system

Abstract

The system according to the embodiment aims to provide an efficient learning experience by generating questions suited to individual learners and managing their progress. [Solution] A system according to an embodiment includes a receiving unit, a generating unit, a recording unit, and a reporting unit. The receiving unit inputs learning goals or current learning status. The generating unit analyzes the information input by the receiving unit and generates questions appropriate for each learner. The recording unit records the learner's progress based on the questions generated by the generating unit. The reporting unit reports the progress recorded by the recording unit to the teacher.

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 had the problem that it is difficult to generate questions suited to individual learners and to properly manage their progress, placing a heavy burden on teachers.

[0005] The system according to the embodiment aims to provide an efficient learning experience by generating questions suited to individual learners and managing their progress. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, a recording unit, and a reporting unit. The receiving unit inputs learning goals or current learning status. The generating unit analyzes the information input by the receiving unit and generates questions appropriate for each learner. The recording unit records the learner's progress based on the questions generated by the generating unit. The reporting unit reports the progress recorded by the recording unit to the teacher. [Effects of the Invention]

[0007] The system according to the embodiment can provide an efficient learning experience by generating questions suited to individual learners and managing their progress. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An online education system according to an embodiment of the present invention is equipped with a generative AI, which generates optimal problems for individual learners and provides support according to their progress. The online education system inputs learning goals and current learning status, and the generative AI analyzes this information to generate optimal problems for each learner. For example, when solving math problems, the generative AI automatically generates problems based on the learner's level of understanding. Furthermore, the system provides appropriate support according to the learner's progress. For example, if a learner struggles with a particular problem, the generative AI provides supplementary explanations and additional practice problems related to that problem. The online education system also automatically records the learner's progress and reports it to the teacher. This makes it easier for teachers to grasp the situation of each learner and enable effective instruction. Furthermore, the generative AI can provide an efficient learning experience even in large-scale educational environments. For example, even when a large number of learners are using the system simultaneously, the generative AI generates individual problems for each learner and provides support according to their progress. This allows the online education system to provide an efficient learning experience and expand the number of learners. This allows the online education system to realize a personalized learning experience and reduce the burden on learners and teachers.

[0029] An online education system according to an embodiment includes a receiving unit, a generating unit, a recording unit, and a reporting unit. The receiving unit inputs learning goals or current learning status. Learning goals include, but are not limited to, short-term goals, long-term goals, specific subjects or skills, etc. Current learning status includes, but is not limited to, test scores, progress, and comprehension. The generating unit uses a generation AI to analyze the information input by the receiving unit and generate questions appropriate for each learner. The generating unit adjusts the difficulty of the questions based on, for example, the learner's level of understanding. The generating unit can also provide relevant supplementary explanations or additional practice questions when a learner has difficulty with a particular question. Furthermore, the generating unit can generate individual questions for each learner, even in a large-scale educational environment. The recording unit records the learner's progress based on the questions generated by the generating unit. The progress includes, but is not limited to, achievement level, study time, and comprehension. The reporting unit reports the progress recorded by the recording unit to the teacher. The reporting unit can provide the learner's progress to the teacher in the form of a graph or report, for example. As a result, the online education system according to the embodiment can provide an efficient learning experience by generating questions that are optimal for each individual learner and recording and reporting their progress.

[0030] The generation unit can adjust the difficulty of questions according to the learner's level of understanding. The level of understanding includes, but is not limited to, test scores, quiz results, and self-assessment, for example. The generation unit can adjust the difficulty of questions based on the learner's test scores, for example. The generation unit can also adjust the difficulty of questions based on quiz results, and for example, based on self-assessment. This can support effective learning by providing questions according to the learner's level of understanding.

[0031] The generation unit can provide relevant supplementary explanations or additional practice questions when a learner has difficulty with a particular problem. Examples of when a learner has difficulty include, but are not limited to, the correct answer rate, answer time, and incorrect answer patterns for a particular problem. For example, the generation unit can provide relevant supplementary explanations when the correct answer rate for a particular problem is low. The generation unit can also provide relevant supplementary explanations when the answer time is long. The generation unit can also provide additional practice questions based on the incorrect answer patterns. This can help a learner continue their learning by providing appropriate support for the problem on which the learner has difficulty.

[0032] The reporting unit can provide the teacher with the learner's progress in the form of a graph or a report. Examples of graphs include, but are not limited to, bar graphs, line graphs, pie charts, etc. The reporting unit can, for example, display the learner's progress in a bar graph. The reporting unit can also display the learner's progress in a line graph. The reporting unit can also display the learner's progress in a pie chart. Examples of report formats include, but are not limited to, text reports, PDFs, slide formats, etc. The reporting unit can, for example, provide the learner's progress in a text report. The reporting unit can also provide the learner's progress in a PDF. The reporting unit can also provide the learner's progress in a slide format. This makes it easier for the teacher to visually grasp the learner's progress.

[0033] The generator can generate individual questions for each learner even in a wide range of educational environments. Examples of wide range of educational environments include, but are not limited to, online education, multiple schools, and different grade levels. For example, the generator can generate individual questions for each learner in an online educational environment. The generator can also generate individual questions for each learner in multiple schools. The generator can also generate individual questions for learners in different grade levels. This makes it possible to provide an individual learning experience even in a large-scale educational environment.

[0034] The generation unit can use an algorithm to evaluate the level of comprehension based on the learner's past answer history and answer time. The answer history includes, for example, past test results, quiz answers, and practice question history, but is not limited to these examples. The answer time includes, for example, the answer time for each question, the average answer time, and the longest answer time, but is not limited to these examples. The generation unit can use, for example, an algorithm to evaluate the level of comprehension based on past test results. The generation unit can also use an algorithm to evaluate the level of comprehension based on quiz answers. The generation unit can also use an algorithm to evaluate the level of comprehension based on the practice question history. This makes it possible to utilize the learner's past data to perform a more accurate comprehension evaluation.

