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 a description and related instruction sentence regarding 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] JP 2022-180282 A 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 specific to the areas in which a 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 appropriately adjust the level of difficulty. [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. Effect of the Invention

[0007] The system according to the embodiment can provide questions that are specific to the areas that the user is weak at, and can adjust the level of difficulty appropriately. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. 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 is a system that identifies a range that a user is weak in and generates problems within that range. In this system, a user inputs a range that the user is weak in, and a generation AI generates problems based on that range. If the user cannot solve the generated problem, the generated problem is regenerated with an adjusted difficulty level. In addition, the problem includes an introductory part to support the user to easily understand. For example, the user inputs the range that the user is weak in. For example, a "specific range of mathematics" is specified. This information is input to the generation AI. Next, the generation AI generates a problem based on the input range. The generation AI creates a problem within the specified range and provides it to the user. For example, if the range is "specific range of mathematics," a problem within that range is generated. If the user cannot solve the generated problem, the generated problem is regenerated with an adjusted difficulty level. The generation AI analyzes the user's answer and adjusts the difficulty level of the problem. For example, if the user cannot solve the problem, an easier problem is generated to support the user to solve it. In addition, the problem includes an introductory part to support the user to easily understand. The generation AI adds an introductory part before the problem to enable the user to understand the background of the problem and the solution method. For example, a specific math problem includes an introductory section that explains the basic concept and the procedure for solving the problem. This allows the user to efficiently study the areas in which he or she is weak and deepens his or her understanding of math. This allows the math learning support system to identify the areas in which the user is weak and generate and provide problems in those areas.

[0029] The mathematics learning support system according to the embodiment includes a reception unit, a generation unit, a provision unit, and an analysis unit. The reception unit inputs the range of difficulty of the user. The range of difficulty of the user includes, for example, a specific range or topic of mathematics, but is not limited to such an example. The reception unit provides, for example, an interface for the user to input the specific range of mathematics. The generation unit generates a problem based on the information input by the reception unit using a generation AI. The generation AI generates a problem using, for example, a text generation AI (for example, LLM). The generation unit can also generate a problem based on a specific range of mathematics using the generation AI. For example, the generation AI uses an algorithm for generating a problem related to a specific range of mathematics. The provision unit provides the problem generated by the generation unit. The provision unit provides, for example, an interface for displaying the generated problem to the user. The provision unit can also add an introduction to the generated problem. For example, the provision unit adds an introduction that explains the background of the problem and the solution method. The analysis unit analyzes the user's answer and adjusts the difficulty level. The analysis unit uses, for example, an algorithm for analyzing the user's answer. 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. In this way, the mathematics learning support system according to the embodiment can 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 range. The user's weak range includes, but is not limited to, a specific range or topic of mathematics. The reception unit provides, for example, an interface for the user to input the specific range of mathematics. Specifically, the reception unit has a graphical user interface (GUI) that the user can intuitively operate, and the user can select the weak range using a drop-down menu or a check box. It is also possible to freely input a specific topic or question format using a text box. Furthermore, the reception unit also has a function of automatically estimating the weak range by referring to the user's past learning history and performance data. For example, by analyzing past test results and answer history of practice questions, if the correct answer rate in a specific range is low, the range can be automatically set as the weak range. This allows the user to save the trouble of accurately inputting the weak range and to proceed with learning more efficiently. The reception unit saves the information input by the user in real time and optimizes the operation of the entire system in cooperation with the generation unit and the analysis unit. For example, if the user adds a new weak range, the information is immediately sent to the generation unit, and the content of the generated problem is updated. This enables the reception unit to quickly respond to the needs of the user and provide individualized learning support.

