, they can identify areas in which a particular student is struggling and provide appropriate support to that student.

[0063] An educational program creation system according to an embodiment includes a receiving unit, a generating unit, a providing unit, a tracking unit, and an evaluating unit. The receiving unit receives questions entered by a learner during class. For example, the receiving unit can receive questions in text, audio, image, or other formats. The generating unit uses a generation AI to analyze the questions received by the receiving unit and generate an appropriate answer. For example, the generating unit analyzes the questions and generates answers using natural language processing technology or a machine learning algorithm. The generating unit inputs a prompt to the generation AI, such as "Please generate an answer to this question," and the generation AI generates an answer. The providing unit provides the answers generated by the generating unit to the learner. For example, the providing unit can provide answers to the learner in text, audio, image, or other formats. The tracking unit tracks the learner's progress based on the answers provided by the providing unit. For example, the tracking unit records what questions the learner asked and what answers they received, and evaluates the learner's progress. The evaluating unit evaluates the learner's level of understanding based on the progress tracked by the tracking unit. For example, the evaluation unit evaluates the learner's level of understanding based on test results, feedback, etc. As a result, the educational program creation system according to the embodiment can provide appropriate answers to learners' questions, track and evaluate their progress and level of understanding, and provide an educational program tailored to each individual learner.

[0064] The reception unit can estimate the learner's emotions and adjust the timing of accepting questions based on the estimated learner's emotions. For example, if the learner is nervous, the reception unit waits until the learner relaxes before accepting questions. If the learner is concentrating, the reception unit can temporarily delay accepting questions so as not to disturb the learner's concentration. If the learner is tired, the reception unit can accept questions after a break. This allows questions to be accepted at a more appropriate time by adjusting the timing of accepting 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-mentioned processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the learner's facial expression data into the generation AI and have the generation AI execute emotion estimation.

[0065] The reception unit can analyze the learner's past question history and select the optimal reception method. For example, the reception unit preferentially suggests question formats that the learner has frequently used in the past. The reception unit can also automatically suggest related questions based on the content of questions asked by the learner in the past. The reception unit can also select the most effective question reception method from the learner's past question history. In this way, by analyzing the past question history, it is possible to provide the learner with the optimal question reception method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the learner's past question history data into the generation AI and have the generation AI select the optimal reception method.

[0066] When receiving questions, the reception unit can filter the questions based on the learner's current learning situation and areas of interest. For example, the reception unit can only accept questions related to the topic the learner is currently studying. The reception unit can also preferentially accept questions that are highly relevant based on the learner's areas of interest. The reception unit can also filter questions of an appropriate level of difficulty according to the learner's progress. In this way, by filtering questions based on the learner's current learning situation and areas of interest, it is possible to preferentially accept questions that are highly relevant. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the learner's learning situation data to a generation AI and have the generation AI perform question filtering.

[0067] When receiving a question, the reception unit can select an appropriate reception means depending on the learner's input method. For example, if the learner uses voice input, the reception unit can receive the question using voice recognition technology. If the learner uses text input, the reception unit can also receive the question using text analysis technology. If the learner uses an image to ask a question, the reception unit can also receive the question using image recognition technology. This allows questions to be received smoothly by selecting the optimal reception means depending on the learner's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the learner's input data into a generation AI and have the generation AI select the optimal reception means.

[0068] The reception unit can estimate the learner's emotions and determine the priority of questions to be received based on the estimated learner's emotions. For example, if the learner is feeling anxious, the reception unit can prioritize receiving that question. If the learner is excited, the reception unit can postpone the question until the learner has calmed down. If the learner is relaxed, the reception unit can also receive the question with normal priority. This allows questions to be received in a more appropriate order by determining the priority of 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-mentioned processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the learner's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0069] When receiving questions, the reception unit can prioritize receiving highly relevant questions by taking into account the learner's geographical location information. For example, if the learner is in a specific area, the reception unit can prioritize receiving questions related to that area. If the learner is traveling, the reception unit can also prioritize receiving questions related to the travel destination. If the learner is at home, the reception unit can also prioritize receiving questions related to studying at home. In this way, by taking the learner's geographical location information into account, highly relevant questions can be prioritized. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the learner's geographical location information data to the generation AI and cause the generation AI to select highly relevant questions.

