system

Abstract

The system according to the embodiment aims to accurately grasp the academic ability and weak areas of a user and provide appropriate practice questions based on that. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a creation unit, and a provision unit. The collection unit collects academic ability data of a user. The analysis unit analyzes the data collected by the collection unit and determines the user's academic ability or weak areas. The creation unit creates practice questions based on the weak areas determined by the analysis unit. The provision unit provides explanations for the practice questions created by the creation unit.

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 techniques do not adequately grasp a user's academic ability and weak areas accurately and provide appropriate practice questions based on that, so there is room for improvement.

[0005] The system according to the embodiment aims to accurately grasp the academic ability and weak areas of a user and provide appropriate practice questions based on that. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a creation unit, and a provision unit. The collection unit collects academic ability data of a user. The analysis unit analyzes the data collected by the collection unit and determines the user's academic ability or weak areas. The creation unit creates practice questions based on the weak areas determined by the analysis unit. The provision unit provides explanations for the practice questions created by the creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately grasp the academic ability and weak areas of a user and provide appropriate practice questions based on that. [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) A study support system according to an embodiment of the present invention automatically analyzes a user's academic ability, determines their weak areas, creates practice problems based on the weak areas, and provides explanations. The study support system collects and analyzes the user's academic ability data, creates practice problems based on the weak areas, and provides explanations. For example, the study support system collects questions and test results previously solved by the user, and a generation AI analyzes them to determine the user's academic ability and weak areas. The study support system then creates practice problems based on the determined weak areas. For example, if it is determined that the user is weak in differential and integral calculus, the generation AI creates practice problems related to differential and integral calculus. The difficulty level of these practice problems is adjusted to match the user's academic ability. Furthermore, the study support system provides explanations for the created practice problems. For example, for differential and integral calculus problems solved by the user, explanations including solution steps and important points are provided. This allows the user to deepen their understanding of the problem. This allows the study support system to efficiently analyze the user's academic ability and provide practice problems based on the user's weak areas, thereby effectively supporting learning. For example, a user can identify their weak areas and solve practice problems based on that information, thereby effectively progressing through their studies. In addition, by learning how to solve problems and what to pay attention to through explanations, you will be able to deal with similar problems.

[0029] A study support system according to an embodiment includes a collection unit, an analysis unit, a creation unit, and a provision unit. The collection unit collects academic ability data of a user. The academic ability data includes, for example, test results, homework grades, quiz scores, etc., but is not limited to these examples. The collection unit collects, for example, questions and test results previously solved by the user. For example, the collection unit can collect online test results. The collection unit can also scan paper-based test results and convert them into digital data for collection. The collection unit can also collect data on questions previously solved by the user. For example, the collection unit collects math questions and test results previously solved by the user. The analysis unit analyzes the data collected by the collection unit to determine the user's academic ability and weak areas. The analysis is performed, for example, using statistical analysis or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit performs statistical analysis on the collected data to determine the user's academic ability. The analysis unit can also use a machine learning algorithm to determine the user's weak areas. Furthermore, the analysis unit can analyze the collected data to determine the user's academic ability and weak areas. For example, the analysis unit can analyze math problems and test results that the user has solved in the past to determine the user's academic ability and weak areas in math. The creation unit creates exercises based on the weak areas determined by the analysis unit. The exercises are created in formats such as, but not limited to, multiple-choice questions, written questions, and practical questions. For example, the creation unit generates exercises that focus on the user's weak areas. The creation unit can also adjust the difficulty of the exercises to match the user's academic ability. Furthermore, the creation unit can create exercises based on the user's weak areas. For example, if the creation unit determines that the user is weak in differential and integral calculus in math, the creation unit creates exercises related to differential and integral calculus. The provision unit provides explanations for the exercises created by the creation unit. The explanations can be provided in formats such as, but not limited to, text explanations, video explanations, and interactive explanations. For example, the provision unit provides detailed explanations for the exercises. The provision unit can also provide detailed explanations for the exercises solved by the user.Furthermore, the providing unit can also provide explanations for the created practice problems. For example, the providing unit provides explanations for calculus problems solved by the user, including solution steps and important points. This allows the study support system according to the embodiment to efficiently analyze the user's academic ability and provide practice problems based on weak areas, thereby effectively supporting learning. For example, the user can effectively advance their studies by identifying their weak areas and solving practice problems based on that. Furthermore, by learning solutions and important points for solving problems through the explanations, the user will be able to deal with similar problems.

[0030] The collection unit can collect the results of problems and tests that the user has previously solved. Examples of previously solved problems and test results include, but are not limited to, online test results and paper-based test results. The collection unit, for example, collects the results of problems and tests that the user has previously solved. For example, the collection unit can collect online test results. The collection unit can also scan paper-based test results, convert them into digital data, and collect them. The collection unit can also collect data on problems that the user has previously solved. For example, the collection unit collects the results of mathematics problems and tests that the user has previously solved. This enables more accurate analysis of academic ability based on the user's past learning history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the results of problems and tests that the user has previously solved into the generation AI and have the generation AI collect the data.

[0031] The analysis unit can analyze the collected data and determine the user's academic ability and weak areas. For example, statistical analysis and machine learning algorithms can be used to determine the academic ability and weak areas, but these examples are not limited to these. For example, the analysis unit can statistically analyze the collected data to determine the user's academic ability. The analysis unit can also determine the user's weak areas using machine learning algorithms. Furthermore, the analysis unit can analyze the collected data and determine the user's academic ability and weak areas. For example, the analysis unit can analyze math problems and test results that the user has previously solved to determine the user's math ability and weak areas. This enables accurate determination of the user's academic ability and weak areas, enabling effective learning support. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI perform the determination of the user's academic ability and weak areas.

[0032] The creation unit can generate exercises that focus on the user's weak areas. Exercises that focus on the user's weak areas include, but are not limited to, problem sets on specific topics. For example, the creation unit generates exercises that focus on the user's weak areas. The creation unit can also adjust the difficulty of the exercises to match the user's academic ability. Furthermore, the creation unit can create exercises based on the user's weak areas. For example, if the creation unit determines that the user is weak in differential and integral calculus, it creates exercises related to differential and integral calculus. This enables efficient learning by providing exercises tailored to the user's weak areas. Some or all of the above-described processing by the creation unit may be performed using, for example, AI, or may be performed without AI. For example, the creation unit can input data related to the user's weak areas into a generation AI and cause the generation AI to generate exercises.