[0031] The generation unit uses the 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 range of mathematics. Specifically, the generation AI uses natural language processing technology to automatically generate questions related to the weak range input by the user. For example, if the user inputs that they are weak in the range of "differential and integral calculus," the generation AI generates questions of a wide range of difficulty, from basic problems to applied problems, related to that range. The generation AI refers to a database of past questions and uses an algorithm to generate similar questions, thereby providing the user with questions that are new but easy to understand. The generation AI can also diversify the format and method of questions. For example, by generating questions in various formats, such as multiple choice questions, written questions, and questions using graphs and diagrams, the user's level of understanding can be evaluated from multiple angles. In addition, the generation unit also has a function to dynamically adjust the difficulty of questions, taking into account the user's learning progress and answer history. For example, if the user answers correctly consecutively, the difficulty of the next question is increased, whereas if the user answers incorrectly consecutively, the difficulty is 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 an introductory portion to the generated problems. Specifically, the providing unit has a function of displaying text and diagrams for providing information and background knowledge necessary for the user to tackle the problems. For example, when solving a calculus problem, adding an explanation of basic theorems and formulas as an introductory portion makes it easier for the user to understand the problem. The providing unit also has a function of providing a solution to the problem and hints in stages. If the user feels difficulty in the process of tackling the problem, the providing unit displays hints at an appropriate time to support the user's understanding. Furthermore, the providing unit has a function of evaluating the user's answer in real time and providing immediate feedback. For example, the providing unit judges whether the answer is correct immediately after the user enters it, and if the answer is correct, encourages the user to move on to the next question, and if the answer is incorrect, displays an explanation and encourages the user to try again. This allows the user to instantly check their level of understanding and efficiently progress with their learning. The providing unit saves the user's learning history and answer history, and works with the analyzing unit to optimize the learning content. For example, by providing questions for the user to review questions that they got wrong in the past or areas 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 uses, for example, an algorithm for analyzing 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 pattern, and the like. This makes it possible to accurately grasp the user's level of understanding and learning progress. The analysis unit dynamically adjusts the difficulty level of the next problem to be provided based on these data. For example, if the user shows a high correct answer rate in a specific range, the difficulty level of the problems 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 saves the user's answer history and builds a database for evaluating long-term learning effects. This makes it possible to propose an individualized learning plan based on the user's learning history. For example, by providing problems for reviewing questions that were answered incorrectly in the past or areas that the user is weak at, the user's learning effect can be improved. Furthermore, the analysis unit can also evaluate the overall learning tendency and the distribution of difficulty levels of problems by aggregating the user's answer data and performing statistical analysis. This can be useful for improving the problem generation algorithm and question provision method of the entire system. The analysis unit also plays a role in providing detailed feedback to the user to increase their motivation to study. For example, by providing advice on the answering process and thinking rather than just indicating whether the answer was correct, the user's understanding can be deepened. In this way, the analysis unit can 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 (for example, LLM). The generation unit can also generate questions based on a specific range of mathematics using the generation AI. For example, the generation AI uses an algorithm for generating questions related to a specific range of mathematics. In this way, the generation of questions 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 prompt for question generation to the generation AI and output the generated questions.

[0035] The generating unit can generate problems based on a specific range of mathematics. The generating unit generates problems based on a specific range of mathematics, for example. The specific range of mathematics includes, but is not limited to, algebra, geometry, calculus, and the like. The generating unit uses, for example, an algorithm for generating problems related to a specific range of mathematics. This makes it possible to provide problems according to the needs of the user by generating problems based on the specific range. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input a prompt for generating problems related to a specific range of mathematics to the generating AI and output the generated problems.

[0036] The providing unit can add an introductory portion to the generated problem. The providing unit, for example, adds an introductory portion to the generated problem. The introductory portion includes, for example, a background explanation of the problem and a hint on the solution, but is not limited to such examples. The providing unit, for example, adds an introductory portion that explains the background of the problem and the solution. By adding the introductory portion, the user can easily understand the background of the problem and the solution. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input a prompt to add an introductory portion to the generated problem to the generating AI, and output the generated introductory portion.