[0070] When receiving a question, the reception unit can analyze the learner's social media activity and receive related questions. For example, the reception unit can receive related questions based on content shared by the learner on social media. The reception unit can also suggest questions that the learner is likely to be interested in based on the learner's social media activity. The reception unit can also receive related questions by referring to the activity of the learner's friends on social media. In this way, by analyzing the learner's social media activity, highly relevant questions can be received. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the learner's social media data into a generation AI and cause the generation AI to select related questions.

[0071] When accepting a question, the acceptance unit can customize the acceptance method by reflecting the learner's past feedback. For example, the acceptance unit preferentially suggests question formats that the learner has previously preferred. The acceptance unit can also select the optimal question acceptance method based on the learner's past feedback. The acceptance unit can also customize the question acceptance interface by referring to the learner's past feedback. In this way, the optimal question acceptance method can be provided by reflecting the learner's past feedback. Some or all of the above-mentioned processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the learner's past feedback data into the generation AI and have the generation AI customize the acceptance method.

[0072] The generation unit can estimate the learner's emotions and adjust the way the answer is expressed based on the estimated learner's emotions. For example, if the learner is feeling anxious, the generation unit can generate an answer using gentle language. If the learner is excited, the generation unit can also generate an answer in a calm tone. If the learner is relaxed, the generation unit can also generate an answer in a normal tone. This allows for providing a more appropriate answer by adjusting the way the answer is expressed depending on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the learner's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0073] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. For example, the generation unit generates a detailed answer for an important question. The generation unit can also generate a concise answer for a general question. The generation unit can also adjust the level of detail of the answer according to the learner's level of understanding. In this way, by adjusting the level of detail of the answer based on the importance of the question, it is possible to provide an answer with an appropriate level of detail. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0074] When generating an answer, the generation unit can apply different generation algorithms depending on the question category. For example, for a math question, the generation unit can apply an algorithm that generates an answer that includes a mathematical formula. For a history question, the generation unit can also apply an algorithm that generates an answer that includes dates and events. For a science question, the generation unit can also apply an algorithm that generates an answer that includes experimental results and theories. In this way, by applying different generation algorithms depending on the question category, more appropriate answers can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question category data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0075] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the learner's past answer results. The generation unit generates a more accurate answer, for example, based on answers received by the learner in the past. The generation unit can also add supplementary information to avoid misunderstandings from the learner's past answer results. The generation unit can also analyze the learner's past answer results and generate an optimal answer. In this way, the accuracy of the answer can be improved by referring to the learner's past answer results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the learner's past answer result data into the generation AI and cause the generation AI to improve the accuracy of the answer.

[0076] The providing unit can estimate the learner's emotions and adjust the way in which the answer is provided based on the estimated learner's emotions. For example, if the learner is feeling anxious, the providing unit can provide the answer in a gentle manner. If the learner is excited, the providing unit can also provide the answer in a calm tone. If the learner is relaxed, the providing unit can also provide the answer in a normal tone. This allows the answer to be provided in a more appropriate manner by adjusting the way in which the answer is provided 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 providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the learner's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0077] When providing an answer, the providing unit can select an appropriate providing method by referring to the learner's past question history. For example, the providing unit preferentially uses a providing method that the learner has previously preferred. The providing unit can also select the optimal providing method from the learner's past question history. The providing unit can also analyze the learner's past question history and select the most effective providing method. In this way, the optimal providing method can be selected by referring to the learner's past question history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the learner's past question history data into the generation AI and have the generation AI select the providing method.

[0078] When providing an answer, the providing unit can customize the provided content based on the learner's current learning situation. The providing unit, for example, provides an answer related to a topic the learner is currently learning. The providing unit can also provide an answer of an appropriate level of difficulty depending on the learner's progress. The providing unit can also provide highly relevant answers based on the learner's areas of interest. In this way, by customizing the provided content based on the learner's current learning situation, more appropriate answers can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the learner's learning situation data into a generation AI and cause the generation AI to customize the provided content.