[0033] The providing unit can provide detailed explanations for the exercises. Detailed explanations include, but are not limited to, step-by-step explanations, video tutorials, and the like. The providing unit can provide, for example, detailed explanations for the exercises. The providing unit can also provide detailed explanations for the exercises solved by the user. Furthermore, the providing unit can provide explanations for the created exercises. For example, the providing unit can provide explanations for calculus problems solved by the user, including solution steps and points to note. By providing detailed explanations, the user's understanding can be deepened. Some or all of the above-described processing by the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input explanations for the exercises to a generating AI and cause the generating AI to generate explanations.

[0034] The creation unit can adjust the difficulty of the practice questions to match the user's academic ability. Examples of adjustments to the difficulty of the practice questions include, but are not limited to, the complexity of the questions and the length of time required to answer them. The creation unit can also adjust the difficulty of the practice questions to match the user's academic ability. The creation unit can also provide practice questions of an appropriate level of difficulty according to the user's academic ability. Furthermore, the creation unit can adjust the difficulty of the practice questions based on the user's academic ability. For example, if the creation unit determines that the user is weak in differential and integral calculus, it adjusts the difficulty of the practice questions related to differential and integral calculus. This allows for effective learning by providing practice questions of an appropriate level of difficulty according to the user's academic ability. Some or all of the above-described processing in the creation unit may be performed using, or without, AI. For example, the creation unit can input the user's academic ability data into the generation AI and cause the generation AI to adjust the difficulty of the practice questions.

[0035] The collection unit can analyze the user's past learning history and select the optimal data collection method. Suitable data collection methods include, but are not limited to, online surveys and the use of sensor data. For example, if the user has solved many text-based problems in the past, the collection unit can select a text-based data collection method. Furthermore, if the user has frequently used voice input in the past, the collection unit can also select a data collection method using voice input. Furthermore, if the user has solved image-based problems in the past, the collection unit can also select a data collection method using image recognition. This enables efficient data collection by selecting the optimal data collection method based on the user's past learning history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past learning history into the generation AI and have the generation AI select the optimal data collection method.

[0036] When collecting academic ability data, the collection unit can filter the data based on the user's current learning situation and areas of interest. Examples of filtering include, but are not limited to, the use of specific keywords and the importance of the data. For example, the collection unit collects only data related to the subject the user is currently studying. The collection unit can also prioritize collecting data related to topics in which the user is interested. Furthermore, the collection unit can focus on collecting data related to areas in which the user is weak. This allows highly relevant data to be collected by filtering the data based on the user's current learning situation and areas of interest. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data related to the user's current learning situation and areas of interest into the generation AI and have the generation AI perform the filtering.

[0037] When collecting academic ability data, the collection unit can select an appropriate collection means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the collection unit can collect academic ability data using voice recognition. Alternatively, if the user prefers text input, the collection unit can collect academic ability data by providing a text-based interface. Furthermore, if the user prefers image input, the collection unit can collect academic ability data using image recognition technology. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data regarding the user's input method into the generation AI and have the generation AI select the optimal collection means.

[0038] When collecting academic ability data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user lives in a specific area, the collection unit can prioritize collecting data related to the educational curriculum of that area. Furthermore, if the user attends a specific school, the collection unit can prioritize collecting data related to the school's curriculum. Furthermore, if the user plans to take an exam in a specific area, the collection unit can prioritize collecting data related to the exam. This enables efficient data collection by collecting highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0039] When collecting academic ability data, the collection unit can analyze the user's social media activities and collect related data. Social media activities include, but are not limited to, post content, number of likes, and comments. For example, the collection unit can collect related data based on the learning content shared by the user on social media. The collection unit can also collect information on education-related accounts the user follows on social media. Furthermore, the collection unit can analyze the activities of learning groups the user participates in on social media and collect related data. This enables efficient data collection by collecting related data based on the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data regarding the user's social media activities into the generation AI and cause the generation AI to collect related data.

[0040] When collecting academic ability data, the collection unit can customize the collection method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, survey results and user comments. The collection unit can adjust the collection method, for example, based on feedback provided by the user in the past. The collection unit can also prioritize collection methods that the user has previously preferred. Furthermore, the collection unit can adjust the collection method to avoid collection methods that the user has previously dissatisfied with. This enables efficient data collection by customizing the collection method based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input data regarding the user's past feedback into the generation AI and have the generation AI customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the academic ability data. The importance of academic ability data includes, but is not limited to, test scores, study time, and the like. For example, the analysis unit performs a detailed analysis of important academic ability data. The analysis unit can also perform a concise analysis of less important data. Furthermore, the analysis unit can focus on analyzing data related to the user's learning goals. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the academic ability data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data related to the importance of academic ability data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of academic ability data. Examples of analysis algorithms include, but are not limited to, regression analysis and clustering. For example, the analysis unit can apply a numerical analysis algorithm to mathematics data. The analysis unit can also apply a natural language processing algorithm to language data. Furthermore, the analysis unit can apply an experimental data analysis algorithm to science data. This enables more accurate analysis by applying an analysis algorithm depending on the category of academic ability data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data related to the category of academic ability data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past test results and learning history. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also identify trends from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can compare the user's past analysis results and improve the reliability of the current analysis result. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the submission time of the academic ability data. The submission time includes, but is not limited to, the submission deadline and the submission frequency. For example, the analysis unit prioritizes the analysis of recently submitted academic ability data. The analysis unit can also prioritize the analysis of data submitted before an important exam. Furthermore, the analysis unit can prioritize the analysis of data within a period specified by the user. This enables efficient analysis by determining the analysis priority based on the submission time of the academic ability data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the submission time of the academic ability data into the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the academic ability data. Relevance includes, but is not limited to, for example, the degree of topic agreement and data correlation. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can prioritize analysis of data related to the user's learning goals. This enables efficient analysis by adjusting the order of analysis based on the relevance of the academic ability data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the relevance of the academic ability data to the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. Technical terminology includes, but is not limited to, academic and technical terms. For example, if the user is a beginner, the analysis unit can provide analysis results that avoid technical terminology. For an intermediate user, the analysis unit can also provide analysis results that use technical terminology appropriately. For an advanced user, the analysis unit can also provide detailed analysis results that use a lot of technical terminology. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input data regarding the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0047] When creating practice questions, the creation unit can analyze the user's past learning history and select optimal questions. Examples of optimal questions include, but are not limited to, the user's academic level and past grades. For example, the creation unit can analyze the trends of questions the user has solved in the past and create similar questions. The creation unit can also create questions to help the user overcome problems that the user found difficult in the past. Furthermore, the creation unit can create more difficult questions based on problems the user found easy in the past. This enables efficient learning by selecting optimal questions based on the user's past learning history. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without AI. For example, the creation unit can input data regarding the user's past learning history into the generation AI and have the generation AI select optimal questions.

