system

Abstract

To provide a system that provides questions specialized in areas that a user is weak at, and appropriately adjusts a level of difficulty.SOLUTION: A system according to the embodiment comprises a receiving unit, a generation unit, a provision unit, and an analysis unit. The receiving unit inputs user's weak areas. The generation unit generates questions based on information input by the receiving unit. The provision unit provides the questions generated by the generation unit. The analysis unit analyzes the user's answers and adjusts a level of difficulty.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult to efficiently provide questions that are specialized in areas in which the user is weak and to appropriately adjust the level of difficulty.

[0005] The system according to the embodiment aims to provide questions that are specialized in areas that the user is weak at, and to adjust the level of difficulty appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, a providing unit, and an analyzing unit. The receiving unit inputs the user's weak areas. The generating unit generates questions based on the information input by the receiving unit. The providing unit provides the questions generated by the generating unit. The analyzing unit analyzes the user's answers and adjusts the difficulty level. [Effects of the Invention]

[0007] The system according to the embodiment can provide questions that are specialized in areas that the user is weak at, and can adjust the level of difficulty appropriately. [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 mathematics learning support system according to an embodiment of the present invention identifies areas in which a user is weak and generates problems within those areas. In this system, the user inputs their weak areas, and a generation AI generates problems based on those areas. If the user is unable to solve the generated problems, the difficulty level is adjusted and the problems are regenerated. The problems also include an introductory section to help the user understand the problem more easily. For example, the user inputs the areas in which the user is weak. For example, the user specifies a "specific area of ​​mathematics." This information is input to the generation AI. The generation AI then generates problems based on the input area. The generation AI creates problems within the specified area and provides them to the user. For example, if the area is a "specific area of ​​mathematics," a problem within that area is generated. If the user is unable to solve the generated problems, the difficulty level is adjusted and the problems are regenerated. The generation AI analyzes the user's answers and adjusts the difficulty of the problems. For example, if the user is unable to solve a problem, an easier problem is generated to help the user solve it. The problems also include an introductory section to help the user understand the problem more easily. The generation AI adds an introductory section before the problem to help the user understand the background of the problem and how to solve it. For example, a specific math problem may include an introductory section that explains the basic concepts and steps for solving the problem. This allows the user to efficiently study areas in which they are weak and deepen their understanding of mathematics. This allows the math learning support system to identify areas in which the user is weak and generate and provide problems in those areas.

[0029] A mathematics learning support system according to an embodiment includes a receiving unit, a generating unit, a providing unit, and an analyzing unit. The receiving unit inputs a user's weak areas. The user's weak areas include, for example, a specific range or topic of mathematics, but are not limited to such examples. The receiving unit, for example, provides an interface for the user to input the specific range of mathematics. The generating unit generates questions based on the information input by the receiving unit using a generation AI. The generation AI generates questions using, for example, a text generation AI (e.g., LLM). The generating unit can also use the generation AI to generate questions based on a specific range of mathematics. For example, the generation AI uses an algorithm for generating questions related to a specific range of mathematics. The providing unit provides the questions generated by the generating unit. For example, the providing unit provides an interface for displaying the generated questions to the user. The providing unit can also add an introduction to the generated questions. For example, the providing unit adds an introduction that explains the background of the question and the solution method. The analyzing unit analyzes the user's answers and adjusts the difficulty level. For example, the analyzing unit uses an algorithm for analyzing the user's answers. The analysis unit may also be associated with a storage unit that stores the user's answer history. For example, the analysis unit may store the user's answer history to improve the accuracy of the analysis. This allows the mathematics learning support system according to the embodiment to identify areas in which the user is weak and generate and provide problems in those areas.

[0030] The reception unit inputs the user's weak areas. The user's weak areas include, but are not limited to, specific mathematical areas or topics. The reception unit provides, for example, an interface for the user to input the specific mathematical areas. Specifically, the reception unit has a graphical user interface (GUI) that the user can intuitively operate, allowing the user to select the weak areas using drop-down menus and check boxes. The user can also freely input specific topics and question formats using text boxes. Furthermore, the reception unit also has a function to automatically estimate the user's weak areas by referencing the user's past learning history and performance data. For example, by analyzing past test results and practice question answer history, if the user's accuracy rate in a specific area is low, the reception unit can automatically set that area as the weak area. This saves the user the trouble of accurately entering their weak areas and allows for more efficient study. The reception unit saves the information entered by the user in real time and works with the generation unit and analysis unit to optimize the operation of the entire system. For example, if the user adds a new weak area, the information is immediately sent to the generation unit, and the generated questions are updated. This allows the reception unit to quickly respond to the user's needs and provide personalized learning support.

[0031] The generation unit uses a generation AI to generate questions based on the information entered by the reception unit. The generation AI generates questions using, for example, a text generation AI (e.g., LLM). The generation unit can also use the generation AI to generate questions based on a specific mathematical field. Specifically, the generation AI uses natural language processing technology to automatically generate questions related to the user's weak areas. For example, if a user inputs that they are weak in "calculus," the generation AI generates questions of a wide range of difficulty, from basic to applied, related to that field. The generation AI references a database of past questions and uses an algorithm to generate similar questions, providing users with questions that are new but easy to understand. The generation AI can also diversify the question format and question presentation method. For example, by generating various question formats, such as multiple-choice questions, essay questions, and questions using graphs and diagrams, the user's understanding can be evaluated from multiple perspectives. Furthermore, the generation unit also has the ability to dynamically adjust the difficulty of questions based on the user's learning progress and answer history. For example, if the user answers correctly consecutively, the difficulty of the next question will be increased, while if the user answers incorrectly consecutively, the difficulty will be decreased, providing an appropriate learning load. In this way, the generation unit can maximize the learning effect of the user and realize efficient learning support.

[0032] The providing unit provides the problems generated by the generating unit. For example, the providing unit provides an interface for displaying the generated problems to the user. The providing unit can also add introductory sections to the generated problems. Specifically, the providing unit has a function for displaying text and diagrams to provide the user with necessary information and background knowledge when tackling the problems. For example, when solving a calculus problem, adding an explanation of basic theorems and formulas as an introductory section makes the problem easier for the user to understand. The providing unit also has a function for providing solutions and hints to the problem step by step. If the user experiences difficulty while tackling the problem, the providing unit displays hints at the appropriate time to support the user's understanding. Furthermore, the providing unit has a function for evaluating the user's answers in real time and providing immediate feedback. For example, the providing unit can determine whether the answer is correct immediately after the user enters it, prompting the user to move on to the next question if the answer is correct, or displaying an explanation and encouraging the user to try again if the answer is incorrect. This allows the user to instantly check their level of understanding and efficiently progress through their studies. The providing unit saves the user's learning history and answer history and works with the analysis unit to optimize the learning content. For example, by providing questions that allow users to review questions that they got wrong in the past or areas that they are weak at, the effectiveness of the user's learning can be improved. This allows the providing unit to provide individualized learning support to the user and support effective learning.

[0033] The analysis unit analyzes the user's answers and adjusts the difficulty level. The analysis unit, for example, uses an algorithm to analyze the user's answers. The analysis unit can also be associated with a storage unit that stores the user's answer history. Specifically, the analysis unit analyzes the user's answer data in detail and evaluates the correct answer rate, answer time, answer patterns, etc. This allows the user's level of understanding and learning progress to be accurately grasped. The analysis unit dynamically adjusts the difficulty level of the next question based on this data. For example, if the user shows a high correct answer rate in a specific range, the difficulty level of questions in that range is increased, while if the user shows a low correct answer rate, the difficulty level is decreased, thereby providing an appropriate learning load. The analysis unit also stores the user's answer history and builds a database for evaluating long-term learning effectiveness. This allows the analysis unit to propose an individualized study plan based on the user's learning history. For example, by providing questions that focus on reviewing questions that the user previously answered incorrectly or weak areas, the user's learning effectiveness can be improved. Furthermore, the analysis unit can aggregate the user's answer data and perform statistical analysis to evaluate overall learning trends and the distribution of question difficulty levels. This can be useful for improving the problem generation algorithm and presentation method of the entire system. The analysis unit also plays a role in providing detailed feedback to users to increase their motivation to study. For example, by providing advice on the process and thinking behind the answer, rather than just the correctness of the answer, the analysis unit can deepen the user's understanding. This allows the analysis unit to maximize the user's learning effect and provide efficient learning support.