[0035] When inputting a learning goal or current learning situation, the reception unit can automatically complete the input content by referring to the user's past learning history. Examples of automatic completion include, but are not limited to, past input history, prediction algorithms, and user profiles. For example, the reception unit can automatically display learning goals previously set by the user as candidates. The reception unit can also suggest related learning goals based on the user's past learning history. The reception unit can also automatically complete the current learning situation based on learning situations previously input by the user. This can utilize the past learning history to reduce the effort required for input and support efficient input.

[0036] When inputting a learning goal, the reception unit can customize the input content based on the user's current living situation or areas of interest. Examples of living situations include, but are not limited to, daily schedules, home environment, and health status. Examples of areas of interest include, but are not limited to, hobbies, subjects of interest, and future goals. The reception unit, for example, suggests appropriate learning goals based on the user's current living situation (work, family, etc.). The reception unit can also suggest related learning goals based on the user's areas of interest (hobbies, interests, etc.). The reception unit can also customize the period for achieving the learning goal to match the user's daily rhythm. This can support more appropriate learning plans by suggesting learning goals that match the user's living situation and areas of interest.

[0037] When inputting a learning goal, the reception unit can select an appropriate input means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user desires voice input, the reception unit can input the learning goal using voice recognition technology. Furthermore, if the user desires text input, the reception unit can also support keyboard input. Furthermore, if the user desires image input, the reception unit can also input the learning goal using image recognition technology. This makes it possible to support efficient input by providing the optimal means depending on the user's input method.

[0038] When inputting learning goals, the reception unit can prioritize inputting highly relevant goals by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. For example, if the user lives in a specific area, the reception unit can suggest learning goals related to that area. Furthermore, if the user attends a specific school or workplace, the reception unit can also suggest learning goals related to that location. Furthermore, if the user is traveling, the reception unit can also suggest learning goals related to the user's travel destination. This can support more appropriate learning plans by providing learning goals based on the user's geographical location information.

[0039] When a user inputs a learning goal, the reception unit can analyze the user's social media activity and suggest related goals. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The reception unit can suggest learning goals based on topics the user has shown interest in on social media, for example. The reception unit can also analyze the user's social media activity history and suggest related learning goals. The reception unit can also suggest related learning goals by referring to learning goals set by the user's friends. This can support a more appropriate learning plan by providing learning goals based on the user's social media activity.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting a learning goal. Examples of feedback include, but are not limited to, user comments, ratings, and survey results. The reception unit can improve the input method, for example, based on the user's past feedback. The reception unit can also suggest an optimal input method based on the user's past feedback. The reception unit can also avoid input methods that the user has previously expressed dissatisfaction with and provide a more appropriate method. By providing an input method based on the user's past feedback, efficient input can be supported.

[0041] When generating questions, the generation unit can adjust the level of detail of the questions based on the learner's level of understanding. Examples of the level of detail include, but are not limited to, the length of the question explanation, the number of specific examples, and the amount of supplementary information. For example, the generation unit generates detailed questions when the learner has a high level of understanding. Furthermore, the generation unit can also generate basic questions when the learner has a low level of understanding. Furthermore, the generation unit can gradually adjust the difficulty of the questions according to the learner's level of understanding. This can support effective learning by providing questions that match the learner's level of understanding.

[0042] When generating questions, the generation unit can apply different generation algorithms depending on the learner's category. Examples of categories include, but are not limited to, grade level, subject, and skill level. For example, the generation unit applies an algorithm for generating questions for beginners. The generation unit can also apply an algorithm for generating questions for intermediate learners. The generation unit can also apply an algorithm for generating questions for advanced learners. This makes it possible to support effective learning by providing questions according to the learner's category.

[0043] When generating questions, the generation unit can improve the accuracy of the questions by referring to the learner's past answer results. Examples of answer results include, but are not limited to, the correct answer rate, answer time, and incorrect answer patterns. For example, the generation unit analyzes the learner's past answer results and generates questions according to the learner's level of understanding. The generation unit can also generate similar questions based on questions that the learner has previously answered incorrectly. The generation unit can also suggest optimal questions based on the learner's past answer results. This makes it possible to provide more accurate questions by utilizing the learner's past answer results.

[0044] When generating questions, the generation unit can determine the priority of questions based on the time of submission by the learner. The submission time includes, but is not limited to, for example, a submission deadline, a frequency of submission, and a timing of submission. For example, the generation unit can prioritize generating questions with an approaching submission deadline. The generation unit can also prioritize generating questions that are submitted early by the learner. The generation unit can also adjust the priority of questions depending on the time of submission. This can support efficient learning by providing questions according to the time of submission by the learner.

[0045] When generating questions, the generation unit can adjust the order of questions based on the learner's relevance. Relevance includes, but is not limited to, relevance to the learning content, relevance of the questions, and relevance to the learner's interests. For example, the generation unit preferentially generates questions related to the learner's current learning content. The generation unit can also preferentially generate questions related to the learner's past learning content. The generation unit can also adjust the order of questions based on the learner's interests and concerns. This can support efficient learning by providing an order of questions according to the learner's relevance.

[0046] When generating questions, the generation unit can adjust the use of technical terminology in the questions according to the learner's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, the generation unit can generate questions with less technical terminology for beginners. Furthermore, the generation unit can also generate questions with an appropriate amount of technical terminology for intermediate learners. Furthermore, the generation unit can generate questions with a heavy use of technical terminology for advanced learners. This can support effective learning by providing questions according to the learner's level of expertise.

[0047] When recording progress status, the recording unit can automatically complete the recorded content by referring to the learner's past learning history. Examples of automatic completion include, but are not limited to, past input history, prediction algorithms, and user profiles. The recording unit automatically completes the progress status based on, for example, the learner's past learning history. The recording unit can also record progress status based on goals the learner has achieved in the past. The recording unit can also automatically record related progress status from the learner's past learning history. This makes it possible to utilize past learning history to reduce the effort required for recording and support efficient recording.