[0048] When creating practice questions, the creation unit can adjust the difficulty of the questions based on the user's current learning situation. Examples of the current learning situation include, but are not limited to, the user's current learning progress and learning time. The creation unit can create questions of an appropriate level of difficulty based on the user's current learning situation. The creation unit can also create questions with difficulty levels adjusted to match the user's current learning goals. Furthermore, the creation unit can create questions with gradually increasing difficulty levels based on the user's current learning progress. This allows for effective learning by adjusting the difficulty of the questions based on the user's current learning situation. Some or all of the above-described processing in the creation unit can be performed using, or without, AI. For example, the creation unit can input data regarding the user's current learning situation into the generation AI and cause the generation AI to adjust the difficulty of the questions.

[0049] When creating practice questions, the creation unit can improve the content of the questions by reflecting user feedback. Examples of feedback include, but are not limited to, survey results and user comments. The creation unit can, for example, adjust the content of the questions based on feedback previously provided by the user. The creation unit can also improve questions that the user was dissatisfied with in the past and provide them again. Furthermore, the creation unit can create new questions by referring to question formats that the user previously preferred. This allows for the provision of more effective practice questions by improving the content of the questions based on user feedback. Some or all of the above-described processing in the creation unit can be performed, for example, using AI or without AI. For example, the creation unit can input data regarding user feedback into a generation AI and have the generation AI improve the content of the questions.

[0050] When creating practice questions, the creation unit can select optimal questions by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP address. For example, if the user lives in a specific area, the creation unit can create questions related to the educational curriculum of that area. Furthermore, if the user attends a specific school, the creation unit can also create questions related to the school's curriculum. Furthermore, if the user plans to take an exam in a specific area, the creation unit can also create questions related to that exam. This enables efficient learning by selecting optimal questions based on the user's geographical location information. Some or all of the above-described processing in the creation unit may be performed using, or without, AI. For example, the creation unit can input data related to the user's geographical location information into a generation AI and have the generation AI select optimal questions.

[0051] When creating practice questions, the creation unit can analyze the user's social media activity to create relevant questions. Social media activity includes, but is not limited to, post content, the number of likes, and comments. The creation unit can create relevant questions, for example, based on learning content shared by the user on social media. The creation unit can also create questions based on information about education-related accounts the user follows on social media. Furthermore, the creation unit can analyze the activities of study groups the user participates in on social media and create relevant questions. This enables efficient learning by creating relevant questions based on the user's social media activity. Some or all of the above-described processing by the creation unit can be performed using, or without, AI. For example, the creation unit can input data about the user's social media activity into a generation AI and cause the generation AI to create relevant questions.

[0052] When creating practice questions, the creation unit can customize the content of the questions by reflecting the user's past feedback. Examples of feedback include, but are not limited to, survey results and user comments. The creation unit customizes the content of the questions based on, for example, feedback provided by the user in the past. The creation unit can also improve questions that the user was dissatisfied with in the past and provide them again. Furthermore, the creation unit can customize new questions by referring to question formats that the user previously preferred. This allows for more effective practice questions to be provided by customizing the content of the questions based on the user's past feedback. Some or all of the above-described processing by the creation unit may be performed using, for example, AI, or may be performed without AI. For example, the creation unit can input data regarding the user's past feedback into the generation AI and have the generation AI customize the content of the questions.

[0053] When providing an explanation, the providing unit can provide an optimal explanation by referring to the user's past learning history. Examples of past learning history include, but are not limited to, test results and homework grades. For example, the providing unit can provide relevant explanations based on explanations for problems the user has solved in the past. The providing unit can also provide explanations that focus on problems the user has previously struggled with. Furthermore, the providing unit can provide explanations to further deepen understanding by referring to explanations for problems the user has previously excelled at. This enables efficient learning by providing optimal explanations based on the user's past learning history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input data regarding the user's past learning history into the generating AI and cause the generating AI to provide optimal explanations.

[0054] When providing an explanation, the providing unit can customize the content of the explanation based on the user's current learning situation. The current learning situation includes, but is not limited to, for example, the user's current learning progress and learning time. The providing unit, for example, provides an appropriate explanation according to the user's current learning situation. The providing unit can also adjust the content of the explanation to match the user's current learning goal. Furthermore, the providing unit can provide a stepwise detailed explanation based on the user's current learning progress. This enables efficient learning by customizing the content of the explanation based on the user's current learning situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data regarding the user's current learning situation into the generating AI and cause the generating AI to customize the content of the explanation.

[0055] When providing an explanation, the providing unit can improve the content of the explanation by reflecting user feedback. Examples of feedback include, but are not limited to, survey results and user comments. The providing unit can adjust the content of the explanation based on, for example, feedback previously provided by the user. The providing unit can also improve an explanation that the user was dissatisfied with in the past and provide it again. Furthermore, the providing unit can provide a new explanation by referring to the format of explanations that the user previously preferred. This enables more effective learning by improving the content of the explanation based on user feedback. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input data regarding user feedback into a generating AI and cause the generating AI to improve the content of the explanation.

[0056] When providing explanations, the providing unit can provide optimal explanations by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user lives in a specific area, the providing unit can provide explanations related to the educational curriculum of that area. Furthermore, if the user attends a specific school, the providing unit can also provide explanations related to the school's curriculum. Furthermore, if the user plans to take an exam in a specific area, the providing unit can also provide explanations related to the exam. This enables efficient learning by providing optimal explanations based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input data related to the user's geographical location information into a generating AI and cause the generating AI to provide optimal explanations.

[0057] When providing commentary, the providing unit can analyze the user's social media activity and provide related commentary. Social media activity includes, but is not limited to, post content, the number of likes, and comments. For example, the providing unit can provide related commentary based on learning content shared by the user on social media. The providing unit can also provide commentary based on information about education-related accounts the user follows on social media. Furthermore, the providing unit can analyze the activity content of learning groups the user participates in on social media and provide related commentary. This enables efficient learning by providing related commentary based on the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input data about the user's social media activity into a generation AI and cause the generation AI to provide related commentary.

[0058] When providing an explanation, the providing unit can customize the content of the explanation by reflecting the user's past feedback. Examples of feedback include, but are not limited to, survey results and user comments. The providing unit customizes the content of the explanation based on, for example, feedback provided by the user in the past. The providing unit can also improve an explanation that the user was dissatisfied with in the past and provide it again. Furthermore, the providing unit can customize a new explanation by referring to the format of explanations that the user preferred in the past. This enables more effective learning by customizing the content of the explanation based on the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data regarding the user's past feedback into a generating AI and cause the generating AI to customize the content of the explanation.