[0034] The generation unit can generate questions using a generation AI. The generation unit generates questions using, for example, a generation AI. The generation AI generates questions using, for example, a text generation AI (e.g., LLM). The generation unit can also use the generation AI to generate questions based on a specific mathematical field. For example, the generation AI uses an algorithm for generating questions related to a specific mathematical field. In this way, question generation is automated by using the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a question generation prompt to the generation AI and output the generated question.

[0035] The generator can generate problems based on a specific mathematical field. For example, the generator generates problems based on a specific mathematical field. Examples of the specific mathematical field include, but are not limited to, algebra, geometry, and calculus. The generator uses, for example, an algorithm for generating problems related to a specific mathematical field. This allows problems to be generated based on the specific field, thereby providing problems tailored to the user's needs. Some or all of the above-described processing in the generator may be performed using, for example, AI, or may be performed without using AI. For example, the generator can input a prompt for generating a problem related to a specific mathematical field to a generation AI and output the generated problem.

[0036] The providing unit can add an introductory portion to the generated problem. For example, the providing unit adds an introductory portion to the generated problem. The introductory portion includes, for example, a background explanation of the problem and a hint on how to solve it, but is not limited to such examples. For example, the providing unit adds an introductory portion that explains the background of the problem and the solution. By adding the introductory portion, the user can more easily understand the background of the problem and the solution. 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 a prompt to the generating AI to add an introductory portion to the generated problem, and output the generated introductory portion.

[0037] The analysis unit can analyze the user's answers and adjust the difficulty level. The analysis unit, for example, analyzes the user's answers. The analysis unit, for example, uses an algorithm to analyze the user's answers. The analysis unit can also be associated with a storage unit that stores the user's answer history. For example, the analysis unit stores the user's answer history to improve the accuracy of the analysis. This makes it possible to provide questions of appropriate difficulty by adjusting the difficulty level based on the user's answers. 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 the user's answer data into a generation AI and output the analysis results.

[0038] The analysis unit may be associated with a storage unit that stores the user's answer history. The analysis unit may be associated with, for example, a storage unit that stores the user's answer history. The storage unit may store, for example, the user's answer history. The answer history may include, for example, but is not limited to, whether the answer was correct or incorrect, the answer time, and details of the answer. The storage unit may, for example, store the user's answer history to improve the accuracy of the analysis. Thus, by storing the answer history, the accuracy of the analysis is improved. Some or all of the above-described processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit may input the user's answer history data into a generation AI to optimize the storage algorithm.

[0039] The reception unit can analyze the user's past learning history and automatically suggest weak areas. The reception unit, for example, analyzes the user's past learning history. The learning history includes, for example, past learning content, study time, learning results, etc., but is not limited to these examples. The reception unit, for example, analyzes the user's past learning history and automatically suggests weak areas. For example, the reception unit can analyze the tendency of questions the user has gotten wrong in the past and suggest weak areas. The reception unit can also evaluate the user's understanding of specific areas from the user's past learning history and automatically display weak areas. It can also identify areas the user has avoided in the past and suggest those areas. In this way, the user's weak areas can be automatically identified by analyzing the past learning history. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's learning history data into a generation AI and have the generation AI suggest weak areas.

[0040] The reception unit can guide the user in entering weak areas based on the user's current learning progress. The reception unit, for example, evaluates the user's current learning progress. Learning progress includes, but is not limited to, current learning content, progress status, and achievement level. The reception unit, for example, guides the user in entering weak areas based on the user's current learning progress. For example, the reception unit can suggest related weak areas based on the area the user is currently studying. The reception unit can also evaluate the user's current learning progress and guide the user to the weak areas to study next. The reception unit can also prompt the user to enter appropriate weak areas based on the difficulty level of the problem the user is currently working on. This allows the user to enter appropriate areas by guiding the input based on the current learning progress. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's learning progress data into a generation AI and cause the generation AI to execute input guidance.

[0041] The reception unit can propose region-specific study tasks by taking into account the user's geographical location information. The reception unit, for example, considers the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, and area code. The reception unit, for example, considers the user's geographical location information to propose region-specific study tasks. For example, the reception unit can propose specific study tasks based on the educational curriculum of the area where the user lives. The reception unit can also propose local educational events and resources based on the user's geographical location information. The reception unit can also propose appropriate study tasks according to the educational level of the area where the user lives. In this way, region-specific study tasks can be proposed by taking into account the geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to suggest study tasks.

[0042] The reception unit can analyze the user's social media activity and suggest related areas of weakness. The reception unit, for example, analyzes the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The reception unit, for example, analyzes the user's social media activity and suggests related areas of weakness. For example, the reception unit can analyze the learning content shared by the user on social media and suggest areas of weakness. The reception unit can also suggest related areas of weakness based on the activity of the user's learning community on social media. The reception unit can also analyze the content of educational accounts the user follows on social media and suggest areas of weakness. In this way, the user's areas of weakness can be identified by analyzing social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest areas of weakness.

[0043] The generation unit can customize the question format according to the user's learning style at the time of generation. For example, the generation unit customizes the question format according to the user's learning style at the time of generation. Learning styles include, but are not limited to, visual, auditory, and experiential. For example, if the user is a visual learner, the generation unit can generate questions that make extensive use of diagrams and graphs. Also, if the user is an auditory learner, the generation unit can generate questions that include audio explanations. Also, if the user is an experiential learner, the generation unit can generate practical exercises. This enables more effective learning by customizing the question format according to the learning style. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's learning style data into the generation AI and cause the generation AI to customize the question format.

[0044] The generation unit can generate more effective questions by referring to past question generation history during generation. For example, the generation unit references past question generation history during generation. The question generation history includes, for example, previously generated questions, the generation date and time, and generation conditions, but is not limited to these examples. For example, the generation unit analyzes trends in questions previously solved by the user and generates similar questions. It can also generate questions with adjusted difficulty based on questions that the user was unable to solve in the past. It can also generate questions of appropriate difficulty by taking into account the time it took the user to solve questions previously solved. By referring to the past question generation history, more effective questions can be provided. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input past question generation history data into the generation AI and cause the generation AI to optimize question generation.

[0045] The generation unit can adjust the content of the questions based on the user's learning environment during generation. For example, the generation unit adjusts the content of the questions based on the user's learning environment during generation. The learning environment includes, but is not limited to, the learning location, the device used, and the ambient noise level. For example, if the user is learning in a quiet environment, the generation unit can generate questions that require concentration. Also, if the user is learning in a noisy environment, the generation unit can generate questions that can be solved in a short time. Also, if the user is learning while on the move, the generation unit can generate questions optimized for mobile devices. By adjusting the content of the questions based on the learning environment, more appropriate questions can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's learning environment data into the generation AI and cause the generation AI to adjust the content of the questions.