[0048] When recording progress, the recording unit can customize the recording content based on the learner's current living situation and areas of interest. Examples of living situation include, but are not limited to, daily schedules, home environment, and health status. Examples of areas of interest include, but are not limited to, hobbies, subjects of interest, and future goals. The recording unit records progress based on, for example, the learner's current living situation (work, family, etc.). The recording unit can also record progress based on the learner's areas of interest (hobbies, interests, etc.). The recording unit can also record progress in accordance with the learner's daily rhythm. This allows for more appropriate recording by providing recording content that is appropriate to the learner's living situation and areas of interest.

[0049] When recording progress, the recording unit can select the optimal recording means depending on the learner's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the learner prefers voice input, the recording unit records the progress using voice recognition technology. Furthermore, if the learner prefers text input, the recording unit can also support keyboard input. Furthermore, if the learner prefers image input, the recording unit can also record the progress using image recognition technology. This makes it possible to support efficient recording by providing the optimal recording means depending on the learner's input method.

[0050] When recording progress, the recording unit can prioritize relevant records by taking into account the learner's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, if the learner lives in a specific area, the recording unit records progress related to that area. Also, if the learner attends a specific school or workplace, the recording unit can record progress related to that location. Also, if the learner is traveling, the recording unit can record progress related to the learner's travel destination. This can support more appropriate recording by providing records based on the learner's geographical location information.

[0051] The recording unit may analyze the learner's social media activity and record the related information when recording the progress. Social media activity may include, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The recording unit may record the progress based on topics the learner has shown interest in on social media. The recording unit may also analyze the learner's social media activity history and record the related progress. The recording unit may also record the related progress with reference to the learning goals set by the learner's friends. This allows for more appropriate recording by providing records based on the learner's social media activity.

[0052] When recording progress, the recording unit can customize the recording method by reflecting the learner's past feedback. Feedback includes, but is not limited to, user comments, ratings, and survey results. For example, the recording unit improves the recording method based on feedback previously provided by the learner. The recording unit can also suggest an optimal recording method based on the learner's past feedback. The recording unit can also avoid recording methods that the learner has previously expressed dissatisfaction with and provide a more appropriate method. This makes it possible to support efficient recording by providing a recording method based on the learner's past feedback.

[0053] When making a report, the reporting unit can adjust the level of detail of the report based on the learner's progress. Examples of the level of detail include, but are not limited to, the length of the report's explanation, the number of specific examples, and the amount of supplementary information. For example, the reporting unit provides a detailed report when the learner's progress is good. Furthermore, the reporting unit can provide a report that focuses on the main points when the learner's progress is slow. Furthermore, the reporting unit can gradually adjust the level of detail of the report depending on the learner's progress. This makes it possible to support efficient reporting by providing reports that are tailored to the learner's progress.

[0054] When reporting, the reporting unit can apply different reporting algorithms depending on the learner's category. Examples of categories include, but are not limited to, grade, subject, and skill level. For example, the reporting unit applies an algorithm for generating a report for beginners. The reporting unit can also apply an algorithm for generating a report for intermediate learners. The reporting unit can also apply an algorithm for generating a report for advanced learners. This makes it possible to provide reports according to the learner's category, thereby supporting efficient reporting.

[0055] When making a report, the reporting unit can improve the accuracy of the report by referring to the learner's past report results. Report results include, but are not limited to, for example, the correct answer rate, answer time, and incorrect answer patterns. For example, the reporting unit analyzes the learner's past report results and generates a report according to the learner's level of understanding. The reporting unit can also generate a similar report based on questions that the learner got wrong in the past. The reporting unit can also suggest an optimal report based on the learner's past report results. This makes it possible to provide a more accurate report by utilizing the learner's past report results.

[0056] When a report is submitted, the reporting unit can determine the priority of the report based on the time of submission by the learner. The submission time includes, but is not limited to, for example, a submission deadline, a frequency of submission, and a timing of submission. For example, the reporting unit generates reports with a deadline approaching. The reporting unit can also generate reports with a priority of early submission by the learner. The reporting unit can also adjust the priority of the reports according to the time of submission. This makes it possible to support efficient reporting by providing reports according to the time of submission by the learner.

[0057] The reporting unit can adjust the order of reports based on the relevance of the learner when making a report. Relevance includes, but is not limited to, for example, relevance of the learning content, relevance of the report, and relevance of the learner's interests. For example, the reporting unit prioritizes generating reports related to the learner's current learning content. The reporting unit can also prioritize generating reports related to the learner's past learning content. The reporting unit can also adjust the order of reports based on the learner's interests and concerns. This can support efficient reporting by providing an order of reports according to the learner's relevance.

[0058] When making a report, the reporting unit can adjust the use of technical terms in the report according to the learner's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, the reporting unit can generate a report with fewer technical terms for beginners. Furthermore, the reporting unit can generate a report with an appropriate amount of technical terms for intermediate learners. Furthermore, the reporting unit can generate a report with a heavy use of technical terms for advanced learners. This allows for efficient reporting by providing a report according to the learner's level of expertise.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can estimate the user's learning style and adjust the input method for learning goals based on the estimated learning style. Learning styles include, but are not limited to, visual, auditory, and experiential learning. For example, if the user has a visual learning style, the reception unit provides a graphical interface and prioritizes visual input means. Furthermore, if the user has an auditory learning style, the reception unit can provide voice input or voice guidance. Furthermore, if the user has an experiential learning style, the reception unit can provide interactive input means. This can support efficient input by providing an input method according to the user's learning style.

[0061] The generation unit can customize the theme of the questions based on the learner's interests and concerns. Examples of interests and concerns include, but are not limited to, sports, music, science, etc. For example, if the learner is interested in sports, the generation unit can generate sports-related questions. Furthermore, if the learner is interested in music, the generation unit can generate music-related questions. Furthermore, if the learner is interested in science, the generation unit can generate science-related questions. This can improve the learner's motivation by providing questions that match the learner's interests and concerns.