[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] When analyzing a user's academic ability data, the analysis unit can adjust the analysis method taking into account the user's learning style. For example, a user with a visual learning style can be provided with analysis results that make extensive use of graphs and charts. A user with an auditory learning style can be provided with analysis results that include audio commentary. Furthermore, a user with a tactile learning style can be provided with interactive analysis tools. This allows for more effective learning support by providing analysis results that suit the user's learning style.

[0061] When collecting a user's academic ability data, the collection unit can adjust the data collection method taking into account the user's learning environment. For example, for a user who studies in a quiet environment, data can be collected using voice input. For a user who studies while on the move, data can be collected using a mobile device. Furthermore, for a user who studies in a group, data can be collected using a collaboration tool. In this way, by providing a data collection method that suits the user's learning environment, more accurate academic ability data can be collected.

[0062] When creating practice questions based on the user's academic ability data, the creation unit can adjust the content of the questions taking into account the user's learning goals. For example, a user with short-term goals can be provided with questions that directly lead to achieving those goals. A user with long-term goals can also be provided with step-by-step questions ranging from basic to advanced. Furthermore, a user studying for a specific exam can be provided with questions specialized for that exam. This makes it possible to provide effective learning support by providing practice questions that match the user's learning goals.

[0063] When providing explanations based on the user's academic ability data, the providing unit can adjust the method of providing the explanations taking into account the user's learning pace. For example, a user who studies at a fast pace can be provided with a concise explanation summarizing the main points. A user who studies at a slow pace can be provided with a detailed step-by-step explanation. Furthermore, a user who studies at a fluctuating pace can be provided with explanations that can be adjusted in real time. This allows for more effective learning support by providing explanations that match the user's learning pace.

[0064] When collecting a user's academic ability data, the collection unit can adjust the frequency of data collection taking into account the user's learning history. For example, for a user who studies frequently, data can be collected periodically. For a user who studies intermittently, data can also be collected in accordance with the timing of the user's studies. Furthermore, for a user who studies over a long period of time, long-term data collection can be performed. In this way, by providing a data collection frequency according to the user's learning history, more accurate academic ability data can be collected.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects the user's academic achievement data. The academic achievement data includes test results, homework grades, quiz scores, etc. The collection unit collects the user's past questions and test results, and can also scan online test results and paper-based test results to convert them into digital data. Step 2: The analysis unit analyzes the data collected by the collection unit and determines the user's academic ability and weak areas. The analysis is performed using statistical analysis and machine learning algorithms. For example, it analyzes the math problems and test results that the user has solved in the past to determine the user's math ability and weak areas. Step 3: The creation unit creates practice questions based on the weak areas determined by the analysis unit. The practice questions are created in the form of multiple-choice questions, written questions, practical questions, etc. For example, practice questions that focus on the user's weak areas may be generated, and the difficulty level of the practice questions may be adjusted to match the user's academic ability. Step 4: The providing unit provides explanations for the practice problems created by the creating unit. The explanations may be provided in the form of text explanations, video explanations, interactive explanations, etc. For example, for a calculus problem solved by a user, an explanation including solution steps and points to note is provided.

[0067] (Example 2) A study support system according to an embodiment of the present invention automatically analyzes a user's academic ability, determines their weak areas, creates practice problems based on the weak areas, and provides explanations. The study support system collects and analyzes the user's academic ability data, creates practice problems based on the weak areas, and provides explanations. For example, the study support system collects questions and test results previously solved by the user, and a generation AI analyzes them to determine the user's academic ability and weak areas. The study support system then creates practice problems based on the determined weak areas. For example, if it is determined that the user is weak in differential and integral calculus, the generation AI creates practice problems related to differential and integral calculus. The difficulty level of these practice problems is adjusted to match the user's academic ability. Furthermore, the study support system provides explanations for the created practice problems. For example, for differential and integral calculus problems solved by the user, explanations including solution steps and important points are provided. This allows the user to deepen their understanding of the problem. This allows the study support system to efficiently analyze the user's academic ability and provide practice problems based on the user's weak areas, thereby effectively supporting learning. For example, a user can identify their weak areas and solve practice problems based on that information, thereby effectively progressing through their studies. In addition, by learning how to solve problems and what to pay attention to through explanations, you will be able to deal with similar problems.

[0068] A study support system according to an embodiment includes a collection unit, an analysis unit, a creation unit, and a provision unit. The collection unit collects academic ability data of a user. The academic ability data includes, for example, test results, homework grades, quiz scores, etc., but is not limited to these examples. The collection unit collects, for example, questions and test results previously solved by the user. For example, the collection unit can collect online test results. The collection unit can also scan paper-based test results and convert them into digital data for collection. The collection unit can also collect data on questions previously solved by the user. For example, the collection unit collects math questions and test results previously solved by the user. The analysis unit analyzes the data collected by the collection unit to determine the user's academic ability and weak areas. The analysis is performed, for example, using statistical analysis or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit performs statistical analysis on the collected data to determine the user's academic ability. The analysis unit can also use a machine learning algorithm to determine the user's weak areas. Furthermore, the analysis unit can analyze the collected data to determine the user's academic ability and weak areas. For example, the analysis unit can analyze math problems and test results that the user has solved in the past to determine the user's academic ability and weak areas in math. The creation unit creates exercises based on the weak areas determined by the analysis unit. The exercises are created in formats such as, but not limited to, multiple-choice questions, written questions, and practical questions. For example, the creation unit generates exercises that focus on the user's weak areas. The creation unit can also adjust the difficulty of the exercises to match the user's academic ability. Furthermore, the creation unit can create exercises based on the user's weak areas. For example, if the creation unit determines that the user is weak in differential and integral calculus in math, the creation unit creates exercises related to differential and integral calculus. The provision unit provides explanations for the exercises created by the creation unit. The explanations can be provided in formats such as, but not limited to, text explanations, video explanations, and interactive explanations. For example, the provision unit provides detailed explanations for the exercises. The provision unit can also provide detailed explanations for the exercises solved by the user.Furthermore, the providing unit can also provide explanations for the created practice problems. For example, the providing unit provides explanations for calculus problems solved by the user, including solution steps and important points. This allows the study support system according to the embodiment to efficiently analyze the user's academic ability and provide practice problems based on weak areas, thereby effectively supporting learning. For example, the user can effectively advance their studies by identifying their weak areas and solving practice problems based on that. Furthermore, by learning solutions and important points for solving problems through the explanations, the user will be able to deal with similar problems.