[0037] The analysis unit can analyze the user's answer and adjust the difficulty level. The analysis unit, for example, analyzes the user's answer. The analysis unit, for example, uses an algorithm to analyze the user's answer. 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 a problem of appropriate difficulty by adjusting the difficulty level based on the user's answer. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the user's answer data to a generation AI and output the analysis result.

[0038] The analysis unit may be associated with a storage unit that stores the answer history of the user. The analysis unit may be associated with, for example, a storage unit that stores the answer history of the user. The storage unit may store, for example, the answer history of the user. The answer history may include, for example, correctness or incorrectness of the answer, the answer time, details of the answer, and the like, but is not limited to such examples. The storage unit may, for example, store the answer history of the user to improve the accuracy of the analysis. As a result, the accuracy of the analysis is improved by storing the answer history. Some or all of the above-mentioned 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 answer history data of the user to a generation AI to optimize the storage algorithm.

[0039] The reception unit can analyze the user's past learning history and automatically suggest the weak range. The reception unit, for example, analyzes the user's past learning history. The learning history includes, for example, past learning contents, learning time, learning results, etc., but is not limited to such examples. The reception unit, for example, analyzes the user's past learning history and automatically suggests the weak range. For example, it analyzes the tendency of questions that the user has made mistakes in the past and suggests the weak range. It is also possible to evaluate the understanding level in a specific range from the user's past learning history and automatically display the weak range. It is also possible to identify the range that the user has avoided in the past and suggest that range. In this way, it is possible to automatically identify the weak range of the user by analyzing the past learning history. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's learning history data to the generation AI and cause the generation AI to execute the suggestion of the weak range.

[0040] The reception unit can guide the input of the weak range based on the user's current learning progress. The reception unit, for example, evaluates the user's current learning progress. The learning progress includes, for example, the current learning content, progress status, and achievement level, but is not limited to such examples. The reception unit, for example, guides the input of the weak range based on the user's current learning progress. For example, based on the range the user is currently studying, a related weak range is suggested. In addition, the reception unit can evaluate the user's current learning progress and guide the weak range to be studied next. In addition, the difficulty level of the problem the user is currently working on can be taken into consideration and the user can be prompted to input an appropriate weak range. As a result, input can be guided based on the current learning progress, making it possible to input an appropriate range. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's learning progress data to the generation AI and cause the generation AI to execute the input guide.

[0041] The reception unit can propose region-specific study tasks in consideration of the geographical location information of the user. The reception unit, for example, considers the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, and region code, but is not limited to such examples. The reception unit, for example, proposes region-specific study tasks in consideration of the geographical location information of the user. For example, a specific study task is proposed based on the educational curriculum of the region where the user lives. In addition, regional educational events and resources can be proposed based on the geographical location information of the user. In addition, appropriate study tasks can be proposed according to the educational level of the region where the user lives. In this way, region-specific study tasks can be proposed by considering the geographical location information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute the proposal of study tasks.

[0042] The reception unit can analyze the social media activity of the user and suggest the relevant weak range. The reception unit, for example, analyzes the social media activity of the user. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. The reception unit, for example, analyzes the social media activity of the user and suggests the relevant weak range. For example, the learning content shared by the user on social media is analyzed and the weak range is suggested. In addition, the reception unit can also suggest the relevant weak range based on the activity of the learning community on the user's social media. In addition, the content of the educational account that the user follows on social media can be analyzed and the weak range can be suggested. In this way, the weak range of the user can be identified by analyzing the social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the social media data of the user to the generation AI and cause the generation AI to execute the suggestion of the weak range.

[0043] The generating unit can customize the format of the questions according to the learning style of the user at the time of generation. The generating unit, for example, customizes the format of the questions according to the learning style of the user at the time of generation. The learning style includes, for example, visual type, auditory type, experiential type, etc., but is not limited to such examples. For example, if the user is a visual learner, the generating unit generates questions that make extensive use of figures and graphs. Also, if the user is an auditory learner, the generating unit can generate questions that include audio explanations. Also, if the user is an experiential learner, practical exercise questions can be generated. This enables more effective learning by customizing the format of the questions according to the learning style. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the learning style data of the user to the generating AI and cause the generating AI to customize the format of the questions.