[0062] The recording unit can monitor the learner's progress in real time and issue an alert if an abnormality is detected. Abnormalities include, but are not limited to, a sudden decrease in study time, a decrease in comprehension, and a stagnation of progress. For example, the recording unit issues an alert if there is a sudden decrease in study time. The recording unit can also issue an alert if there is a decrease in comprehension. The recording unit can also issue an alert if progress stagnates. This makes it possible to grasp the learner's progress in real time and prompt appropriate action.

[0063] The generation unit can predict future learning performance based on the learner's past learning data and propose an appropriate learning plan. Learning data includes, but is not limited to, past test results, study time, and level of understanding, for example. The generation unit can predict future learning performance based on, for example, past test results. The generation unit can also predict future learning performance based on study time. The generation unit can also predict future learning performance based on level of understanding. This makes it possible to propose a more effective learning plan by utilizing the learner's past data.

[0064] The generator can adjust the format of the questions based on the learner's learning environment. Examples of learning environments include, but are not limited to, online, offline, and hybrid. For example, in an online environment, the generator can generate interactive questions. In an offline environment, the generator can also generate printable questions. In a hybrid environment, the generator can also generate questions that can be used both online and offline. This can support efficient learning by providing questions that are appropriate for the learner's learning environment.

[0065] The generation unit can adjust the frequency of questions based on the learner's learning pace. Examples of learning pace include, but are not limited to, fast, normal, and slow. For example, if the learner's learning pace is fast, the generation unit can frequently present questions. Also, if the learner's learning pace is normal, the generation unit can present questions at a moderate frequency. Also, if the learner's learning pace is slow, the generation unit can present questions at a slow frequency. This can support efficient learning by providing questions at a frequency that matches the learner's learning pace.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The receptionist inputs learning goals or current learning status. Learning goals include short-term goals, long-term goals, specific subjects or skills, etc. Current learning status includes test scores, progress, level of understanding, etc. Step 2: The generator analyzes the information entered by the reception unit and generates questions appropriate for each individual learner. Using AI generation, the generator can adjust the difficulty of the questions according to the learner's level of understanding and provide relevant supplementary explanations or additional practice questions if the learner has difficulty with a particular question. It can also generate individual questions for each learner, even in large-scale educational environments. Step 3: The recorder records the learner's progress based on the questions generated by the generator, including the level of achievement, study time, and understanding. Step 4: The reporting unit reports the progress recorded by the recording unit to the teacher. The reporting unit can provide the teacher with the progress of the learner in the form of a graph or report.

[0068] (Example 2) An online education system according to an embodiment of the present invention is equipped with a generative AI, which generates optimal problems for individual learners and provides support according to their progress. The online education system inputs learning goals and current learning status, and the generative AI analyzes this information to generate optimal problems for each learner. For example, when solving math problems, the generative AI automatically generates problems based on the learner's level of understanding. Furthermore, the system provides appropriate support according to the learner's progress. For example, if a learner struggles with a particular problem, the generative AI provides supplementary explanations and additional practice problems related to that problem. The online education system also automatically records the learner's progress and reports it to the teacher. This makes it easier for teachers to grasp the situation of each learner and enable effective instruction. Furthermore, the generative AI can provide an efficient learning experience even in large-scale educational environments. For example, even when a large number of learners are using the system simultaneously, the generative AI generates individual problems for each learner and provides support according to their progress. This allows the online education system to provide an efficient learning experience and expand the number of learners. This allows the online education system to realize a personalized learning experience and reduce the burden on learners and teachers.

[0069] An online education system according to an embodiment includes a receiving unit, a generating unit, a recording unit, and a reporting unit. The receiving unit inputs learning goals or current learning status. Learning goals include, but are not limited to, short-term goals, long-term goals, specific subjects or skills, etc. Current learning status includes, but is not limited to, test scores, progress, and comprehension. The generating unit uses a generation AI to analyze the information input by the receiving unit and generate questions appropriate for each learner. The generating unit adjusts the difficulty of the questions based on, for example, the learner's level of understanding. The generating unit can also provide relevant supplementary explanations or additional practice questions when a learner has difficulty with a particular question. Furthermore, the generating unit can generate individual questions for each learner, even in a large-scale educational environment. The recording unit records the learner's progress based on the questions generated by the generating unit. The progress includes, but is not limited to, achievement level, study time, and comprehension. The reporting unit reports the progress recorded by the recording unit to the teacher. The reporting unit can provide the learner's progress to the teacher in the form of a graph or report, for example. As a result, the online education system according to the embodiment can provide an efficient learning experience by generating questions that are optimal for each individual learner and recording and reporting their progress.

[0070] The generation unit can adjust the difficulty of questions according to the learner's level of understanding. The level of understanding includes, but is not limited to, test scores, quiz results, and self-assessment, for example. The generation unit can adjust the difficulty of questions based on the learner's test scores, for example. The generation unit can also adjust the difficulty of questions based on quiz results, and for example, based on self-assessment. This can support effective learning by providing questions according to the learner's level of understanding.

[0071] The generation unit can provide relevant supplementary explanations or additional practice questions when a learner has difficulty with a particular problem. Examples of when a learner has difficulty include, but are not limited to, the correct answer rate, answer time, and incorrect answer patterns for a particular problem. For example, the generation unit can provide relevant supplementary explanations when the correct answer rate for a particular problem is low. The generation unit can also provide relevant supplementary explanations when the answer time is long. The generation unit can also provide additional practice questions based on the incorrect answer patterns. This can help a learner continue their learning by providing appropriate support for the problem on which the learner has difficulty.