[0069] The collection unit can collect the results of problems and tests that the user has previously solved. Examples of previously solved problems and test results include, but are not limited to, online test results and paper-based test results. The collection unit, for example, collects the results of problems and tests that the user has previously solved. For example, the collection unit can collect online test results. The collection unit can also scan paper-based test results, convert them into digital data, and collect them. The collection unit can also collect data on problems that the user has previously solved. For example, the collection unit collects the results of mathematics problems and tests that the user has previously solved. This enables more accurate analysis of academic ability based on the user's past learning history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the results of problems and tests that the user has previously solved into the generation AI and have the generation AI collect the data.

[0070] The analysis unit can analyze the collected data and determine the user's academic ability and weak areas. For example, statistical analysis and machine learning algorithms can be used to determine the academic ability and weak areas, but these examples are not limited to these. For example, the analysis unit can statistically analyze the collected data to determine the user's academic ability. The analysis unit can also determine the user's weak areas using machine learning algorithms. Furthermore, the analysis unit can analyze the collected data and determine the user's academic ability and weak areas. For example, the analysis unit can analyze math problems and test results that the user has previously solved to determine the user's math ability and weak areas. This enables accurate determination of the user's academic ability and weak areas, enabling effective learning support. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI perform the determination of the user's academic ability and weak areas.

[0071] The creation unit can generate exercises that focus on the user's weak areas. Exercises that focus on the user's weak areas include, but are not limited to, problem sets on specific topics. For example, the creation unit generates exercises that focus on the user's weak areas. The creation unit can also adjust the difficulty of the exercises to match the user's academic ability. Furthermore, the creation unit can create exercises based on the user's weak areas. For example, if the creation unit determines that the user is weak in differential and integral calculus, it creates exercises related to differential and integral calculus. This enables efficient learning by providing exercises tailored to the user's weak areas. Some or all of the above-described processing by the creation unit may be performed using, for example, AI, or may be performed without AI. For example, the creation unit can input data related to the user's weak areas into a generation AI and cause the generation AI to generate exercises.

[0072] The providing unit can provide detailed explanations for the exercises. Detailed explanations include, but are not limited to, step-by-step explanations, video tutorials, and the like. The providing unit can provide, for example, detailed explanations for the exercises. The providing unit can also provide detailed explanations for the exercises solved by the user. Furthermore, the providing unit can provide explanations for the created exercises. For example, the providing unit can provide explanations for calculus problems solved by the user, including solution steps and points to note. By providing detailed explanations, the user's understanding can be deepened. Some or all of the above-described processing by the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input explanations for the exercises to a generating AI and cause the generating AI to generate explanations.

[0073] The creation unit can adjust the difficulty of the practice questions to match the user's academic ability. Examples of adjustments to the difficulty of the practice questions include, but are not limited to, the complexity of the questions and the length of time required to answer them. The creation unit can also adjust the difficulty of the practice questions to match the user's academic ability. The creation unit can also provide practice questions of an appropriate level of difficulty according to the user's academic ability. Furthermore, the creation unit can adjust the difficulty of the practice questions based on the user's academic ability. For example, if the creation unit determines that the user is weak in differential and integral calculus, it adjusts the difficulty of the practice questions related to differential and integral calculus. This allows for effective learning by providing practice questions of an appropriate level of difficulty according to the user's academic ability. Some or all of the above-described processing in the creation unit may be performed using, or without, AI. For example, the creation unit can input the user's academic ability data into the generation AI and cause the generation AI to adjust the difficulty of the practice questions.

[0074] The study support system estimates a user's emotions and adjusts the timing of collecting academic ability data based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing to collect academic ability data when the user is in a relaxed state. Furthermore, if the user is concentrating, the collection unit collects academic ability data at that timing, thereby obtaining more accurate data. Furthermore, if the user is tired, the collection unit collects academic ability data after the user has rested, thereby improving the accuracy of the data. This allows for more accurate collection of academic ability data by adjusting the timing of data collection according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without an AI. For example, the collection unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the collection timing based on the emotion.

[0075] The collection unit can analyze the user's past learning history and select the optimal data collection method. Suitable data collection methods include, but are not limited to, online surveys and the use of sensor data. For example, if the user has solved many text-based problems in the past, the collection unit can select a text-based data collection method. Furthermore, if the user has frequently used voice input in the past, the collection unit can also select a data collection method using voice input. Furthermore, if the user has solved image-based problems in the past, the collection unit can also select a data collection method using image recognition. This enables efficient data collection by selecting the optimal data collection method based on the user's past learning history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past learning history into the generation AI and have the generation AI select the optimal data collection method.

[0076] When collecting academic ability data, the collection unit can filter the data based on the user's current learning situation and areas of interest. Examples of filtering include, but are not limited to, the use of specific keywords and the importance of the data. For example, the collection unit collects only data related to the subject the user is currently studying. The collection unit can also prioritize collecting data related to topics in which the user is interested. Furthermore, the collection unit can focus on collecting data related to areas in which the user is weak. This allows highly relevant data to be collected by filtering the data based on the user's current learning situation and areas of interest. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data related to the user's current learning situation and areas of interest into the generation AI and have the generation AI perform the filtering.

[0077] When collecting academic ability data, the collection unit can select an appropriate collection means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the collection unit can collect academic ability data using voice recognition. Alternatively, if the user prefers text input, the collection unit can collect academic ability data by providing a text-based interface. Furthermore, if the user prefers image input, the collection unit can collect academic ability data using image recognition technology. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data regarding the user's input method into the generation AI and have the generation AI select the optimal collection means.

[0078] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. Data priority can include, but is not limited to, the importance of the data and the timing of collection. For example, when the user is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, when the user is stressed, the collection unit can prioritize collecting simple data. Furthermore, when the user is concentrating, the collection unit can prioritize collecting important data. This enables efficient data collection by determining the priority of data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0079] When collecting academic ability data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user lives in a specific area, the collection unit can prioritize collecting data related to the educational curriculum of that area. Furthermore, if the user attends a specific school, the collection unit can prioritize collecting data related to the school's curriculum. Furthermore, if the user plans to take an exam in a specific area, the collection unit can prioritize collecting data related to the exam. This enables efficient data collection by collecting highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0080] When collecting academic ability data, the collection unit can analyze the user's social media activities and collect related data. Social media activities include, but are not limited to, post content, number of likes, and comments. For example, the collection unit can collect related data based on the learning content shared by the user on social media. The collection unit can also collect information on education-related accounts the user follows on social media. Furthermore, the collection unit can analyze the activities of learning groups the user participates in on social media and collect related data. This enables efficient data collection by collecting related data based on the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data regarding the user's social media activities into the generation AI and cause the generation AI to collect related data.