[0044] The generating unit can generate more effective questions by referring to the past question generation history at the time of generation. The generating unit, for example, refers to the past question generation history at the time of generation. The question generation history includes, for example, questions generated in the past, the generation date and time, and the generation conditions, but is not limited to such examples. The generating unit, for example, analyzes the tendency of questions solved by the user in the past and generates similar questions. In addition, the generating unit can generate questions with adjusted difficulty based on questions that the user could not solve in the past. In addition, the generating unit can generate questions of appropriate difficulty by considering the solution time of the questions solved by the user in the past. In this way, more effective questions can be provided by referring to the past question generation history. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input past question generation history data to the generating AI and cause the generating AI to execute optimization of question generation.

[0045] The generation unit can adjust the content of the problem based on the user's learning environment at the time of generation. The generation unit, for example, adjusts the content of the problem based on the user's learning environment at the time of generation. The learning environment includes, for example, the learning location, the device used, the surrounding noise level, etc., but is not limited to such examples. For example, when the user is studying in a quiet environment, the generation unit generates a problem that requires concentration. In addition, when the user is studying in a noisy environment, it can also generate a problem that can be solved in a short time. In addition, when the user is studying while moving, it can also generate a problem optimized for a mobile device. In this way, by adjusting the content of the problem based on the learning environment, it is possible to provide a more appropriate problem. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's learning environment data to the generation AI and cause the generation AI to adjust the content of the problem.

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

[0047] The providing unit can provide questions at optimal timing by referring to the user's learning history at the time of providing. The providing unit, for example, refers to the user's learning history at the time of providing. The learning history includes, for example, past learning content, learning time, learning results, etc., but is not limited to such examples. The providing unit provides questions at optimal timing, for example, based on the time period in which the user studied in the past. In addition, it is also possible to provide questions at a time period when the user's concentration is high based on the user's learning history. In addition, it is also possible to provide questions at an appropriate timing by taking into account the answering time of questions that the user answered in the past. In this way, it is possible to provide questions at optimal timing by referring to the learning history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's learning history data to the generating AI and cause the generating AI to execute optimization of the timing of provision.

[0048] The providing unit can provide the problem in an optimal format by considering the device information of the user at the time of providing. For example, the providing unit can consider the device information of the user at the time of providing. The device information includes, for example, the type of device, the screen size, the OS version, etc., but is not limited to such examples. For example, when the user is using a smartphone, the providing unit can provide a display method that matches the screen size. In addition, when the user is using a tablet, it can also provide a display method optimized for a large screen. In addition, when the user is using a personal computer, it can also provide a display method including detailed information. In this way, by considering the device information, it is possible to provide the problem in an optimal format. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information data of the user to the generating AI and cause the generating AI to execute optimization of the display format.

[0049] The providing unit can provide region-specific problems by considering the geographical location information of the user at the time of providing. The providing unit, for example, considers the geographical location information of the user at the time of providing. The geographical location information includes, for example, GPS data, IP address, region code, etc., but is not limited to such examples. The providing unit, for example, provides specific problems based on the educational curriculum of the region where the user lives. In addition, it is also possible to provide regional educational events and resources based on the geographical location information of the user. In addition, it is also possible to provide appropriate problems according to the educational level of the region where the user lives. In this way, region-specific problems can be provided by considering the geographical location information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information data of the user to the generating AI and cause the generating AI to provide region-specific problems.

[0050] The providing unit can analyze the social media activity of the user at the time of providing and provide related questions. The providing unit, for example, analyzes the social media activity of the user at the time of providing. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. The providing unit, for example, analyzes the learning content shared by the user on social media and provides related questions. Also, related questions can be provided based on the activity of the learning community on the user's social media. Also, related questions can be provided by analyzing the content of educational accounts that the user follows on social media. In this way, related questions can be provided by analyzing social media activities. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the social media data of the user to the generating AI and cause the generating AI to provide related questions.