[0046] The generation unit can select the theme of the question based on the user's interests at the time of generation. For example, the generation unit selects the theme of the question based on the user's interests at the time of generation. Interests include, but are not limited to, hobbies, topics of interest, and past learning history. For example, if the user is interested in sports, the generation unit can generate sports-related questions. Also, if the user is interested in music, the generation unit can generate music-related questions. Also, if the user is interested in science, the generation unit can generate science-related questions. In this way, selecting the theme of the question based on the user's interests can increase motivation to learn. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's interest data into the generation AI and cause the generation AI to select the theme of the question.

[0047] The providing unit can provide questions at the optimal timing by referring to the user's learning history when providing the questions. For example, the providing unit can refer to the user's learning history when providing the questions. The learning history includes, for example, past learning content, learning time, learning results, etc., but is not limited to these examples. For example, the providing unit can provide questions at the optimal timing based on the time periods in which the user studied in the past. Furthermore, the providing unit can provide questions at times when the user's concentration is highest based on the user's learning history. Furthermore, the providing unit can provide questions at the appropriate timing by taking into account the answer times of questions the user previously answered. In this way, by referring to the learning history, questions can be provided at the optimal timing. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's learning history data into the generation AI and cause the generation AI to optimize the timing of providing the questions.

[0048] The providing unit can provide questions in an optimal format by taking into account the user's device information when providing the questions. For example, the providing unit can consider the user's device information when providing the questions. Device information includes, but is not limited to, the device type, screen size, and OS version. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Also, if the user is using a PC, the providing unit can provide a display method that includes detailed information. This allows questions to be provided in an optimal format by taking into account the device information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to optimize the display format.

[0049] The providing unit may provide region-specific questions by taking into account the user's geographical location information. For example, the providing unit may consider the user's geographical location information when providing the questions. Examples of geographical location information include, but are not limited to, GPS data, IP address, and area code. For example, the providing unit may provide specific questions based on the educational curriculum of the area where the user lives. The providing unit may also provide local educational events and resources based on the user's geographical location information. Appropriate questions may also be provided depending on the educational level of the area where the user lives. This allows region-specific questions to be provided by taking into account the geographical location information. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input the user's geographical location information data into a generation AI and cause the generation AI to provide region-specific questions.

[0050] The providing unit can analyze the user's social media activity and provide relevant questions at the time of providing. For example, the providing unit can analyze the user's social media activity at the time of providing. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the providing unit can analyze learning content shared by the user on social media and provide relevant questions. The providing unit can also provide relevant questions based on the activity of the user's learning community on social media. The providing unit can also analyze the content of educational accounts the user follows on social media and provide relevant questions. In this way, relevant questions can be provided by analyzing social media activity. Some or all of the above-described processing by the providing unit can be performed, for example, using AI or without using AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to provide relevant questions.

[0051] The analysis unit can improve the accuracy of the analysis by referring to the user's past answer history during analysis. For example, the analysis unit can refer to the user's past answer history during analysis. The answer history includes, for example, correct / incorrect answers, answer time, and answer details, but is not limited to these examples. The analysis unit can, for example, analyze trends in questions the user has previously answered to improve the accuracy of the analysis. It can also identify specific error patterns from the user's past answer history and reflect them in the analysis. It can also improve the accuracy of the analysis by taking into account the answer time of questions the user has previously answered. By referring to the past answer history, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's past answer history data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0052] The analysis unit can customize the analysis algorithm according to the user's learning style during analysis. For example, the analysis unit customizes the analysis algorithm according to the user's learning style during analysis. Learning styles include, but are not limited to, visual, auditory, and experiential learning. For example, if the user is a visual learner, the analysis unit can provide analysis results that make extensive use of diagrams and graphs. Furthermore, if the user is an auditory learner, the analysis unit can provide analysis results that include audio explanations. Furthermore, if the user is an experiential learner, the analysis unit can provide analysis results that include practical exercises. This enables more effective analysis by customizing the analysis algorithm according to the learning style. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's learning style data into the generation AI and have the generation AI customize the analysis algorithm.

[0053] The analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information during analysis. For example, the analysis unit can take into account the user's geographical location information during analysis. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and area codes. For example, the analysis unit can improve the accuracy of the analysis based on the educational curriculum of the area where the user lives. Furthermore, the analysis unit can also perform analysis according to the educational level of the area based on the user's geographical location information. Furthermore, the analysis accuracy can be improved by taking into account educational events and resources in the area where the user lives. Thus, by taking into account the geographical location information, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's geographical location information data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0054] The analysis unit may analyze the user's social media activity during the analysis and provide related analysis results. For example, the analysis unit may analyze the user's social media activity during the analysis. Social media activity may include, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the analysis unit may analyze learning content shared by the user on social media and provide related analysis results. The analysis unit may also provide related analysis results based on the activity of the user's learning community on social media. The analysis unit may also analyze the content of educational accounts the user follows on social media and provide related analysis results. In this way, the analysis unit may provide related analysis results by analyzing social media activity. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the user's social media data into a generation AI and cause the generation AI to provide analysis results.

[0055] The storage unit can optimize the storage algorithm by referring to previously saved data when saving data. The storage unit, for example, refers to previously saved data when saving data. Saved data includes, for example, the type of data previously saved, the save date and time, and the save conditions, but is not limited to these examples. The storage unit, for example, analyzes trends in data previously saved by the user and proposes an optimal save method. It can also identify specific patterns from the user's previously saved data and optimize the save algorithm. It can also evaluate the importance of data previously saved by the user and propose an optimal save method. In this way, the save algorithm can be optimized by referring to the previously saved data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input previously saved data to a generation AI and cause the generation AI to optimize the save algorithm.

[0056] The storage unit may weight the stored data taking into account the user's geographical location information when storing the data. For example, the storage unit may consider the user's geographical location information when storing the data. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and area codes. For example, the storage unit may weight the stored data based on the educational curriculum of the area where the user lives. The storage unit may also weight the stored data according to the educational level of the area based on the user's geographical location information. The storage unit may also weight the stored data taking into account educational events and resources in the area where the user lives. In this way, the weighting of the stored data can be optimized by taking into account the geographical location information. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit may input the user's geographical location information data to a generation AI and cause the generation AI to weight the stored data.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The reception unit can automatically identify areas where the user has had difficulty in the past based on the user's learning history and assist with input. For example, it can analyze the accuracy rate and answer time of questions the user has answered in the past to evaluate the level of difficulty in specific areas. It can also identify areas that the user has avoided in the past and suggest those areas preferentially. Furthermore, it can evaluate the user's level of understanding of specific topics based on the user's learning history and automatically display areas where the user has difficulty. This makes it possible to more accurately identify areas where the user has difficulty and assist with input by utilizing the user's past learning history.

[0059] The generator can customize the question format according to the user's learning style. For example, it can generate questions that make extensive use of diagrams and graphs for visual learners, and questions that include audio explanations for auditory learners. It can also generate practical exercises for experiential learners. Furthermore, it can adjust the way questions are presented and the format of feedback based on the user's learning style. This allows it to provide the user with questions that are optimal for their learning style, improving learning effectiveness.

[0060] The generator can select the theme of the questions based on the user's interests. For example, if the user is interested in sports, it can generate questions related to sports, and if the user is interested in music, it can generate questions related to music. If the user is interested in science, it can also generate questions related to science. Furthermore, it can analyze the user's past learning history and social media activity to provide questions based on their interests. This can provide questions that match the user's interests and increase their motivation to learn.

[0061] The providing unit can provide questions in the optimal format taking into account the user's device information. For example, if the user is using a smartphone, a display method tailored to the screen size is provided, and if the user is using a tablet, a display method optimized for a large screen is provided. Also, if the user is using a PC, a display method including detailed information can be provided. Furthermore, questions can be provided in the optimal format depending on the OS version and usage environment of the user's device. This makes it possible to provide questions in the optimal format taking into account the user's device information.