[0081] When collecting academic ability data, the collection unit can customize the collection method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, survey results and user comments. The collection unit can adjust the collection method, for example, based on feedback provided by the user in the past. The collection unit can also prioritize collection methods that the user has previously preferred. Furthermore, the collection unit can adjust the collection method to avoid collection methods that the user has previously dissatisfied with. This enables efficient data collection by customizing the collection method based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input data regarding the user's past feedback into the generation AI and have the generation AI customize the collection method.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. Examples of presentation methods of the analysis include, but are not limited to, graph display, text report, etc. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results when the user is stressed. Furthermore, the analysis unit can provide analysis results that emphasize important points when the user is concentrating. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the academic ability data. The importance of academic ability data includes, but is not limited to, test scores, study time, and the like. For example, the analysis unit performs a detailed analysis of important academic ability data. The analysis unit can also perform a concise analysis of less important data. Furthermore, the analysis unit can focus on analyzing data related to the user's learning goals. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the academic ability data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data related to the importance of academic ability data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of academic ability data. Examples of analysis algorithms include, but are not limited to, regression analysis and clustering. For example, the analysis unit can apply a numerical analysis algorithm to mathematics data. The analysis unit can also apply a natural language processing algorithm to language data. Furthermore, the analysis unit can apply an experimental data analysis algorithm to science data. This enables more accurate analysis by applying an analysis algorithm depending on the category of academic ability data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data related to the category of academic ability data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past test results and learning history. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also identify trends from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can compare the user's past analysis results and improve the reliability of the current analysis result. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The length of the analysis can include, but is not limited to, the level of detail of the analysis and time constraints. For example, the analysis unit can provide a detailed analysis when the user is relaxed. The analysis unit can also provide a concise analysis when the user is stressed. Furthermore, the analysis unit can provide an analysis that emphasizes important points when the user is concentrating. This allows for adjusting the length of the analysis according to the user's emotions, thereby providing more appropriate analysis results. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0087] During analysis, the analysis unit can determine the analysis priority based on the submission time of the academic ability data. The submission time includes, but is not limited to, the submission deadline and the submission frequency. For example, the analysis unit prioritizes the analysis of recently submitted academic ability data. The analysis unit can also prioritize the analysis of data submitted before an important exam. Furthermore, the analysis unit can prioritize the analysis of data within a period specified by the user. This enables efficient analysis by determining the analysis priority based on the submission time of the academic ability data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the submission time of the academic ability data into the generation AI and have the generation AI determine the analysis priority.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the academic ability data. Relevance includes, but is not limited to, for example, the degree of topic agreement and data correlation. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can prioritize analysis of data related to the user's learning goals. This enables efficient analysis by adjusting the order of analysis based on the relevance of the academic ability data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the relevance of the academic ability data to the generation AI and have the generation AI adjust the order of analysis.

[0089] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. Technical terminology includes, but is not limited to, academic and technical terms. For example, if the user is a beginner, the analysis unit can provide analysis results that avoid technical terminology. For an intermediate user, the analysis unit can also provide analysis results that use technical terminology appropriately. For an advanced user, the analysis unit can also provide detailed analysis results that use a lot of technical terminology. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input data regarding the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0090] The creation unit can estimate the user's emotions and adjust the method for creating exercises based on the estimated user emotions. Examples of the method for creating exercises include, but are not limited to, the format of the exercises and the setting of the difficulty level. For example, the creation unit can create detailed exercises when the user is relaxed. Furthermore, the creation unit can create concise exercises when the user is stressed. Furthermore, the creation unit can create exercises that emphasize important points when the user is concentrating. This allows for adjusting the method for creating exercises based on the user's emotions, thereby providing more appropriate exercises. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the creation unit may be performed using, for example, an AI. For example, the creation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method for creating exercises.

[0091] When creating practice questions, the creation unit can analyze the user's past learning history and select optimal questions. Examples of optimal questions include, but are not limited to, the user's academic level and past grades. For example, the creation unit can analyze the trends of questions the user has solved in the past and create similar questions. The creation unit can also create questions to help the user overcome problems that the user found difficult in the past. Furthermore, the creation unit can create more difficult questions based on problems the user found easy in the past. This enables efficient learning by selecting optimal questions based on the user's past learning history. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without AI. For example, the creation unit can input data regarding the user's past learning history into the generation AI and have the generation AI select optimal questions.

[0092] When creating practice questions, the creation unit can adjust the difficulty of the questions based on the user's current learning situation. Examples of the current learning situation include, but are not limited to, the user's current learning progress and learning time. The creation unit can create questions of an appropriate level of difficulty based on the user's current learning situation. The creation unit can also create questions with difficulty levels adjusted to match the user's current learning goals. Furthermore, the creation unit can create questions with gradually increasing difficulty levels based on the user's current learning progress. This allows for effective learning by adjusting the difficulty of the questions based on the user's current learning situation. Some or all of the above-described processing in the creation unit can be performed using, or without, AI. For example, the creation unit can input data regarding the user's current learning situation into the generation AI and cause the generation AI to adjust the difficulty of the questions.

[0093] When creating practice questions, the creation unit can improve the content of the questions by reflecting user feedback. Examples of feedback include, but are not limited to, survey results and user comments. The creation unit can, for example, adjust the content of the questions based on feedback previously provided by the user. The creation unit can also improve questions that the user was dissatisfied with in the past and provide them again. Furthermore, the creation unit can create new questions by referring to question formats that the user previously preferred. This allows for the provision of more effective practice questions by improving the content of the questions based on user feedback. Some or all of the above-described processing in the creation unit can be performed, for example, using AI or without AI. For example, the creation unit can input data regarding user feedback into a generation AI and have the generation AI improve the content of the questions.

[0094] The creation unit can estimate the user's emotions and prioritize the exercises based on the estimated user emotions. The prioritization of exercises can include, but is not limited to, the importance of the exercises and the user's weak areas. For example, the creation unit can prioritize difficult exercises when the user is relaxed. The creation unit can also prioritize easy exercises when the user is stressed. Furthermore, the creation unit can prioritize important exercises when the user is concentrating. This enables efficient learning by prioritizing exercises based on the user's emotions. The emotion estimation can be achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the creation unit can be performed using, for example, an AI. For example, the creation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the exercises.