[0079] The providing unit can improve the answer providing method by reflecting the learner's feedback when providing an answer. The providing unit, for example, customizes the answer providing method based on the learner's feedback. The providing unit can also select the optimal answer providing method by referring to the learner's feedback. The providing unit can also analyze the learner's feedback and improve the answer providing method. In this way, the answer providing method can be improved by reflecting the learner's feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the learner's feedback data into the generating AI and cause the generating AI to improve the answer providing method.

[0080] The tracking unit can estimate the learner's emotions and adjust the progress tracking method based on the estimated learner's emotions. For example, if the learner is feeling anxious, the tracking unit can track the learner's progress more gently. If the learner is excited, the tracking unit can also track the learner's progress more precisely. If the learner is relaxed, the tracking unit can apply a normal progress tracking method. This allows the progress to be tracked in a more appropriate manner by adjusting the progress tracking 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 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 tracking unit can be performed using AI, for example, or without AI. For example, the tracking unit can input the learner's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0081] When tracking progress, the tracking unit can evaluate current progress by referring to past tracking data. The tracking unit evaluates current progress based on, for example, the learner's past progress data. The tracking unit can also analyze progress trends from the learner's past tracking data. The tracking unit can also compare and evaluate current progress by referring to the learner's past tracking data. This allows current progress to be accurately evaluated by referring to the past tracking data. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the learner's past tracking data into the generation AI and have the generation AI evaluate current progress.

[0082] The tracking unit can apply different tracking methods to different learner categories when tracking progress. For example, the tracking unit applies a basic progress tracking method to beginner learners. The tracking unit can also apply a detailed progress tracking method to intermediate learners. The tracking unit can also apply a specialized progress tracking method to advanced learners. In this way, applying different tracking methods to different learner categories enables more appropriate progress tracking. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input learner category data to the generation AI and cause the generation AI to apply an appropriate tracking method.

[0083] The tracking unit can track progress while taking into account the learner's attribute information. The tracking unit selects an appropriate progress tracking method, for example, depending on the learner's age. The tracking unit can also apply an optimal progress tracking method depending on the learner's learning style. The tracking unit can also customize the progress tracking method based on the learner's learning history. This enables more appropriate progress tracking by taking the learner's attribute information into consideration. Some or all of the above-mentioned processing in the tracking unit can be performed, for example, using AI, or can be performed without using AI. For example, the tracking unit can input the learner's attribute information data into the generation AI and cause the generation AI to select a tracking method.

[0084] The tracking unit can estimate the learner's emotions and adjust the importance of progress based on the estimated learner's emotions. For example, if the learner is feeling anxious, the tracking unit prioritizes tracking the learner's progress. If the learner is excited, the tracking unit can postpone the learner's progress until the learner has calmed down. If the learner is relaxed, the tracking unit can track the learner's progress with normal importance. This allows for more appropriate progress tracking by adjusting the importance of progress 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-mentioned processing in the tracking unit can be performed using AI, for example, or without AI. For example, the tracking unit can input the learner's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0085] When tracking progress, the tracking unit can analyze changes in progress based on the learner's submission time. The tracking unit analyzes changes in progress based on, for example, the time of assignment submission by the learner. The tracking unit can also analyze progress trends from the learner's submission time. The tracking unit can also evaluate changes in progress by referring to the learner's submission time. In this way, progress trends can be grasped by analyzing changes in progress based on the learner's submission time. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input data on the learner's submission time into the generation AI and cause the generation AI to analyze changes in progress.

[0086] When tracking progress, the tracking unit can analyze the progress by referring to the learner's related data. The tracking unit, for example, analyzes the progress based on the learner's related data. The tracking unit can also analyze the progress trend from the learner's related data. The tracking unit can also evaluate the progress by referring to the learner's related data. In this way, the progress trend can be grasped by referring to the learner's related data. Some or all of the above-mentioned processing in the tracking unit may be performed using AI, for example, or may be performed without using AI. For example, the tracking unit can input the learner's related data into the generation AI and cause the generation AI to perform an analysis of the progress.