[0072] The reporting unit can provide the teacher with the learner's progress in the form of a graph or a report. Examples of graphs include, but are not limited to, bar graphs, line graphs, pie charts, etc. The reporting unit can, for example, display the learner's progress in a bar graph. The reporting unit can also display the learner's progress in a line graph. The reporting unit can also display the learner's progress in a pie chart. Examples of report formats include, but are not limited to, text reports, PDFs, slide formats, etc. The reporting unit can, for example, provide the learner's progress in a text report. The reporting unit can also provide the learner's progress in a PDF. The reporting unit can also provide the learner's progress in a slide format. This makes it easier for the teacher to visually grasp the learner's progress.

[0073] The generator can generate individual questions for each learner even in a wide range of educational environments. Examples of wide range of educational environments include, but are not limited to, online education, multiple schools, and different grade levels. For example, the generator can generate individual questions for each learner in an online educational environment. The generator can also generate individual questions for each learner in multiple schools. The generator can also generate individual questions for learners in different grade levels. This makes it possible to provide an individual learning experience even in a large-scale educational environment.

[0074] The generation unit can use an algorithm to evaluate the level of comprehension based on the learner's past answer history and answer time. The answer history includes, for example, past test results, quiz answers, and practice question history, but is not limited to these examples. The answer time includes, for example, the answer time for each question, the average answer time, and the longest answer time, but is not limited to these examples. The generation unit can use, for example, an algorithm to evaluate the level of comprehension based on past test results. The generation unit can also use an algorithm to evaluate the level of comprehension based on quiz answers. The generation unit can also use an algorithm to evaluate the level of comprehension based on the practice question history. This makes it possible to utilize the learner's past data to perform a more accurate comprehension evaluation.

[0075] The reception unit can estimate the user's emotions and adjust the input method for learning goals based on the estimated user emotions. Examples of emotions include, but are not limited to, stress, relaxation, and hurry. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is feeling relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of learning goals. This provides an input method that corresponds to the user's emotions, thereby reducing stress and supporting efficient input. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] When inputting a learning goal or current learning situation, the reception unit can automatically complete the input content by referring to the user's past learning history. Examples of automatic completion include, but are not limited to, past input history, prediction algorithms, and user profiles. For example, the reception unit can automatically display learning goals previously set by the user as candidates. The reception unit can also suggest related learning goals based on the user's past learning history. The reception unit can also automatically complete the current learning situation based on learning situations previously input by the user. This can utilize the past learning history to reduce the effort required for input and support efficient input.

[0077] When inputting a learning goal, the reception unit can customize the input content based on the user's current living situation or areas of interest. Examples of living situations include, but are not limited to, daily schedules, home environment, and health status. Examples of areas of interest include, but are not limited to, hobbies, subjects of interest, and future goals. The reception unit, for example, suggests appropriate learning goals based on the user's current living situation (work, family, etc.). The reception unit can also suggest related learning goals based on the user's areas of interest (hobbies, interests, etc.). The reception unit can also customize the period for achieving the learning goal to match the user's daily rhythm. This can support more appropriate learning plans by suggesting learning goals that match the user's living situation and areas of interest.

[0078] When inputting a learning goal, the reception unit can select an appropriate input means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user desires voice input, the reception unit can input the learning goal using voice recognition technology. Furthermore, if the user desires text input, the reception unit can also support keyboard input. Furthermore, if the user desires image input, the reception unit can also input the learning goal using image recognition technology. This makes it possible to support efficient input by providing the optimal means depending on the user's input method.

[0079] The reception unit can estimate the user's emotion and prioritize input content based on the estimated user emotion. Emotions include, but are not limited to, stress, relaxation, and hurry. For example, when the user is feeling stressed, the reception unit can prioritize and display important input items. Furthermore, when the user is relaxed, the reception unit can display detailed input items. Furthermore, when the user is in a hurry, the reception unit can display the minimum number of input items. This allows for efficient input support by prioritizing input content according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] When inputting learning goals, the reception unit can prioritize inputting highly relevant goals by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, and location information services. For example, if the user lives in a specific area, the reception unit can suggest learning goals related to that area. Furthermore, if the user attends a specific school or workplace, the reception unit can also suggest learning goals related to that location. Furthermore, if the user is traveling, the reception unit can also suggest learning goals related to the user's travel destination. This can support more appropriate learning plans by providing learning goals based on the user's geographical location information.

[0081] When a user inputs a learning goal, the reception unit can analyze the user's social media activity and suggest related goals. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The reception unit can suggest learning goals based on topics the user has shown interest in on social media, for example. The reception unit can also analyze the user's social media activity history and suggest related learning goals. The reception unit can also suggest related learning goals by referring to learning goals set by the user's friends. This can support a more appropriate learning plan by providing learning goals based on the user's social media activity.

[0082] The reception unit can customize the input method by reflecting the user's past feedback when inputting a learning goal. Examples of feedback include, but are not limited to, user comments, ratings, and survey results. The reception unit can improve the input method, for example, based on the user's past feedback. The reception unit can also suggest an optimal input method based on the user's past feedback. The reception unit can also avoid input methods that the user has previously expressed dissatisfaction with and provide a more appropriate method. By providing an input method based on the user's past feedback, efficient input can be supported.

[0083] The generation unit can estimate the user's emotions and adjust the appropriate way of expressing questions based on the estimated user emotions. Examples of emotions include, but are not limited to, relaxation, stress, and excitement. For example, if the user is relaxed, the generation unit can generate visually appealing questions. Furthermore, if the user is stressed, the generation unit can generate simple and easy-to-understand questions. Furthermore, if the user is excited, the generation unit can generate challenging questions. This allows for efficient learning by providing a way of expressing questions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] When generating questions, the generation unit can adjust the level of detail of the questions based on the learner's level of understanding. Examples of the level of detail include, but are not limited to, the length of the question explanation, the number of specific examples, and the amount of supplementary information. For example, the generation unit generates detailed questions when the learner has a high level of understanding. Furthermore, the generation unit can also generate basic questions when the learner has a low level of understanding. Furthermore, the generation unit can gradually adjust the difficulty of the questions according to the learner's level of understanding. This can support effective learning by providing questions that match the learner's level of understanding.