[0095] When creating practice questions, the creation unit can select optimal questions by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP address. For example, if the user lives in a specific area, the creation unit can create questions related to the educational curriculum of that area. Furthermore, if the user attends a specific school, the creation unit can also create questions related to the school's curriculum. Furthermore, if the user plans to take an exam in a specific area, the creation unit can also create questions related to that exam. This enables efficient learning by selecting optimal questions based on the user's geographical location information. Some or all of the above-described processing in the creation unit may be performed using, or without, AI. For example, the creation unit can input data related to the user's geographical location information into a generation AI and have the generation AI select optimal questions.

[0096] When creating practice questions, the creation unit can analyze the user's social media activity to create relevant questions. Social media activity includes, but is not limited to, post content, the number of likes, and comments. The creation unit can create relevant questions, for example, based on learning content shared by the user on social media. The creation unit can also create questions based on information about education-related accounts the user follows on social media. Furthermore, the creation unit can analyze the activities of study groups the user participates in on social media and create relevant questions. This enables efficient learning by creating relevant questions based on the user's social media activity. Some or all of the above-described processing by the creation unit can be performed using, or without, AI. For example, the creation unit can input data about the user's social media activity into a generation AI and cause the generation AI to create relevant questions.

[0097] When creating practice questions, the creation unit can customize the content of the questions by reflecting the user's past feedback. Examples of feedback include, but are not limited to, survey results and user comments. The creation unit customizes the content of the questions based on, for example, feedback provided by the user in the past. The creation unit can also improve questions that the user was dissatisfied with in the past and provide them again. Furthermore, the creation unit can customize new questions by referring to question formats that the user previously preferred. This allows for more effective practice questions to be provided by customizing the content of the questions based on the user's past feedback. Some or all of the above-described processing by the creation unit may be performed using, for example, AI, or may be performed without AI. For example, the creation unit can input data regarding the user's past feedback into the generation AI and have the generation AI customize the content of the questions.

[0098] The providing unit can estimate the user's emotions and adjust the explanation provision method based on the estimated user's emotions. Explanation provision methods include, but are not limited to, text explanations and video explanations. For example, the providing unit can provide detailed explanations when the user is relaxed. Furthermore, the providing unit can provide concise explanations when the user is stressed. Furthermore, the providing unit can provide explanations that emphasize important points when the user is concentrating. This allows for adjusting the explanation provision method according to the user's emotions, thereby providing explanations that are easier to understand. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the explanation provision method.

[0099] When providing an explanation, the providing unit can provide an optimal explanation by referring to the user's past learning history. Examples of past learning history include, but are not limited to, test results and homework grades. For example, the providing unit can provide relevant explanations based on explanations for problems the user has solved in the past. The providing unit can also provide explanations that focus on problems the user has previously struggled with. Furthermore, the providing unit can provide explanations to further deepen understanding by referring to explanations for problems the user has previously excelled at. This enables efficient learning by providing optimal explanations based on the user's past learning history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input data regarding the user's past learning history into the generating AI and cause the generating AI to provide optimal explanations.

[0100] When providing an explanation, the providing unit can customize the content of the explanation based on the user's current learning situation. The current learning situation includes, but is not limited to, for example, the user's current learning progress and learning time. The providing unit, for example, provides an appropriate explanation according to the user's current learning situation. The providing unit can also adjust the content of the explanation to match the user's current learning goal. Furthermore, the providing unit can provide a stepwise detailed explanation based on the user's current learning progress. This enables efficient learning by customizing the content of the explanation based on the user's current learning situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data regarding the user's current learning situation into the generating AI and cause the generating AI to customize the content of the explanation.

[0101] When providing an explanation, the providing unit can improve the content of the explanation by reflecting user feedback. Examples of feedback include, but are not limited to, survey results and user comments. The providing unit can adjust the content of the explanation based on, for example, feedback previously provided by the user. The providing unit can also improve an explanation that the user was dissatisfied with in the past and provide it again. Furthermore, the providing unit can provide a new explanation by referring to the format of explanations that the user previously preferred. This enables more effective learning by improving the content of the explanation based on user feedback. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input data regarding user feedback into a generating AI and cause the generating AI to improve the content of the explanation.

[0102] The providing unit can estimate the user's emotions and determine the priority of explanations based on the estimated user emotions. The priority of explanations can include, but is not limited to, the importance of the explanations and the user's level of understanding. For example, when the user is relaxed, the providing unit can prioritize providing detailed explanations. Furthermore, when the user is stressed, the providing unit can prioritize providing concise explanations. Furthermore, when the user is concentrating, the providing unit can prioritize important explanations. This enables efficient learning by determining the priority of explanations according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of explanations.

[0103] When providing explanations, the providing unit can provide optimal explanations by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user lives in a specific area, the providing unit can provide explanations related to the educational curriculum of that area. Furthermore, if the user attends a specific school, the providing unit can also provide explanations related to the school's curriculum. Furthermore, if the user plans to take an exam in a specific area, the providing unit can also provide explanations related to the exam. This enables efficient learning by providing optimal explanations based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input data related to the user's geographical location information into a generating AI and cause the generating AI to provide optimal explanations.

[0104] When providing commentary, the providing unit can analyze the user's social media activity and provide related commentary. Social media activity includes, but is not limited to, post content, the number of likes, and comments. For example, the providing unit can provide related commentary based on learning content shared by the user on social media. The providing unit can also provide commentary based on information about education-related accounts the user follows on social media. Furthermore, the providing unit can analyze the activity content of learning groups the user participates in on social media and provide related commentary. This enables efficient learning by providing related commentary based on the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input data about the user's social media activity into a generation AI and cause the generation AI to provide related commentary.