[0051] The analysis unit can improve the accuracy of the analysis by referring to the user's past answer history during the analysis. The analysis unit, for example, refers to the user's past answer history during the analysis. The answer history includes, for example, correctness of the answer, answer time, details of the answer, and the like, but is not limited to such examples. The analysis unit, for example, analyzes the tendency of questions that the user has answered in the past, and improves the accuracy of the analysis. In addition, it is also possible to identify a specific mistake pattern from the user's past answer history and reflect it in the analysis. In addition, it is also possible to improve the accuracy of the analysis by taking into account the answer time of questions that the user has answered in the past. As a result, the accuracy of the analysis is improved by referring to the past answer history. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's past answer history data to the generation AI and cause the generation AI to execute optimization of the analysis algorithm.

[0052] The analysis unit can customize the analysis algorithm according to the learning style of the user at the time of analysis. The analysis unit, for example, customizes the analysis algorithm according to the learning style of the user at the time of analysis. The learning style includes, for example, visual type, auditory type, experiential type, etc., but is not limited to such examples. For example, if the user is a visual learner, the analysis unit provides an analysis result that makes extensive use of figures and graphs. Also, if the user is an auditory learner, the analysis unit can provide an analysis result that includes audio explanations. Also, if the user is an experiential learner, the analysis unit can provide an analysis result that includes practical exercises. This enables more effective analysis by customizing the analysis algorithm according to the learning style. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's learning style data to the generation AI and cause the generation AI to execute customization of the analysis algorithm.

[0053] The analysis unit can improve the accuracy of the analysis by considering the geographical location information of the user during the analysis. The analysis unit, for example, considers the geographical location information of the user during the analysis. The geographical location information includes, for example, GPS data, IP address, and area code, but is not limited to such examples. The analysis unit improves the accuracy of the analysis, for example, based on the educational curriculum of the area where the user lives. In addition, based on the geographical location information of the user, an analysis according to the educational level of the area can be performed. In addition, the accuracy of the analysis can be improved by considering educational events and resources in the area where the user lives. As a result, the accuracy of the analysis is improved by considering the geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute optimization of the analysis algorithm.

[0054] The analysis unit can analyze the social media activity of the user at the time of analysis and provide related analysis results. The analysis unit, for example, analyzes the social media activity of the user at the time of analysis. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. The analysis unit, for example, analyzes the learning content shared by the user on social media and provides related analysis results. In addition, the analysis unit can provide related analysis results based on the activity of the learning community on the user's social media. In addition, the analysis unit can analyze the content of the educational accounts that the user follows on social media and provide related analysis results. In this way, by analyzing the social media activity, related analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the social media data of the user to the generation AI and cause the generation AI to provide the analysis results.

[0055] The storage unit can optimize the storage algorithm by referring to the past stored data at the time of storage. The storage unit, for example, refers to the past stored data at the time of storage. The stored data includes, for example, the type of data stored in the past, the storage date and time, the storage conditions, and the like, but is not limited to such examples. The storage unit, for example, analyzes the tendency of data stored in the past by the user and proposes an optimal storage method. In addition, it is also possible to identify a specific pattern from the user's past stored data and optimize the storage algorithm. In addition, it is also possible to evaluate the importance of data stored in the past by the user and propose an optimal storage method. In this way, the storage algorithm can be optimized by referring to the past stored data. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the past stored data to the generation AI and cause the generation AI to execute optimization of the storage algorithm.