[0062] The analysis unit can improve the accuracy of the analysis by referring to the user's past answer history. For example, it can analyze the correct answers and answer times of questions the user has answered in the past to identify specific mistake patterns. It can also evaluate the user's understanding of a specific topic from the user's past answer history and reflect this in the analysis. It can also analyze the trends in questions the user has answered in the past and optimize the analysis algorithm. In this way, the accuracy of the analysis can be improved by referring to the past answer history.

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

[0064] Step 1: The reception unit inputs the user's weak areas. The user's weak areas include, but are not limited to, specific mathematical areas or topics. The reception unit provides, for example, an interface for the user to input the specific mathematical areas. Step 2: The generation unit uses a generation AI to generate questions based on the information input by the reception unit. The generation AI generates questions using, for example, a text generation AI (e.g., LLM). The generation unit can also use the generation AI to generate questions based on a specific mathematical field. For example, the generation AI uses an algorithm for generating questions related to a specific mathematical field. Step 3: The providing unit provides the problem generated by the generating unit. For example, the providing unit provides an interface for displaying the generated problem to the user. The providing unit can also add an introductory section to the generated problem. For example, the providing unit adds an introductory section that explains the background of the problem and the solution method. Step 4: The analysis unit analyzes the user's answers and adjusts the difficulty level. The analysis unit, for example, uses an algorithm to analyze the user's answers. The analysis unit may also be associated with a storage unit that stores the user's answer history. For example, the analysis unit stores the user's answer history to improve the accuracy of the analysis.

[0065] (Example 2) A mathematics learning support system according to an embodiment of the present invention identifies areas in which a user is weak and generates problems within those areas. In this system, the user inputs their weak areas, and a generation AI generates problems based on those areas. If the user is unable to solve the generated problems, the difficulty level is adjusted and the problems are regenerated. The problems also include an introductory section to help the user understand the problem more easily. For example, the user inputs the areas in which the user is weak. For example, the user specifies a "specific area of ​​mathematics." This information is input to the generation AI. The generation AI then generates problems based on the input area. The generation AI creates problems within the specified area and provides them to the user. For example, if the area is a "specific area of ​​mathematics," a problem within that area is generated. If the user is unable to solve the generated problems, the difficulty level is adjusted and the problems are regenerated. The generation AI analyzes the user's answers and adjusts the difficulty of the problems. For example, if the user is unable to solve a problem, an easier problem is generated to help the user solve it. The problems also include an introductory section to help the user understand the problem more easily. The generation AI adds an introductory section before the problem to help the user understand the background of the problem and how to solve it. For example, a specific math problem may include an introductory section that explains the basic concepts and steps for solving the problem. This allows the user to efficiently study areas in which they are weak and deepen their understanding of mathematics. This allows the math learning support system to identify areas in which the user is weak and generate and provide problems in those areas.

[0066] A mathematics learning support system according to an embodiment includes a receiving unit, a generating unit, a providing unit, and an analyzing unit. The receiving unit inputs a user's weak areas. The user's weak areas include, for example, a specific range or topic of mathematics, but are not limited to such examples. The receiving unit, for example, provides an interface for the user to input the specific range of mathematics. The generating unit generates questions based on the information input by the receiving unit using a generation AI. The generation AI generates questions using, for example, a text generation AI (e.g., LLM). The generating unit can also use the generation AI to generate questions based on a specific range of mathematics. For example, the generation AI uses an algorithm for generating questions related to a specific range of mathematics. The providing unit provides the questions generated by the generating unit. For example, the providing unit provides an interface for displaying the generated questions to the user. The providing unit can also add an introduction to the generated questions. For example, the providing unit adds an introduction that explains the background of the question and the solution method. The analyzing unit analyzes the user's answers and adjusts the difficulty level. For example, the analyzing unit uses an algorithm for analyzing the user's answers. The analysis unit may also be associated with a storage unit that stores the user's answer history. For example, the analysis unit may store the user's answer history to improve the accuracy of the analysis. This allows the mathematics learning support system according to the embodiment to identify areas in which the user is weak and generate and provide problems in those areas.

[0067] The reception unit inputs the user's weak areas. The user's weak areas include, but are not limited to, specific mathematical areas or topics. The reception unit provides, for example, an interface for the user to input the specific mathematical areas. Specifically, the reception unit has a graphical user interface (GUI) that the user can intuitively operate, allowing the user to select the weak areas using drop-down menus and check boxes. The user can also freely input specific topics and question formats using text boxes. Furthermore, the reception unit also has a function to automatically estimate the user's weak areas by referencing the user's past learning history and performance data. For example, by analyzing past test results and practice question answer history, if the user's accuracy rate in a specific area is low, the reception unit can automatically set that area as the weak area. This saves the user the trouble of accurately inputting their weak areas and allows for more efficient study. The reception unit saves the information entered by the user in real time and works with the generation unit and analysis unit to optimize the operation of the entire system. For example, if the user adds a new weak area, the information is immediately sent to the generation unit, and the generated questions are updated. This allows the reception unit to quickly respond to the user's needs and provide personalized learning support.

[0068] The generation unit uses a generation AI to generate questions based on the information entered by the reception unit. The generation AI generates questions using, for example, a text generation AI (e.g., LLM). The generation unit can also use the generation AI to generate questions based on a specific mathematical field. Specifically, the generation AI uses natural language processing technology to automatically generate questions related to the user's weak areas. For example, if a user inputs that they are weak in "calculus," the generation AI generates questions of a wide range of difficulty, from basic to applied, related to that field. The generation AI references a database of past questions and uses an algorithm to generate similar questions, providing users with questions that are new but easy to understand. The generation AI can also diversify the question format and question presentation method. For example, by generating various question formats, such as multiple-choice questions, essay questions, and questions using graphs and diagrams, the user's understanding can be evaluated from multiple perspectives. Furthermore, the generation unit also has the ability to dynamically adjust the difficulty of questions based on the user's learning progress and answer history. For example, if the user answers correctly consecutively, the difficulty of the next question will be increased, while if the user answers incorrectly consecutively, the difficulty will be decreased, providing an appropriate learning load. In this way, the generation unit can maximize the learning effect of the user and realize efficient learning support.

[0069] The providing unit provides the problems generated by the generating unit. For example, the providing unit provides an interface for displaying the generated problems to the user. The providing unit can also add introductory sections to the generated problems. Specifically, the providing unit has a function for displaying text and diagrams to provide the user with necessary information and background knowledge when tackling the problems. For example, when solving a calculus problem, adding an explanation of basic theorems and formulas as an introductory section makes the problem easier for the user to understand. The providing unit also has a function for providing solutions and hints to the problem step by step. If the user experiences difficulty while tackling the problem, the providing unit displays hints at the appropriate time to support the user's understanding. Furthermore, the providing unit has a function for evaluating the user's answers in real time and providing immediate feedback. For example, the providing unit can determine whether the answer is correct immediately after the user enters it, prompting the user to move on to the next question if the answer is correct, or displaying an explanation and encouraging the user to try again if the answer is incorrect. This allows the user to instantly check their level of understanding and efficiently progress through their studies. The providing unit saves the user's learning history and answer history and works with the analysis unit to optimize the learning content. For example, by providing questions that allow users to review questions that they got wrong in the past or areas that they are weak at, the effectiveness of the user's learning can be improved. This allows the providing unit to provide individualized learning support to the user and support effective learning.