[0105] When providing an explanation, the providing unit can customize the content of the explanation by reflecting the user's past feedback. Examples of feedback include, but are not limited to, survey results and user comments. The providing unit customizes the content of the explanation based on, for example, feedback provided by the user in the past. The providing unit can also improve an explanation that the user was dissatisfied with in the past and provide it again. Furthermore, the providing unit can customize a new explanation by referring to the format of explanations that the user preferred in the past. This enables more effective learning by customizing the content of the explanation based on the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data regarding the user's past feedback into a generating AI and cause the generating AI to customize the content of the explanation. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, creation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's academic ability data using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and determines the user's academic ability and weak areas. The creation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates practice questions based on the determined weak areas. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides explanations for the created practice questions. Furthermore, the emotion estimation function, realized, for example, by the specific processing unit 290 of the data processing device 12, adjusts the timing of collecting academic ability data based on the user's emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, creation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's academic ability data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and determines the user's academic ability and weak areas. The creation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates practice questions based on the determined weak areas. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides explanations for the created practice questions. Furthermore, the emotion estimation function, realized, for example, by the specific processing unit 290 of the data processing device 12, adjusts the timing of collecting academic ability data based on the user's emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, creation unit, and provision unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the user's academic ability data using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and determines the user's academic ability and weak areas. The creation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates practice questions based on the determined weak areas. The provision unit, realized, for example, by the control unit 46A of the headset-type terminal 314, provides explanations for the created practice questions. Furthermore, the emotion estimation function, realized, for example, by the specific processing unit 290 of the data processing device 12, adjusts the timing of collecting academic ability data based on the user's emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, creation unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's academic ability data using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and determines the user's academic ability and weak areas. The creation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates practice questions based on the determined weak areas. The provision unit, realized, for example, by the control unit 46A of the robot 414, provides explanations for the created practice questions. Furthermore, the emotion estimation function, realized, for example, by the specific processing unit 290 of the data processing device 12, adjusts the timing of collecting academic ability data based on the user's emotions.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When analyzing a user's academic ability data, the analysis unit can adjust the analysis method taking into account the user's learning style. For example, a user with a visual learning style can be provided with analysis results that make extensive use of graphs and charts. A user with an auditory learning style can be provided with analysis results that include audio commentary. Furthermore, a user with a tactile learning style can be provided with interactive analysis tools. This allows for more effective learning support by providing analysis results that suit the user's learning style.

[0108] When collecting a user's academic ability data, the collection unit can adjust the data collection method taking into account the user's learning environment. For example, for a user who studies in a quiet environment, data can be collected using voice input. For a user who studies while on the move, data can be collected using a mobile device. Furthermore, for a user who studies in a group, data can be collected using a collaboration tool. In this way, by providing a data collection method that suits the user's learning environment, more accurate academic ability data can be collected.

[0109] When creating practice questions based on the user's academic ability data, the creation unit can adjust the content of the questions taking into account the user's learning goals. For example, a user with short-term goals can be provided with questions that directly lead to achieving those goals. A user with long-term goals can also be provided with step-by-step questions ranging from basic to advanced. Furthermore, a user studying for a specific exam can be provided with questions specialized for that exam. This makes it possible to provide effective learning support by providing practice questions that match the user's learning goals.

[0110] When providing explanations based on the user's academic ability data, the providing unit can adjust the method of providing the explanations taking into account the user's learning pace. For example, a user who studies at a fast pace can be provided with a concise explanation summarizing the main points. A user who studies at a slow pace can be provided with a detailed step-by-step explanation. Furthermore, a user who studies at a fluctuating pace can be provided with explanations that can be adjusted in real time. This allows for more effective learning support by providing explanations that match the user's learning pace.

[0111] When collecting a user's academic ability data, the collection unit can adjust the frequency of data collection taking into account the user's learning history. For example, for a user who studies frequently, data can be collected periodically. For a user who studies intermittently, data can also be collected in accordance with the timing of the user's studies. Furthermore, for a user who studies over a long period of time, long-term data collection can be performed. In this way, by providing a data collection frequency according to the user's learning history, more accurate academic ability data can be collected.

[0112] The analysis unit can estimate the user's emotions and adjust the method of providing feedback on the analysis results based on the estimated user emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is stressed, brief feedback can be provided. Furthermore, if the user is concentrating, feedback that emphasizes important points can be provided. This allows for more effective learning support by providing feedback according to the user's emotions.

[0113] The creation unit can estimate the user's emotions and adjust the format of the exercises based on the estimated user's emotions. For example, if the user is relaxed, detailed written questions can be provided. If the user is stressed, multiple-choice questions can be provided. Furthermore, if the user is concentrating, practical questions can be provided. This allows for more effective learning support by providing exercise formats that correspond to the user's emotions.

[0114] The providing unit can estimate the user's emotions and adjust the timing of providing commentary based on the estimated user's emotions. For example, if the user is relaxed, the unit can provide commentary immediately after studying. If the user is feeling stressed, the unit can provide commentary after a break. Furthermore, if the user is concentrating, the unit can provide commentary in real time while studying. This allows for more effective learning support by adjusting the timing of providing commentary according to the user's emotions.

[0115] The collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user's emotions. For example, if the user is relaxed, data can be collected using a detailed questionnaire. If the user is stressed, data can be collected in the form of a simple quiz. If the user is concentrating, data can be collected using a practical test. This allows for more accurate academic ability data to be collected by providing a data collection method that corresponds to the user's emotions.

[0116] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis results can be displayed using detailed graphs and charts. If the user is stressed, the analysis results can be displayed using a concise text report. Furthermore, if the user is concentrating, the analysis results can be displayed using an interactive dashboard. This allows for more effective learning support by providing a display method of the analysis results that corresponds to the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects the user's academic achievement data. The academic achievement data includes test results, homework grades, quiz scores, etc. The collection unit collects the user's past questions and test results, and can also scan online test results and paper-based test results to convert them into digital data. Step 2: The analysis unit analyzes the data collected by the collection unit and determines the user's academic ability and weak areas. The analysis is performed using statistical analysis and machine learning algorithms. For example, it analyzes the math problems and test results that the user has solved in the past to determine the user's math ability and weak areas. Step 3: The creation unit creates practice questions based on the weak areas determined by the analysis unit. The practice questions are created in the form of multiple-choice questions, written questions, practical questions, etc. For example, practice questions that focus on the user's weak areas may be generated, and the difficulty level of the practice questions may be adjusted to match the user's academic ability. Step 4: The providing unit provides explanations for the practice problems created by the creating unit. The explanations may be provided in the form of text explanations, video explanations, interactive explanations, etc. For example, for a calculus problem solved by a user, an explanation including solution steps and points to note is provided.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 collection unit that collects academic ability data of users; an analysis unit that analyzes the data collected by the collection unit and determines the user's academic ability or weak areas; a creation unit that creates practice questions based on the weak areas determined by the analysis unit; a providing unit that provides explanations for the exercises created by the creating unit; Equipped with A system characterized by:

2. The collecting unit Collecting past questions and test results from users 2. The system of claim 1.

3. The analysis unit Analyze the collected data to determine the user's academic ability and weak areas 2. The system of claim 1.

4. The creation unit Generate practice questions that focus on the user's weak areas 2. The system of claim 1.

5. The providing unit Provides detailed explanations for exercises 2. The system of claim 1.

6. The creation unit Adjust the difficulty of practice questions to suit the user's academic ability 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and clarify specific criteria for adjusting the timing of collecting academic achievement data based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze users' past learning history and select the appropriate data collection method 2. The system of claim 1.

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