[0056] The storage unit can weight the stored data in consideration of the geographical location information of the user at the time of storage. The storage unit, for example, considers the geographical location information of the user at the time of storage. The geographical location information includes, for example, GPS data, IP address, and area code, but is not limited to such examples. The storage unit, for example, weights the stored data based on the educational curriculum of the area where the user lives. In addition, the storage unit can weight the stored data according to the educational level of the area based on the geographical location information of the user. In addition, the stored data can be weighted in consideration of educational events and resources in the area where the user lives. In this way, the weighting of the stored data can be optimized by considering the geographical location information. Part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute the weighting of 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 that the user has had difficulty with in the past based on the user's learning history and assist with input. For example, the reception unit can analyze the accuracy rate and answer time of questions that the user has answered in the past to evaluate the degree of difficulty in a specific area. 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 a specific topic from the user's learning history and automatically display areas that the user has difficulty with. This makes it possible to more accurately identify areas that the user has difficulty with by utilizing the user's past learning history and assist with input.

[0059] The generator can customize the question format according to the user's learning style. For example, questions that make extensive use of figures and graphs can be generated for visual learners, and questions that include audio explanations can be generated 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 makes it possible to provide optimal questions according to the user's learning style and improve the effectiveness of learning.

[0060] The generation unit can select the theme of the questions based on the user's interests. For example, if the user is interested in sports, questions related to sports are generated, and if the user is interested in music, questions related to music are generated. Also, if the user is interested in science, questions related to science can be generated. Furthermore, the user's past learning history and social media activity can be analyzed to provide questions based on the user's interests. This makes it possible to provide questions according to the user's interests and increase the user's motivation to learn.

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

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

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The reception unit inputs the user's weak areas. The weak areas of the user include, but are not limited to, specific areas or topics of mathematics. The reception unit provides, for example, an interface for the user to input the specific area of ​​mathematics. Step 2: The generation unit uses the 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 range of mathematics. For example, the generation AI uses an algorithm for generating questions related to a specific range of mathematics. 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 portion to the generated problem. For example, the providing unit adds an introductory portion 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 is a system that identifies a range that a user is weak in and generates problems within that range. In this system, a user inputs a range that the user is weak in, and a generation AI generates problems based on that range. If the user cannot solve the generated problem, the generated problem is regenerated with an adjusted difficulty level. In addition, the problem includes an introductory part to support the user to easily understand. For example, the user inputs the range that the user is weak in. For example, a "specific range of mathematics" is specified. This information is input to the generation AI. Next, the generation AI generates a problem based on the input range. The generation AI creates a problem within the specified range and provides it to the user. For example, if the range is "specific range of mathematics," a problem within that range is generated. If the user cannot solve the generated problem, the generated problem is regenerated with an adjusted difficulty level. The generation AI analyzes the user's answer and adjusts the difficulty level of the problem. For example, if the user cannot solve the problem, an easier problem is generated to support the user to solve it. In addition, the problem includes an introductory part to support the user to easily understand. The generation AI adds an introductory part before the problem to enable the user to understand the background of the problem and the solution method. For example, a specific math problem includes an introductory section that explains the basic concept and the procedure for solving the problem. This allows the user to efficiently study the areas in which he or she is weak and deepens his or her understanding of math. This allows the math learning support system to identify the areas in which the user is weak and generate and provide problems in those areas.