[0085] When generating questions, the generation unit can apply different generation algorithms depending on the learner's category. Examples of categories include, but are not limited to, grade level, subject, and skill level. For example, the generation unit applies an algorithm for generating questions for beginners. The generation unit can also apply an algorithm for generating questions for intermediate learners. The generation unit can also apply an algorithm for generating questions for advanced learners. This makes it possible to support effective learning by providing questions according to the learner's category.

[0086] When generating questions, the generation unit can improve the accuracy of the questions by referring to the learner's past answer results. Examples of answer results include, but are not limited to, the correct answer rate, answer time, and incorrect answer patterns. For example, the generation unit analyzes the learner's past answer results and generates questions according to the learner's level of understanding. The generation unit can also generate similar questions based on questions that the learner has previously answered incorrectly. The generation unit can also suggest optimal questions based on the learner's past answer results. This makes it possible to provide more accurate questions by utilizing the learner's past answer results.

[0087] The generation unit can estimate the user's emotions and adjust the length of the questions based on the estimated user emotions. Examples of emotions include, but are not limited to, being in a hurry, being relaxed, or feeling stressed. For example, the generation unit can generate short questions when the user is in a hurry. The generation unit can also generate detailed questions when the user is relaxed. The generation unit can also generate simple questions when the user is feeling stressed. This can support efficient learning by providing questions of a length that corresponds to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] When generating questions, the generation unit can determine the priority of questions based on the time of submission by the learner. The submission time includes, but is not limited to, for example, a submission deadline, a frequency of submission, and a timing of submission. For example, the generation unit can prioritize generating questions with an approaching submission deadline. The generation unit can also prioritize generating questions that are submitted early by the learner. The generation unit can also adjust the priority of questions depending on the time of submission. This can support efficient learning by providing questions according to the time of submission by the learner.

[0089] When generating questions, the generation unit can adjust the order of questions based on the learner's relevance. Relevance includes, but is not limited to, relevance to the learning content, relevance of the questions, and relevance to the learner's interests. For example, the generation unit preferentially generates questions related to the learner's current learning content. The generation unit can also preferentially generate questions related to the learner's past learning content. The generation unit can also adjust the order of questions based on the learner's interests and concerns. This can support efficient learning by providing an order of questions according to the learner's relevance.

[0090] When generating questions, the generation unit can adjust the use of technical terminology in the questions according to the learner's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, the generation unit can generate questions with less technical terminology for beginners. Furthermore, the generation unit can also generate questions with an appropriate amount of technical terminology for intermediate learners. Furthermore, the generation unit can generate questions with a heavy use of technical terminology for advanced learners. This can support effective learning by providing questions according to the learner's level of expertise.

[0091] The recording unit can estimate the user's emotions and adjust the progress recording method based on the estimated user emotions. Emotions include, but are not limited to, for example, relaxed, stressed, and in a hurry. For example, when the user is relaxed, the recording unit can record detailed progress. Furthermore, when the user is stressed, the recording unit can record simple progress. Furthermore, when the user is in a hurry, the recording unit can record progress that focuses on the main points. This provides a progress recording method that corresponds to the user's emotions, thereby supporting efficient recording. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] When recording progress status, the recording unit can automatically complete the recorded content by referring to the learner's past learning history. Examples of automatic completion include, but are not limited to, past input history, prediction algorithms, and user profiles. The recording unit automatically completes the progress status based on, for example, the learner's past learning history. The recording unit can also record progress status based on goals the learner has achieved in the past. The recording unit can also automatically record related progress status from the learner's past learning history. This makes it possible to utilize past learning history to reduce the effort required for recording and support efficient recording.

[0093] When recording progress, the recording unit can customize the recording content based on the learner's current living situation and areas of interest. Examples of living situation include, but are not limited to, daily schedules, home environment, and health status. Examples of areas of interest include, but are not limited to, hobbies, subjects of interest, and future goals. The recording unit records progress based on, for example, the learner's current living situation (work, family, etc.). The recording unit can also record progress based on the learner's areas of interest (hobbies, interests, etc.). The recording unit can also record progress in accordance with the learner's daily rhythm. This allows for more appropriate recording by providing recording content that is appropriate to the learner's living situation and areas of interest.

[0094] When recording progress, the recording unit can select the optimal recording means depending on the learner's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the learner prefers voice input, the recording unit records the progress using voice recognition technology. Furthermore, if the learner prefers text input, the recording unit can also support keyboard input. Furthermore, if the learner prefers image input, the recording unit can also record the progress using image recognition technology. This makes it possible to support efficient recording by providing the optimal recording means depending on the learner's input method.

[0095] The recording unit can estimate the user's emotions and prioritize the recorded content based on the estimated user emotions. Emotions include, but are not limited to, stress, relaxation, and hurry. For example, when the user is feeling stressed, the recording unit prioritizes recording of important progress. Furthermore, when the user is relaxed, the recording unit can also record detailed progress. Furthermore, when the user is in a hurry, the recording unit can also record progress that focuses on the main points. This allows for efficient recording by prioritizing the recorded content according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] When recording progress, the recording unit can prioritize relevant records by taking into account the learner's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, if the learner lives in a specific area, the recording unit records progress related to that area. Also, if the learner attends a specific school or workplace, the recording unit can record progress related to that location. Also, if the learner is traveling, the recording unit can record progress related to the learner's travel destination. This can support more appropriate recording by providing records based on the learner's geographical location information.

[0097] The recording unit may analyze the learner's social media activity and record the related information when recording the progress. Social media activity may include, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The recording unit may record the progress based on topics the learner has shown interest in on social media. The recording unit may also analyze the learner's social media activity history and record the related progress. The recording unit may also record the related progress with reference to the learning goals set by the learner's friends. This allows for more appropriate recording by providing records based on the learner's social media activity.