[0070] The analysis unit analyzes the user's answers and adjusts the difficulty level. The analysis unit, for example, uses an algorithm to analyze the user's answers. The analysis unit can also be associated with a storage unit that stores the user's answer history. Specifically, the analysis unit analyzes the user's answer data in detail and evaluates the correct answer rate, answer time, answer patterns, etc. This allows the user's level of understanding and learning progress to be accurately grasped. The analysis unit dynamically adjusts the difficulty level of the next question based on this data. For example, if the user shows a high correct answer rate in a specific range, the difficulty level of questions in that range is increased, while if the user shows a low correct answer rate, the difficulty level is decreased, thereby providing an appropriate learning load. The analysis unit also stores the user's answer history and builds a database for evaluating long-term learning effectiveness. This allows the analysis unit to propose an individualized study plan based on the user's learning history. For example, by providing questions that focus on reviewing questions that the user previously answered incorrectly or weak areas, the user's learning effectiveness can be improved. Furthermore, the analysis unit can aggregate the user's answer data and perform statistical analysis to evaluate overall learning trends and the distribution of question difficulty levels. This can be useful for improving the problem generation algorithm and presentation method of the entire system. The analysis unit also plays a role in providing detailed feedback to users to increase their motivation to study. For example, by providing advice on the process and thinking behind the answer, rather than just the correctness of the answer, the analysis unit can deepen the user's understanding. This allows the analysis unit to maximize the user's learning effect and provide efficient learning support.

[0071] The generation unit can generate questions using a generation AI. The generation unit generates questions using, for example, a generation AI. The generation AI generates questions using, for example, a text generation AI (e.g., LLM). The generation unit can also use the generation AI to generate questions based on a specific mathematical field. For example, the generation AI uses an algorithm for generating questions related to a specific mathematical field. In this way, question generation is automated by using the generation AI. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a question generation prompt to the generation AI and output the generated question.

[0072] The generator can generate problems based on a specific mathematical field. For example, the generator generates problems based on a specific mathematical field. Examples of the specific mathematical field include, but are not limited to, algebra, geometry, and calculus. The generator uses, for example, an algorithm for generating problems related to a specific mathematical field. This allows problems to be generated based on the specific field, thereby providing problems tailored to the user's needs. Some or all of the above-described processing in the generator may be performed using, for example, AI, or may be performed without using AI. For example, the generator can input a prompt for generating a problem related to a specific mathematical field to a generation AI and output the generated problem.

[0073] The providing unit can add an introductory portion to the generated problem. For example, the providing unit adds an introductory portion to the generated problem. The introductory portion includes, for example, a background explanation of the problem and a hint on how to solve it, but is not limited to such examples. For example, the providing unit adds an introductory portion that explains the background of the problem and the solution. By adding the introductory portion, the user can more easily understand the background of the problem and the solution. 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 a prompt to the generating AI to add an introductory portion to the generated problem, and output the generated introductory portion.

[0074] The analysis unit can analyze the user's answers and adjust the difficulty level. The analysis unit, for example, analyzes the user's answers. The analysis unit, for example, uses an algorithm to analyze the user's answers. The analysis unit can also be associated with a storage unit that stores the user's answer history. For example, the analysis unit stores the user's answer history to improve the accuracy of the analysis. This makes it possible to provide questions of appropriate difficulty by adjusting the difficulty level based on the user's answers. 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 the user's answer data into a generation AI and output the analysis results.

[0075] The analysis unit may be associated with a storage unit that stores the user's answer history. The analysis unit may be associated with, for example, a storage unit that stores the user's answer history. The storage unit may store, for example, the user's answer history. The answer history may include, for example, but is not limited to, whether the answer was correct or incorrect, the answer time, and details of the answer. The storage unit may, for example, store the user's answer history to improve the accuracy of the analysis. Thus, by storing the answer history, the accuracy of the analysis is improved. Some or all of the above-described processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit may input the user's answer history data into a generation AI to optimize the storage algorithm.

[0076] The reception unit can estimate the user's emotions and adjust the input method for the difficult range based on the estimated user emotions. The reception unit, for example, estimates the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, with 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. The reception unit, for example, estimates the user's emotions and adjusts the input method for the difficult range based on the estimated user emotions. For example, if the user is stressed, a simple interface can be provided to minimize input steps. Also, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Also, if the user is in a hurry, voice input can be prioritized to enable quick input of the difficult range. This allows more appropriate input by adjusting the input method according to the user's emotions. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the input method.

[0077] The reception unit can analyze the user's past learning history and automatically suggest weak areas. The reception unit, for example, analyzes the user's past learning history. The learning history includes, for example, past learning content, study time, learning results, etc., but is not limited to these examples. The reception unit, for example, analyzes the user's past learning history and automatically suggests weak areas. For example, the reception unit can analyze the tendency of questions the user has gotten wrong in the past and suggest weak areas. The reception unit can also evaluate the user's understanding of specific areas from the user's past learning history and automatically display weak areas. It can also identify areas the user has avoided in the past and suggest those areas. In this way, the user's weak areas can be automatically identified by analyzing the past learning history. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's learning history data into a generation AI and have the generation AI suggest weak areas.

[0078] The reception unit can guide the user in entering weak areas based on the user's current learning progress. The reception unit, for example, evaluates the user's current learning progress. Learning progress includes, but is not limited to, current learning content, progress status, and achievement level. The reception unit, for example, guides the user in entering weak areas based on the user's current learning progress. For example, the reception unit can suggest related weak areas based on the area the user is currently studying. The reception unit can also evaluate the user's current learning progress and guide the user to the weak areas to study next. The reception unit can also prompt the user to enter appropriate weak areas based on the difficulty level of the problem the user is currently working on. This allows the user to enter appropriate areas by guiding the input based on the current learning progress. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's learning progress data into a generation AI and cause the generation AI to execute input guidance.

[0079] The reception unit can estimate the user's emotion and adjust the design of the input interface based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, with 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. The reception unit, for example, estimates the user's emotion and adjusts the design of the input interface based on the estimated user's emotion. For example, if the user is nervous, the reception unit can provide an interface with calm colors to reduce visual stress. If the user is enjoying themselves, the reception unit can provide an interface with bright colors to make input work more enjoyable. If the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This allows for more comfortable input by adjusting the interface design according to the user's emotion. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the interface design.

[0080] The reception unit can propose region-specific study tasks by taking into account the user's geographical location information. The reception unit, for example, considers the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, and area code. The reception unit, for example, considers the user's geographical location information to propose region-specific study tasks. For example, the reception unit can propose specific study tasks based on the educational curriculum of the area where the user lives. The reception unit can also propose local educational events and resources based on the user's geographical location information. The reception unit can also propose appropriate study tasks according to the educational level of the area where the user lives. In this way, region-specific study tasks can be proposed by taking into account the geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to suggest study tasks.

[0081] The reception unit can analyze the user's social media activity and suggest related areas of weakness. The reception unit, for example, analyzes the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The reception unit, for example, analyzes the user's social media activity and suggests related areas of weakness. For example, the reception unit can analyze the learning content shared by the user on social media and suggest areas of weakness. The reception unit can also suggest related areas of weakness based on the activity of the user's learning community on social media. The reception unit can also analyze the content of educational accounts the user follows on social media and suggest areas of weakness. In this way, the user's areas of weakness can be identified by analyzing social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest areas of weakness.

[0082] The generation unit can estimate the user's emotions and adjust the way questions are presented based on the estimated user emotions. The generation unit, for example, estimates the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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. The generation unit, for example, estimates the user's emotions and adjusts the way questions are presented based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate questions that proceed at a leisurely pace. If the user is in a hurry, the generation unit can generate questions that are concise and to the point. If the user is excited, the generation unit can generate questions that add visually stimulating effects. This allows the generation unit to provide more appropriate questions by adjusting the way questions are presented based on the user's emotions. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the way questions are presented.