[0066] The mathematics learning support system according to the embodiment includes a reception unit, a generation unit, a provision unit, and an analysis unit. The reception unit inputs the range of difficulty of the user. The range of difficulty of the user includes, for example, a specific range or topic of mathematics, but is not limited to such an example. The reception unit provides, for example, an interface for the user to input the specific range of mathematics. The generation unit generates a problem based on the information input by the reception unit using a generation AI. The generation AI generates a problem using, for example, a text generation AI (for example, LLM). The generation unit can also generate a problem based on a specific range of mathematics using the generation AI. For example, the generation AI uses an algorithm for generating a problem related to a specific range of mathematics. The provision unit provides the problem generated by the generation unit. The provision unit provides, for example, an interface for displaying the generated problem to the user. The provision unit can also add an introduction to the generated problem. For example, the provision unit adds an introduction that explains the background of the problem and the solution method. The analysis unit analyzes the user's answer and adjusts the difficulty level. The analysis unit uses, for example, an algorithm for analyzing the user's answer. 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. In this way, the mathematics learning support system according to the embodiment can 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 range. The user's weak range includes, but is not limited to, a specific range or topic of mathematics. The reception unit provides, for example, an interface for the user to input the specific range of mathematics. Specifically, the reception unit has a graphical user interface (GUI) that the user can intuitively operate, and the user can select the weak range using a drop-down menu or a check box. It is also possible to freely input a specific topic or question format using a text box. Furthermore, the reception unit also has a function of automatically estimating the weak range by referring to the user's past learning history and performance data. For example, by analyzing past test results and answer history of practice questions, if the correct answer rate in a specific range is low, the range can be automatically set as the weak range. This allows the user to save the trouble of accurately inputting the weak range and to proceed with learning more efficiently. The reception unit saves the information input by the user in real time and optimizes the operation of the entire system in cooperation with the generation unit and the analysis unit. For example, if the user adds a new weak range, the information is immediately sent to the generation unit, and the content of the generated problem is updated. This enables the reception unit to quickly respond to the needs of the user and provide individualized learning support.

[0068] The generation unit uses the 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 range of mathematics. Specifically, the generation AI uses natural language processing technology to automatically generate questions related to the weak range input by the user. For example, if the user inputs that they are weak in the range of "differential and integral calculus," the generation AI generates questions of a wide range of difficulty, from basic problems to applied problems, related to that range. The generation AI refers to a database of past questions and uses an algorithm to generate similar questions, thereby providing the user with questions that are new but easy to understand. The generation AI can also diversify the format and method of questions. For example, by generating questions in various formats, such as multiple choice questions, written questions, and questions using graphs and diagrams, the user's level of understanding can be evaluated from multiple angles. In addition, the generation unit also has a function to dynamically adjust the difficulty of questions, taking into account the user's learning progress and answer history. For example, if the user answers correctly consecutively, the difficulty of the next question is increased, whereas if the user answers incorrectly consecutively, the difficulty is 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 an introductory portion to the generated problems. Specifically, the providing unit has a function of displaying text and diagrams for providing information and background knowledge necessary for the user to tackle the problems. For example, when solving a calculus problem, adding an explanation of basic theorems and formulas as an introductory portion makes it easier for the user to understand the problem. The providing unit also has a function of providing a solution to the problem and hints in stages. If the user feels difficulty in the process of tackling the problem, the providing unit displays hints at an appropriate time to support the user's understanding. Furthermore, the providing unit has a function of evaluating the user's answer in real time and providing immediate feedback. For example, the providing unit judges whether the answer is correct immediately after the user enters it, and if the answer is correct, encourages the user to move on to the next question, and if the answer is incorrect, displays an explanation and encourages the user to try again. This allows the user to instantly check their level of understanding and efficiently progress with their learning. The providing unit saves the user's learning history and answer history, and works with the analyzing unit to optimize the learning content. For example, by providing questions for the user to review questions that they got wrong in the past or areas 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 uses, for example, an algorithm for analyzing 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 pattern, and the like. This makes it possible to accurately grasp the user's level of understanding and learning progress. The analysis unit dynamically adjusts the difficulty level of the next problem to be provided based on these data. For example, if the user shows a high correct answer rate in a specific range, the difficulty level of the problems 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 saves the user's answer history and builds a database for evaluating long-term learning effects. This makes it possible to propose an individualized learning plan based on the user's learning history. For example, by providing problems for reviewing questions that were answered incorrectly in the past or areas that the user is weak at, the user's learning effect can be improved. Furthermore, the analysis unit can also evaluate the overall learning tendency and the distribution of difficulty levels of problems by aggregating the user's answer data and performing statistical analysis. This can be useful for improving the problem generation algorithm and question provision method of the entire system. The analysis unit also plays a role in providing detailed feedback to the user to increase their motivation to study. For example, by providing advice on the answering process and thinking rather than just indicating whether the answer was correct, the user's understanding can be deepened. In this way, the analysis unit can 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 (for example, LLM). The generation unit can also generate questions based on a specific range of mathematics using the generation AI. For example, the generation AI uses an algorithm for generating questions related to a specific range of mathematics. In this way, the generation of questions 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 prompt for question generation to the generation AI and output the generated questions.