[0087] The tracking unit can track progress while taking into account the learner's learning style. For example, the tracking unit selects an appropriate progress tracking method according to the learner's learning style. The tracking unit can also apply an optimal progress tracking method based on the learner's learning style. The tracking unit can also customize the progress tracking method by taking into account the learner's learning style. This enables more appropriate progress tracking by taking the learner's learning style into consideration. Some or all of the above-mentioned processing in the tracking unit can be performed using, for example, AI, or can be performed without using AI. For example, the tracking unit can input the learner's learning style data into the generation AI and cause the generation AI to select a tracking method.

[0088] The evaluation unit can estimate the learner's emotions and adjust the method of assessing comprehension based on the estimated learner's emotions. For example, if the learner is feeling anxious, the evaluation unit can assess the learner's comprehension using gentle language. If the learner is excited, the evaluation unit can assess the learner's comprehension using a calm tone. If the learner is relaxed, the evaluation unit can assess the learner's comprehension using a normal tone. This allows for more appropriate assessment by adjusting the method of assessing comprehension 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-mentioned processing in the evaluation unit can be performed using AI, for example, or without AI. For example, the evaluation unit can input the learner's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0089] When assessing the level of understanding, the evaluation unit can evaluate the current level of understanding by referring to past evaluation data. The evaluation unit evaluates the current level of understanding based on, for example, the learner's past evaluation data. The evaluation unit can also analyze trends in the level of understanding from the learner's past evaluation data. The evaluation unit can also compare and evaluate the current level of understanding by referring to the learner's past evaluation data. This makes it possible to accurately evaluate the current level of understanding by referring to the past evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the learner's past evaluation data into the generation AI and have the generation AI evaluate the current level of understanding.

[0090] The evaluation unit can apply different evaluation methods to different learner categories when evaluating comprehension. For example, the evaluation unit applies a basic comprehension evaluation method to beginner learners. The evaluation unit can also apply a detailed comprehension evaluation method to intermediate learners. The evaluation unit can also apply a specialized comprehension evaluation method to advanced learners. In this way, applying different evaluation methods to different learner categories enables more appropriate comprehension evaluation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input learner category data into the generation AI and cause the generation AI to apply an appropriate evaluation method.

[0091] The evaluation unit can perform comprehension assessment by taking into account the learner's attribute information. The evaluation unit selects an appropriate comprehension assessment method, for example, depending on the learner's age. The evaluation unit can also apply the optimal comprehension assessment method depending on the learner's learning style. The evaluation unit can also customize the comprehension assessment method based on the learner's learning history. This enables more appropriate comprehension assessment by taking into account the learner's attribute information. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the learner's attribute information data into the generation AI and have the generation AI select the assessment method.

[0092] The evaluation unit can estimate the learner's emotions and adjust the importance of the level of understanding based on the estimated learner's emotions. For example, if the learner is feeling anxious, the evaluation unit can prioritize the evaluation of the learner's level of understanding. If the learner is excited, the evaluation unit can postpone the evaluation of the learner's level of understanding until the learner has calmed down. If the learner is relaxed, the evaluation unit can also evaluate the learner's level of understanding with a normal level of importance. This allows for a more appropriate evaluation of the learner's level of understanding by adjusting the importance of the level of understanding 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-mentioned processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input the learner's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0093] During comprehension assessment, the evaluation unit can analyze changes in the learner's level of understanding based on the time of submission. The evaluation unit analyzes changes in the learner's level of understanding based on, for example, the time of submission of the assignment by the learner. The evaluation unit can also analyze trends in the learner's level of understanding from the time of submission. The evaluation unit can also evaluate changes in the learner's level of understanding by referring to the time of submission of the learner. In this way, trends in the learner's level of understanding can be grasped by analyzing changes in the learner's level of understanding based on the time of submission. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the learner's submission time into the generation AI and cause the generation AI to analyze changes in the learner's level of understanding.