[0098] When recording progress, the recording unit can customize the recording method by reflecting the learner's past feedback. Feedback includes, but is not limited to, user comments, ratings, and survey results. For example, the recording unit improves the recording method based on feedback previously provided by the learner. The recording unit can also suggest an optimal recording method based on the learner's past feedback. The recording unit can also avoid recording methods that the learner has previously expressed dissatisfaction with and provide a more appropriate method. This makes it possible to support efficient recording by providing a recording method based on the learner's past feedback.

[0099] The reporting unit can estimate the user's emotion and adjust the way the report is expressed based on the estimated user's emotion. Emotions include, but are not limited to, for example, relaxed, stressed, and in a hurry. For example, the reporting unit can provide a detailed report when the user is relaxed. Furthermore, the reporting unit can provide a simple and easy-to-understand report when the user is stressed. Furthermore, the reporting unit can provide a report that focuses on the main points when the user is in a hurry. This allows for efficient reporting by providing a way to express the report according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] When making a report, the reporting unit can adjust the level of detail of the report based on the learner's progress. Examples of the level of detail include, but are not limited to, the length of the report's explanation, the number of specific examples, and the amount of supplementary information. For example, the reporting unit provides a detailed report when the learner's progress is good. Furthermore, the reporting unit can provide a report that focuses on the main points when the learner's progress is slow. Furthermore, the reporting unit can gradually adjust the level of detail of the report depending on the learner's progress. This makes it possible to support efficient reporting by providing reports that are tailored to the learner's progress.

[0101] When reporting, the reporting unit can apply different reporting algorithms depending on the learner's category. Examples of categories include, but are not limited to, grade, subject, and skill level. For example, the reporting unit applies an algorithm for generating a report for beginners. The reporting unit can also apply an algorithm for generating a report for intermediate learners. The reporting unit can also apply an algorithm for generating a report for advanced learners. This makes it possible to provide reports according to the learner's category, thereby supporting efficient reporting.

[0102] When making a report, the reporting unit can improve the accuracy of the report by referring to the learner's past report results. Report results include, but are not limited to, for example, the correct answer rate, answer time, and incorrect answer patterns. For example, the reporting unit analyzes the learner's past report results and generates a report according to the learner's level of understanding. The reporting unit can also generate a similar report based on questions that the learner got wrong in the past. The reporting unit can also suggest an optimal report based on the learner's past report results. This makes it possible to provide a more accurate report by utilizing the learner's past report results.

[0103] The reporting unit can estimate the user's emotion and adjust the length of the report based on the estimated user's emotion. Emotions include, but are not limited to, for example, being in a hurry, being relaxed, or feeling stressed. For example, the reporting unit can provide a short report when the user is in a hurry. The reporting unit can also provide a detailed report when the user is relaxed. The reporting unit can also provide a simple report when the user is feeling stressed. This can support efficient reporting by providing a report length that corresponds to the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] When a report is submitted, the reporting unit can determine the priority of the report based on the time of submission by the learner. The submission time includes, but is not limited to, for example, a submission deadline, a frequency of submission, and a timing of submission. For example, the reporting unit generates reports with a deadline approaching. The reporting unit can also generate reports with a priority of early submission by the learner. The reporting unit can also adjust the priority of the reports according to the time of submission. This makes it possible to support efficient reporting by providing reports according to the time of submission by the learner.

[0105] The reporting unit can adjust the order of reports based on the relevance of the learner when making a report. Relevance includes, but is not limited to, for example, relevance of the learning content, relevance of the report, and relevance of the learner's interests. For example, the reporting unit prioritizes generating reports related to the learner's current learning content. The reporting unit can also prioritize generating reports related to the learner's past learning content. The reporting unit can also adjust the order of reports based on the learner's interests and concerns. This can support efficient reporting by providing an order of reports according to the learner's relevance.

[0106] When making a report, the reporting unit can adjust the use of technical terms in the report according to the learner's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, the reporting unit can generate a report with fewer technical terms for beginners. Furthermore, the reporting unit can generate a report with an appropriate amount of technical terms for intermediate learners. Furthermore, the reporting unit can generate a report with a heavy use of technical terms for advanced learners. This allows for efficient reporting by providing a report according to the learner's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, recording unit, and reporting unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and inputs learning goals and current learning status. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates questions appropriate for each learner using a generation AI. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the learner's progress based on the generated questions. The reporting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and reports the recorded progress to the teacher. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, recording unit, and reporting unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and inputs learning goals and current learning status. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates questions appropriate for each learner using a generation AI. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the learner's progress based on the generated questions. The reporting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and reports the recorded progress to the teacher. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned receiving unit, generating unit, recording unit, and reporting unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the receiving unit is realized by the microphone 238 of the headset-type terminal 314 and inputs the learning goals and current learning situation. The generating unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates questions appropriate for each learner using a generation AI. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the learner's progress based on the generated questions. The reporting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and reports the recorded progress to the teacher. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, recording unit, and reporting unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and inputs learning goals and current learning status. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates questions appropriate for each learner using a generation AI. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the learner's progress based on the generated questions. The reporting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and reports the recorded progress to the teacher.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The reception unit can estimate the user's learning style and adjust the input method for learning goals based on the estimated learning style. Learning styles include, but are not limited to, visual, auditory, and experiential learning. For example, if the user has a visual learning style, the reception unit provides a graphical interface and prioritizes visual input means. Furthermore, if the user has an auditory learning style, the reception unit can provide voice input or voice guidance. Furthermore, if the user has an experiential learning style, the reception unit can provide interactive input means. This can support efficient input by providing an input method according to the user's learning style.