[0083] The generation unit can customize the question format according to the user's learning style at the time of generation. For example, the generation unit customizes the question format according to the user's learning style at the time of generation. Learning styles include, but are not limited to, visual, auditory, and experiential. For example, if the user is a visual learner, the generation unit can generate questions that make extensive use of diagrams and graphs. Also, if the user is an auditory learner, the generation unit can generate questions that include audio explanations. Also, if the user is an experiential learner, the generation unit can generate practical exercises. This enables more effective learning by customizing the question format according to the learning style. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's learning style data into the generation AI and cause the generation AI to customize the question format.

[0084] The generation unit can generate more effective questions by referring to past question generation history during generation. For example, the generation unit references past question generation history during generation. The question generation history includes, for example, previously generated questions, the generation date and time, and generation conditions, but is not limited to these examples. For example, the generation unit analyzes trends in questions previously solved by the user and generates similar questions. It can also generate questions with adjusted difficulty based on questions that the user was unable to solve in the past. It can also generate questions of appropriate difficulty by taking into account the time it took the user to solve questions previously solved. By referring to the past question generation history, more effective questions can be provided. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input past question generation history data into the generation AI and cause the generation AI to optimize question generation.

[0085] The generation unit can estimate the user's emotions and adjust the difficulty of the questions based on the estimated user emotions. The generation unit, for example, estimates the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The generation unit, for example, estimates the user's emotions and adjusts the difficulty of the questions based on the estimated user emotions. For example, if the user is stressed, it can generate easy questions. Also, if the user is relaxed, it can generate questions of appropriate difficulty. Also, if the user is excited, it can generate challenging questions. In this way, by adjusting the difficulty of the questions according to the user's emotions, it is possible to provide questions of an appropriate difficulty. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the difficulty of the questions.

[0086] The generation unit can adjust the content of the questions based on the user's learning environment during generation. For example, the generation unit adjusts the content of the questions based on the user's learning environment during generation. The learning environment includes, but is not limited to, the learning location, the device used, and the ambient noise level. For example, if the user is learning in a quiet environment, the generation unit can generate questions that require concentration. Also, if the user is learning in a noisy environment, the generation unit can generate questions that can be solved in a short time. Also, if the user is learning while on the move, the generation unit can generate questions optimized for mobile devices. By adjusting the content of the questions based on the learning environment, more appropriate questions can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's learning environment data into the generation AI and cause the generation AI to adjust the content of the questions.

[0087] The generation unit can select the theme of the question based on the user's interests at the time of generation. For example, the generation unit selects the theme of the question based on the user's interests at the time of generation. Interests include, but are not limited to, hobbies, topics of interest, and past learning history. For example, if the user is interested in sports, the generation unit can generate sports-related questions. Also, if the user is interested in music, the generation unit can generate music-related questions. Also, if the user is interested in science, the generation unit can generate science-related questions. In this way, selecting the theme of the question based on the user's interests can increase motivation to learn. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's interest data into the generation AI and cause the generation AI to select the theme of the question.

[0088] The providing unit can estimate the user's emotions and adjust the way questions are presented based on the estimated user emotions. The providing unit, for example, estimates the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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. The providing unit, for example, estimates the user's emotions and adjusts the way questions are presented based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. Also, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate question presentation by adjusting the presentation method according to the user's emotions. 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 the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method.

[0089] The providing unit can provide questions at the optimal timing by referring to the user's learning history when providing the questions. For example, the providing unit can refer to the user's learning history when providing the questions. The learning history includes, for example, past learning content, learning time, learning results, etc., but is not limited to these examples. For example, the providing unit can provide questions at the optimal timing based on the time periods in which the user studied in the past. Furthermore, the providing unit can provide questions at times when the user's concentration is highest based on the user's learning history. Furthermore, the providing unit can provide questions at the appropriate timing by taking into account the answer times of questions the user previously answered. In this way, by referring to the learning history, questions can be provided at the optimal timing. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's learning history data into the generation AI and cause the generation AI to optimize the timing of providing the questions.

[0090] The providing unit can provide questions in an optimal format by taking into account the user's device information when providing the questions. For example, the providing unit can consider the user's device information when providing the questions. Device information includes, but is not limited to, the device type, screen size, and OS version. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Also, if the user is using a PC, the providing unit can provide a display method that includes detailed information. This allows questions to be provided in an optimal format by taking into account the device information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to optimize the display format.

[0091] The providing unit can estimate the user's emotions and adjust the display order of questions based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, with 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. The providing unit, for example, estimates the user's emotions and adjusts the display order of questions based on the estimated user's emotions. For example, if the user is nervous, questions can be displayed in order from easier to harder. Also, if the user is relaxed, questions can be displayed in order from more difficult to harder. Also, if the user is in a hurry, questions can be displayed in order from most important to most important. In this way, by adjusting the display order according to the user's emotions, questions can be provided in a more appropriate order. 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 user's emotion data into the generation AI and cause the generation AI to adjust the display order.

[0092] The providing unit may provide region-specific questions by taking into account the user's geographical location information. For example, the providing unit may consider the user's geographical location information when providing the questions. Examples of geographical location information include, but are not limited to, GPS data, IP address, and area code. For example, the providing unit may provide specific questions based on the educational curriculum of the area where the user lives. The providing unit may also provide local educational events and resources based on the user's geographical location information. Appropriate questions may also be provided depending on the educational level of the area where the user lives. This allows region-specific questions to be provided by taking into account the geographical location information. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input the user's geographical location information data into a generation AI and cause the generation AI to provide region-specific questions.

[0093] The providing unit can analyze the user's social media activity and provide relevant questions at the time of providing. For example, the providing unit can analyze the user's social media activity at the time of providing. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the providing unit can analyze learning content shared by the user on social media and provide relevant questions. The providing unit can also provide relevant questions based on the activity of the user's learning community on social media. The providing unit can also analyze the content of educational accounts the user follows on social media and provide relevant questions. In this way, relevant questions can be provided by analyzing social media activity. Some or all of the above-described processing by the providing unit can be performed, for example, using AI or without using AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to provide relevant questions.

[0094] The analysis unit can estimate the user's emotions and adjust the answer analysis method based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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. The analysis unit, for example, estimates the user's emotions and adjusts the answer analysis method based on the estimated user emotions. For example, if the user is nervous, it can provide a simple, highly visible analysis result. Also, if the user is relaxed, it can provide a detailed analysis result. Also, if the user is in a hurry, it can provide an analysis result that focuses on the main points. This allows the analysis method to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the answer analysis method.

[0095] The analysis unit can improve the accuracy of the analysis by referring to the user's past answer history during analysis. For example, the analysis unit can refer to the user's past answer history during analysis. The answer history includes, for example, correct / incorrect answers, answer time, and answer details, but is not limited to these examples. The analysis unit can, for example, analyze trends in questions the user has previously answered to improve the accuracy of the analysis. It can also identify specific error patterns from the user's past answer history and reflect them in the analysis. It can also improve the accuracy of the analysis by taking into account the answer time of questions the user has previously answered. By referring to the past answer history, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's past answer history data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0096] The analysis unit can customize the analysis algorithm according to the user's learning style during analysis. For example, the analysis unit customizes the analysis algorithm according to the user's learning style during analysis. Learning styles include, but are not limited to, visual, auditory, and experiential learning. For example, if the user is a visual learner, the analysis unit can provide analysis results that make extensive use of diagrams and graphs. Furthermore, if the user is an auditory learner, the analysis unit can provide analysis results that include audio explanations. Furthermore, if the user is an experiential learner, the analysis unit can provide analysis results that include practical exercises. This enables more effective analysis by customizing the analysis algorithm according to the learning style. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's learning style data into the generation AI and have the generation AI customize the analysis algorithm.