[0094] The evaluation unit can analyze the level of comprehension by referring to the learner's related data when evaluating the level of comprehension. The evaluation unit, for example, analyzes the level of comprehension based on the learner's related data. The evaluation unit can also analyze the trend of the level of comprehension from the learner's related data. The evaluation unit can also evaluate the level of comprehension by referring to the learner's related data. In this way, the trend of the level of comprehension can be grasped by referring to the learner's related data. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the learner's related data into the generation AI and have the generation AI perform an analysis of the level of comprehension.

[0095] The evaluation unit can perform comprehension assessment taking into account the learner's learning style. For example, the evaluation unit selects an appropriate comprehension assessment method according to the learner's learning style. The evaluation unit can also apply the optimal comprehension assessment method based on the learner's learning style. The evaluation unit can also customize the comprehension assessment method taking into account the learner's learning style. This enables more appropriate comprehension assessment by taking the learner's learning style into consideration. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the learner's learning style data into the generation AI and have the generation AI select the assessment method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, provision unit, tracking unit, and evaluation 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 control unit 46A of the smart device 14 and receives a question entered by a learner. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question using a generation AI and generates an appropriate answer. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated answer to the learner. The tracking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and tracks the learner's progress based on the provided answer. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the learner's level of understanding. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, provision unit, tracking unit, and evaluation 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 control unit 46A of the smart glasses 214 and receives a question input by a learner. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question using a generation AI and generates an appropriate answer. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated answer to the learner. The tracking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and tracks the learner's progress based on the provided answer. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the learner's level of understanding. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, provision unit, tracking unit, and evaluation 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 control unit 46A of the headset-type terminal 314 and receives a question entered by a learner. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question using a generation AI and generates an appropriate answer. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the generated answer to the learner. The tracking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and tracks the learner's progress based on the provided answer. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the learner's level of understanding. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, tracking unit, and evaluation 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 control unit 46A of the robot 414 and receives a question input by a learner. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question using a generation AI and generates an appropriate answer. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated answer to the learner. The tracking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and tracks the learner's progress based on the provided answer. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the learner's level of understanding.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The reception unit can also acquire physiological data from the learner and adjust the timing of question reception. For example, it can monitor the learner's heart rate and electrodermal activity and accept questions when the learner is relaxed. If the learner is feeling stressed, it can wait until the learner is relaxed. Also, if the learner is concentrating, it can temporarily delay accepting questions so as not to disturb their concentration. In this way, by adjusting the timing of question reception according to the learner's physiological state, questions can be accepted at more appropriate times.

[0098] The generation unit can analyze the learner's past learning patterns and select the optimal answer format. For example, it can provide answer formats that the learner has preferred in the past with priority. It can also generate relevant answers based on answer formats that the learner has found easy to understand in the past. It can also select the most effective answer format from the learner's past learning patterns. In this way, it is possible to provide the learner with the optimal answer format by analyzing their past learning patterns.

[0099] The providing unit can estimate the learner's emotions and adjust the way in which the answer is provided based on the estimated learner's emotions. For example, if the learner is feeling anxious, the answer can be provided in gentle language. If the learner is excited, the answer can be provided in a calm tone. If the learner is relaxed, the answer can be provided in a normal tone. In this way, by adjusting the way in which the answer is provided according to the learner's emotions, the answer can be provided in a more appropriate manner.

[0100] The tracking unit can monitor the learner's learning environment and adjust the progress tracking method. For example, if the learner is studying in a quiet environment, detailed progress tracking can be performed. If the learner is studying in a noisy environment, progress tracking can be performed more slowly. If the learner is on the move, progress tracking can also be temporarily stopped. In this way, more appropriate progress tracking can be achieved by adjusting the progress tracking method according to the learner's learning environment.

[0101] The evaluation unit can estimate the learner's emotions and adjust the method of evaluating comprehension based on the estimated learner's emotions. For example, if the learner is feeling anxious, the level of comprehension can be evaluated using gentle language. If the learner is excited, the level of comprehension can be evaluated using a calm tone. If the learner is relaxed, the level of comprehension can be evaluated using a normal tone. This allows for more appropriate evaluation by adjusting the method of evaluating comprehension according to the learner's emotions.