[0109] The generation unit can customize the theme of the questions based on the learner's interests and concerns. Examples of interests and concerns include, but are not limited to, sports, music, science, etc. For example, if the learner is interested in sports, the generation unit can generate sports-related questions. Furthermore, if the learner is interested in music, the generation unit can generate music-related questions. Furthermore, if the learner is interested in science, the generation unit can generate science-related questions. This can improve the learner's motivation by providing questions that match the learner's interests and concerns.

[0110] The generation unit can estimate the learner's emotions and adjust the feedback method for the questions based on the estimated emotions. Emotions include, but are not limited to, joy, sadness, anger, and the like. For example, the generation unit can provide positive feedback if the learner is feeling joyful. Furthermore, the generation unit can provide encouraging feedback if the learner is feeling sad. Furthermore, the generation unit can provide calm feedback if the learner is feeling angry. In this way, by providing feedback according to the learner's emotions, it is possible to support continuation of learning.

[0111] The recording unit can monitor the learner's progress in real time and issue an alert if an abnormality is detected. Abnormalities include, but are not limited to, a sudden decrease in study time, a decrease in comprehension, and a stagnation of progress. For example, the recording unit issues an alert if there is a sudden decrease in study time. The recording unit can also issue an alert if there is a decrease in comprehension. The recording unit can also issue an alert if progress stagnates. This makes it possible to grasp the learner's progress in real time and prompt appropriate action.

[0112] The reporting unit can estimate the learner's emotions and adjust the timing of the report based on the estimated emotions. Emotions include, but are not limited to, for example, being focused, being tired, being excited, etc. For example, the reporting unit can delay the report when the learner is focused. Furthermore, the reporting unit can also simplify the report when the learner is tired. Furthermore, the reporting unit can provide a detailed report when the learner is excited. This can support efficient reporting by providing the timing of the report according to the learner's emotions.

[0113] The generation unit can predict future learning performance based on the learner's past learning data and propose an appropriate learning plan. Learning data includes, but is not limited to, past test results, study time, and level of understanding, for example. The generation unit can predict future learning performance based on, for example, past test results. The generation unit can also predict future learning performance based on study time. The generation unit can also predict future learning performance based on level of understanding. This makes it possible to propose a more effective learning plan by utilizing the learner's past data.

[0114] The reception unit can estimate the user's emotions and evaluate the level of achievement of the learning goal based on the estimated emotions. Emotions include, but are not limited to, satisfaction, dissatisfaction, and a sense of accomplishment. For example, if the user feels satisfied, the reception unit can evaluate the level of achievement as high. Furthermore, if the user feels dissatisfied, the reception unit can also evaluate the level of achievement as low. Furthermore, if the user feels a sense of accomplishment, the reception unit can also evaluate the level of achievement as appropriate. This can improve the user's motivation to learn by providing an achievement evaluation that corresponds to the user's emotions.

[0115] The generator can adjust the format of the questions based on the learner's learning environment. Examples of learning environments include, but are not limited to, online, offline, and hybrid. For example, in an online environment, the generator can generate interactive questions. In an offline environment, the generator can also generate printable questions. In a hybrid environment, the generator can also generate questions that can be used both online and offline. This can support efficient learning by providing questions that are appropriate for the learner's learning environment.

[0116] The reporting unit can estimate the learner's emotions and adjust the report format based on the estimated emotions. Emotions include, but are not limited to, joy, sadness, anger, and the like. For example, if the learner is feeling joy, the reporting unit can provide a colorful and visually appealing report. Also, if the learner is feeling sad, the reporting unit can provide a simple and calm report. Also, if the learner is feeling anger, the reporting unit can provide a calm and objective report. In this way, efficient reporting can be supported by providing a report format according to the learner's emotions.

[0117] The generation unit can adjust the frequency of questions based on the learner's learning pace. Examples of learning pace include, but are not limited to, fast, normal, and slow. For example, if the learner's learning pace is fast, the generation unit can frequently present questions. Also, if the learner's learning pace is normal, the generation unit can present questions at a moderate frequency. Also, if the learner's learning pace is slow, the generation unit can present questions at a slow frequency. This can support efficient learning by providing questions at a frequency that matches the learner's learning pace.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The receptionist inputs learning goals or current learning status. Learning goals include short-term goals, long-term goals, specific subjects or skills, etc. Current learning status includes test scores, progress, level of understanding, etc. Step 2: The generator analyzes the information entered by the reception unit and generates questions appropriate for each individual learner. Using AI generation, the generator can adjust the difficulty of the questions according to the learner's level of understanding and provide relevant supplementary explanations or additional practice questions if the learner has difficulty with a particular question. It can also generate individual questions for each learner, even in large-scale educational environments. Step 3: The recorder records the learner's progress based on the questions generated by the generator, including the level of achievement, study time, and understanding. Step 4: The reporting unit reports the progress recorded by the recording unit to the teacher. The reporting unit can provide the teacher with the progress of the learner in the form of a graph or report.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the 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.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0171] The data processing system 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.

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

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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 inputting learning goals or current learning status; a generation unit that analyzes the information input by the reception unit and generates questions appropriate for each learner; a recording unit that records the progress of the learner based on the questions generated by the generating unit; a reporting unit that reports the progress recorded by the recording unit to a teacher. A system characterized by:

2. The generation unit Adjust the difficulty of questions according to the learner's level of understanding 2. The system of claim 1.

3. The generation unit Providing additional explanations or practice questions if you are struggling with a particular problem 2. The system of claim 1.

4. The reporting unit Provide teachers with graphs and reports on learner progress 2. The system of claim 1.

5. The generation unit Generate personalized questions for each learner in a wide range of educational environments 2. The system of claim 1.

6. The generation unit Uses an algorithm to evaluate a learner's level of understanding based on their past answer history and answer time.

2. The system of claim 1.

7. The reception unit Inferring user emotions and adjusting the learning goal input method based on the estimated user emotions 2. The system of claim 1.

8. The reception unit When entering learning goals or current learning status, the system automatically completes the input by referring to the user's past learning history.

2. The system of claim 1.

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