[0097] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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. The analysis unit, for example, estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. Also, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate analysis results to be provided by adjusting the display method according to the user's emotions. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0098] The analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information during analysis. For example, the analysis unit can take into account the user's geographical location information during analysis. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and area codes. For example, the analysis unit can improve the accuracy of the analysis based on the educational curriculum of the area where the user lives. Furthermore, the analysis unit can also perform analysis according to the educational level of the area based on the user's geographical location information. Furthermore, the analysis accuracy can be improved by taking into account educational events and resources in the area where the user lives. Thus, by taking into account the geographical location information, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's geographical location information data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0099] The analysis unit may analyze the user's social media activity during the analysis and provide related analysis results. For example, the analysis unit may analyze the user's social media activity during the analysis. Social media activity may include, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the analysis unit may analyze learning content shared by the user on social media and provide related analysis results. The analysis unit may also provide related analysis results based on the activity of the user's learning community on social media. The analysis unit may also analyze the content of educational accounts the user follows on social media and provide related analysis results. In this way, the analysis unit may provide related analysis results by analyzing social media activity. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the user's social media data into a generation AI and cause the generation AI to provide analysis results.

[0100] The storage unit can estimate the user's emotions and select data to be saved based on the estimated user emotions. The storage unit, for example, estimates the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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. The storage unit, for example, estimates the user's emotions and selects data to be saved based on the estimated user emotions. For example, if the user is stressed, only important data can be saved. Also, if the user is relaxed, detailed data can be saved. Also, if the user is in a hurry, data that focuses on the main points can be saved. In this way, by selecting data to be saved according to the user's emotions, more important data can be saved. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's emotion data into the generation AI and have the generation AI select data to be saved.

[0101] The storage unit can optimize the storage algorithm by referring to previously saved data when saving data. The storage unit, for example, refers to previously saved data when saving data. Saved data includes, for example, the type of data previously saved, the save date and time, and the save conditions, but is not limited to these examples. The storage unit, for example, analyzes trends in data previously saved by the user and proposes an optimal save method. It can also identify specific patterns from the user's previously saved data and optimize the save algorithm. It can also evaluate the importance of data previously saved by the user and propose an optimal save method. In this way, the save algorithm can be optimized by referring to the previously saved data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input previously saved data to a generation AI and cause the generation AI to optimize the save algorithm.

[0102] The storage unit can estimate the user's emotions and prioritize the stored data based on the estimated user emotions. The storage unit, for example, estimates the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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. The storage unit, for example, estimates the user's emotions and prioritizes the stored data based on the estimated user emotions. For example, if the user is stressed, important data can be prioritized for storage. Also, if the user is relaxed, detailed data can be prioritized for storage. Also, if the user is in a hurry, data with a focus on the main points can be prioritized for storage. In this way, important data can be prioritized by prioritizing the stored data according to the user's emotions. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of the stored data.

[0103] The storage unit may weight the stored data taking into account the user's geographical location information when storing the data. For example, the storage unit may consider the user's geographical location information when storing the data. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and area codes. For example, the storage unit may weight the stored data based on the educational curriculum of the area where the user lives. The storage unit may also weight the stored data according to the educational level of the area based on the user's geographical location information. The storage unit may also weight the stored data taking into account educational events and resources in the area where the user lives. In this way, the weighting of the stored data can be optimized by taking into account the geographical location information. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit may input the user's geographical location information data to a generation AI and cause the generation AI to weight the stored data.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The reception unit can automatically identify areas where the user has had difficulty in the past based on the user's learning history and assist with input. For example, it can analyze the accuracy rate and answer time of questions the user has answered in the past to evaluate the level of difficulty in specific areas. It can also identify areas that the user has avoided in the past and suggest those areas preferentially. Furthermore, it can evaluate the user's level of understanding of specific topics based on the user's learning history and automatically display areas where the user has difficulty. This makes it possible to more accurately identify areas where the user has difficulty and assist with input by utilizing the user's past learning history.

[0106] The generator can customize the question format according to the user's learning style. For example, it can generate questions that make extensive use of diagrams and graphs for visual learners, and questions that include audio explanations for auditory learners. It can also generate practical exercises for experiential learners. Furthermore, it can adjust the way questions are presented and the format of feedback based on the user's learning style. This allows it to provide the user with questions that are optimal for their learning style, improving learning effectiveness.

[0107] The generator can select the theme of the questions based on the user's interests. For example, if the user is interested in sports, it can generate questions related to sports, and if the user is interested in music, it can generate questions related to music. If the user is interested in science, it can also generate questions related to science. Furthermore, it can analyze the user's past learning history and social media activity to provide questions based on their interests. This can provide questions that match the user's interests and increase their motivation to learn.

[0108] The providing unit can provide questions in the optimal format taking into account the user's device information. For example, if the user is using a smartphone, a display method tailored to the screen size is provided, and if the user is using a tablet, a display method optimized for a large screen is provided. Also, if the user is using a PC, a display method including detailed information can be provided. Furthermore, questions can be provided in the optimal format depending on the OS version and usage environment of the user's device. This makes it possible to provide questions in the optimal format taking into account the user's device information.

[0109] The analysis unit can improve the accuracy of the analysis by referring to the user's past answer history. For example, it can analyze the correct answers and answer times of questions the user has answered in the past to identify specific mistake patterns. It can also evaluate the user's understanding of a specific topic from the user's past answer history and reflect this in the analysis. It can also analyze the trends in questions the user has answered in the past and optimize the analysis algorithm. In this way, the accuracy of the analysis can be improved by referring to the past answer history.

[0110] The reception unit can estimate the user's emotions and adjust the input method for difficult ranges based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable quick input of difficult ranges. In this way, adjusting the input method according to the user's emotions enables more appropriate input.

[0111] The generation unit can estimate the user's emotions and adjust the way questions are presented based on the estimated user emotions. For example, if the user is relaxed, questions that proceed at a leisurely pace can be generated. If the user is in a hurry, questions that are concise and to the point can be generated. Furthermore, if the user is excited, questions with visually stimulating effects can be generated. In this way, by adjusting the way questions are presented according to the user's emotions, more appropriate questions can be provided.

[0112] The providing unit can estimate the user's emotions and adjust the way questions are presented based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the presentation method according to the user's emotions, more appropriate questions can be presented.

[0113] The analysis unit can estimate the user's emotions and adjust the answer analysis method based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, an analysis result that focuses on the main points can be provided. In this way, by adjusting the answer analysis method according to the user's emotions, more appropriate analysis results can be provided.

[0114] The storage unit can estimate the user's emotions and select data to be saved based on the estimated user emotions. For example, if the user is feeling stressed, only important data can be saved. If the user is relaxed, detailed data can be saved. Furthermore, if the user is in a hurry, data that focuses on the main points can be saved. In this way, by selecting data to be saved according to the user's emotions, more important data can be saved.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit inputs the user's weak areas. The user's weak areas include, but are not limited to, specific mathematical areas or topics. The reception unit provides, for example, an interface for the user to input the specific mathematical areas. Step 2: The generation unit uses a generation AI to generate questions based on the information input by the reception unit. The generation AI generates questions using, for example, a text generation AI (e.g., LLM). The generation unit can also use the generation AI to generate questions based on a specific mathematical field. For example, the generation AI uses an algorithm for generating questions related to a specific mathematical field. Step 3: The providing unit provides the problem generated by the generating unit. For example, the providing unit provides an interface for displaying the generated problem to the user. The providing unit can also add an introductory section to the generated problem. For example, the providing unit adds an introductory section that explains the background of the problem and the solution method. Step 4: The analysis unit analyzes the user's answers and adjusts the difficulty level. The analysis unit, for example, uses an algorithm to analyze the user's answers. The analysis unit may also be associated with a storage unit that stores the user's answer history. For example, the analysis unit stores the user's answer history to improve the accuracy of the analysis.