[0102] The reception unit can analyze the learner's social media activity and receive relevant questions. For example, it can receive relevant questions based on the content the learner has shared on social media. It can also suggest questions that the learner might be interested in based on the learner's social media activity. It can also receive relevant questions based on the activity of the learner's friends on social media. In this way, it is possible to receive highly relevant questions by analyzing the learner's social media activity.

[0103] The generation unit can estimate the learner's emotions and adjust the way the answer is expressed based on the estimated learner's emotions. For example, if the learner is feeling anxious, the unit can generate an answer using gentle language. If the learner is excited, the unit can generate an answer in a calm tone. If the learner is relaxed, the unit can generate an answer in a normal tone. This makes it possible to provide more appropriate answers by adjusting the way the answer is expressed depending on the learner's emotions.

[0104] The provision unit can select an appropriate provision method by referring to the learner's past question history. For example, it can preferentially use a provision method that the learner has previously preferred. It can also select the optimal provision method from the learner's past question history. It can also analyze the learner's past question history and select the most effective provision method. In this way, the optimal provision method can be selected by referring to the learner's past question history.

[0105] The tracking unit can estimate the learner's emotions and adjust the progress tracking method based on the estimated learner's emotions. For example, if the learner is anxious, progress tracking can be performed gently. If the learner is excited, progress tracking can be performed in detail. If the learner is relaxed, a normal progress tracking method can be applied. In this way, progress can be tracked in a more appropriate manner by adjusting the progress tracking method according to the learner's emotions.

[0106] When assessing comprehension, the assessment unit can analyze changes in comprehension based on the time of submission by the learner. For example, changes in comprehension can be analyzed based on the time of submission by the learner. Trends in comprehension can also be analyzed from the time of submission by the learner. Changes in comprehension can also be evaluated by referring to the time of submission by the learner. In this way, trends in comprehension can be grasped by analyzing changes in comprehension based on the time of submission by the learner.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The reception unit receives questions entered by learners during lessons. For example, the reception unit can receive questions in text format, audio format, image format, etc. Step 2: The generation unit uses the generation AI to analyze the question received by the reception unit and generate an appropriate answer. For example, the generation unit analyzes the question using natural language processing technology or a machine learning algorithm and generates an answer. The generation unit inputs a prompt to the generation AI saying, "Please generate an answer to this question," and the generation AI generates an answer. Step 3: The providing unit provides the answer generated by the generating unit to the learner. For example, the providing unit can provide the answer to the learner in a text format, an audio format, an image format, or the like. Step 4: The tracking unit tracks the progress of the learner based on the answers provided by the providing unit. For example, the tracking unit records what questions the learner asked and what answers they received, and evaluates the learner's progress. Step 5: The assessor evaluates the learner's level of understanding based on the progress tracked by the tracker. For example, the assessor evaluates the learner's level of understanding based on test results, feedback, etc.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 reception section for accepting questions; a generation unit that analyzes the question received by the reception unit and generates an answer; a providing unit that provides the answer generated by the generating unit; a tracking unit that tracks the progress of the learner based on the answers provided by the providing unit; an evaluation unit that evaluates the learner's level of understanding based on the progress tracked by the tracking unit; Equipped with A system characterized by:

2. The reception unit Estimate the learner's emotions and adjust the timing of accepting questions based on the estimated emotions of the learner.

2. The system of claim 1.

3. The reception unit Analyze the learner's past question history and select the appropriate reception method 2. The system of claim 1.

4. The reception unit When accepting questions, filter them based on the learner's current learning status and areas of interest 2. The system of claim 1.

5. The reception unit When accepting questions, select the appropriate method of acceptance depending on the learner's input method.

2. The system of claim 1.

6. The reception unit Estimate the learner's emotions and prioritize the questions to be accepted based on the estimated learner's emotions.

2. The system of claim 1.

7. The reception unit When accepting questions, the system takes into account the learner's geographic location to prioritize relevant questions.

2. The system of claim 1.

8. The reception unit When receiving questions, analyze learners' social media activity and receive related questions.

2. The system of claim 1.

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