[0117] 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.

[0118] 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> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats including voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. The AIs other than the generation AI are, 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 are 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 in whole by AI, but are not limited to these examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0119] 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.

[0120] Each of the multiple elements, including the above-described reception unit, generation unit, provision unit, and analysis unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and provides an interface for the user to input areas of difficulty. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates questions using a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and displays the generated questions to the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answers and adjusts the difficulty level. The correspondence between each unit and the device or control unit is not limited to the above example and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] Each of the multiple elements, including the above-described reception unit, generation unit, provision unit, and analysis unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for the user to input their weak areas. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates questions using a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and displays the generated questions to the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answers and adjusts the difficulty level. The correspondence between each unit and the device or control unit is not limited to the above example and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] 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.

[0152] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and analysis unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and provides an interface for the user to input their weak areas. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates questions using a generation AI. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and displays the generated questions to the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answers and adjusts the difficulty level. The correspondence between each unit and the device or control unit is not limited to the above example and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0168] 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.

[0169] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and analysis unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for the user to input their weak areas. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates questions using a generation AI. The provision unit is realized, for example, by the control unit 46A of the robot 414 and displays the generated questions to the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answers and adjusts the difficulty level. The correspondence between each unit and the device or control unit is not limited to the above example and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Appendix 1) a reception unit for inputting the range of difficulty of the user; a generator that generates questions based on the information input by the receiver; a providing unit that provides the questions generated by the generating unit; An analysis unit that analyzes the user's answer and adjusts the difficulty level. A system characterized by: (Appendix 2) The generation unit Generate problems using generative AI 2. The system of claim 1. (Appendix 3) The generation unit Generate questions based on specific math areas 2. The system of claim 1. (Appendix 4) The system described in Appendix 1, characterized in that the providing unit adds an introductory part to the generated question. (Appendix 5) The analysis unit Analyze user answers and adjust difficulty 2. The system of claim 1. (Appendix 6) The system described in Appendix 1, characterized in that the analysis unit is associated with a storage unit that stores the user's answer history. (Appendix 7) The reception unit Estimate the user's emotions and adjust the input method for difficult areas based on the estimated user emotions. 2. The system of claim 1. (Appendix 8) The reception unit Analyzes the user's past learning history and automatically suggests areas of difficulty 2. The system of claim 1. (Appendix 9) The reception unit Guide users through difficult areas based on their current learning progress 2. The system of claim 1. (Appendix 10) The reception unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions. 2. The system of claim 1. (Appendix 11) The reception unit Considering the user's geographic location, the app suggests location-specific learning tasks 2. The system of claim 1. (Appendix 12) The reception unit Analyze users' social media activity and suggest related weaknesses 2. The system of claim 1. (Appendix 13) The generation unit Inferring user emotions and adjusting problem wording based on the inferred user emotions 2. The system of claim 1. (Appendix 14) The generation unit Customize question format at generation time to suit the user's learning style 2. The system of claim 1. (Appendix 15) The generation unit When generating questions, refer to the history of past question generation to generate more effective questions. 2. The system of claim 1. (Appendix 16) The generation unit Estimate the user's emotions and adjust the difficulty of the questions based on the estimated user emotions. 2. The system of claim 1. (Appendix 17) The generation unit At generation time, adjust the content of the questions based on the user's learning environment 2. The system of claim 1. (Appendix 18) The generation unit At the time of generation, the topic of the question is selected based on the user's interests. 2. The system of claim 1. (Appendix 19) The providing unit Inferring user emotions and adjusting the way questions are presented based on the estimated user emotions 2. The system of claim 1. (Appendix 20) The providing unit When providing questions, the system refers to the user's learning history and provides questions at the optimal time. 2. The system of claim 1. (Appendix 21) The providing unit When providing questions, the questions are provided in the optimal format taking into account the user's device information. 2. The system of claim 1. (Appendix 22) The providing unit Infer user sentiment and adjust the question display order based on the estimated user sentiment 2. The system of claim 1. (Appendix 23) The providing unit When serving, it takes into account the user's geographic location to provide region-specific questions. 2. The system of claim 1. (Appendix 24) The providing unit When provided, analyze your social media activity and provide relevant issues 2. The system of claim 1. (Appendix 25) The analysis unit Estimate the user's emotions and adjust the answer analysis method based on the estimated user emotions. 2. The system of claim 1. (Appendix 26) The analysis unit During analysis, the accuracy of the analysis is improved by referring to the user's past answer history. 2. The system of claim 1. (Appendix 27) The analysis unit During analysis, the analysis algorithm is customized according to the user's learning style. 2. The system of claim 1. (Appendix 28) The analysis unit Inferring user emotions and adjusting the display of analysis results based on the estimated user emotions 2. The system of claim 1. (Appendix 29) The analysis unit During analysis, the geographic location of the user is taken into account to improve the accuracy of the analysis. 2. The system of claim 1. (Appendix 30) The analysis unit During analytics, analyze your social media activity and provide you with relevant analytics results 2. The system of claim 1. (Appendix 31) The storage unit Estimate the user's emotions and select data to be saved based on the estimated user emotions. 2. The system of claim 1. (Appendix 32) The storage unit When saving, the saving algorithm is optimized by referencing past saved data. 2. The system of claim 1. (Appendix 33) The storage unit Estimate user emotions and prioritize data to be stored based on the estimated user emotions. 2. The system of claim 1. (Appendix 34) The storage unit When storing, the data is weighted based on the user's geographic location. 2. The system of claim 1. [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that estimates a user's emotion using an emotion identification model, and adjusts an input method of an interface for inputting areas that the user is weak at based on the estimated emotion of the user so as to provide a simple interface and minimize input procedures when the estimated emotion of the user is stressed; a generation unit that inputs a question generation prompt to a generation AI based on the information about the weak area input by the reception unit, and generates questions by the generation AI; a providing unit that provides the problem generated by the generating unit by adding an introductory portion that explains the background or a solution of the problem; an analysis unit that analyzes the user's answer and adjusts the difficulty of the question so that the generation unit generates an easier question if the user cannot solve the question; A system characterized by:

2. The generation unit A text generation AI is used as the generation AI, and a question is generated by inputting the question generation prompt to the text generation AI.

2. The system of claim 1.

3. The generation unit Based on the information about the specific mathematical field input by the reception unit, the problem generation prompt is input to the generation AI, and the generation AI generates a problem.

2. The system of claim 1.

4. The providing unit Adding an introductory section to the problem generated by the generator, which explains the basic concept of the problem and the procedure for solving the problem 2. The system of claim 1.

5. The generation unit A theme of the question is selected based on information about the user's interests, and a prompt for generating the question is input to the generation AI based on the selected theme, and the question is generated by the generation AI.

2. The system of claim 1.

6. The reception unit Analyzing information indicating the user's social media activity, proposing areas of weakness for the user based on the analysis results, and adjusting the input method to accept input in the proposed areas of weakness.

2. The system of claim 1.

7. The reception unit The emotion of the user is estimated using the emotion identification model, and based on the estimated emotion of the user, the input method for the weak range is adjusted so that detailed input options are provided when the user is relaxed and voice input is prioritized when the user is in a hurry.

2. The system of claim 1.

8. The reception unit Analyzing information indicating the user's past learning history, automatically suggesting areas in which the user is weak based on the analysis results and trends in questions that the user has gotten wrong in the past, and adjusting the input method to accept input in the suggested areas in which the user is weak 2. The system of claim 1.

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