[0072] The generating unit can generate problems based on a specific range of mathematics. The generating unit generates problems based on a specific range of mathematics, for example. The specific range of mathematics includes, but is not limited to, algebra, geometry, calculus, and the like. The generating unit uses, for example, an algorithm for generating problems related to a specific range of mathematics. This makes it possible to provide problems according to the needs of the user by generating problems based on the specific range. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input a prompt for generating problems related to a specific range of mathematics to the generating AI and output the generated problems.

[0073] The providing unit can add an introductory portion to the generated problem. The providing unit, for example, adds an introductory portion to the generated problem. The introductory portion includes, for example, a background explanation of the problem and a hint on the solution, but is not limited to such examples. The providing unit, for example, adds an introductory portion that explains the background of the problem and the solution. By adding the introductory portion, the user can easily understand the background of the problem and the solution. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input a prompt to add an introductory portion to the generated problem to the generating AI, and output the generated introductory portion.

[0074] The analysis unit can analyze the user's answer and adjust the difficulty level. The analysis unit, for example, analyzes the user's answer. The analysis unit, for example, uses an algorithm to analyze the user's answer. 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 a problem of appropriate difficulty by adjusting the difficulty level based on the user's answer. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the user's answer data to a generation AI and output the analysis result.

[0075] The analysis unit may be associated with a storage unit that stores the answer history of the user. The analysis unit may be associated with, for example, a storage unit that stores the answer history of the user. The storage unit may store, for example, the answer history of the user. The answer history may include, for example, correctness or incorrectness of the answer, the answer time, details of the answer, and the like, but is not limited to such examples. The storage unit may, for example, store the answer history of the user to improve the accuracy of the analysis. As a result, the accuracy of the analysis is improved by storing the answer history. Some or all of the above-mentioned 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 answer history data of the user to a generation AI to optimize the storage algorithm.

[0076] The reception unit can estimate the user's emotion and adjust the input method of the difficult range based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The reception unit, for example, estimates the user's emotion and adjusts the input method of the difficult range based on the estimated user's emotion. For example, when the user is stressed, a simple interface is provided to minimize the input procedure. In addition, when the user is relaxed, detailed input options can be provided and a customizable input method can be proposed. In addition, when the user is in a hurry, voice input can be prioritized so that the difficult range can be input quickly. This allows more appropriate input by adjusting the input method according to the user's emotion. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the input method.

[0077] The reception unit can analyze the user's past learning history and automatically suggest the weak range. The reception unit, for example, analyzes the user's past learning history. The learning history includes, for example, past learning contents, learning time, learning results, etc., but is not limited to such examples. The reception unit, for example, analyzes the user's past learning history and automatically suggests the weak range. For example, it analyzes the tendency of questions that the user has made mistakes in the past and suggests the weak range. It is also possible to evaluate the understanding level in a specific range from the user's past learning history and automatically display the weak range. It is also possible to identify the range that the user has avoided in the past and suggest that range. In this way, it is possible to automatically identify the weak range of the user by analyzing the past learning history. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's learning history data to the generation AI and cause the generation AI to execute the suggestion of the weak range.

[0078] The reception unit can guide the input of the weak range based on the user's current learning progress. The reception unit, for example, evaluates the user's current learning progress. The learning progress includes, for example, the current learning content, progress status, and achievement level, but is not limited to such examples. The reception unit, for example, guides the input of the weak range based on the user's current learning progress. For example, based on the range the user is currently studying, a related weak range is suggested. In addition, the reception unit can evaluate the user's current learning progress and guide the weak range to be studied next. In addition, the difficulty level of the problem the user is currently working on can be taken into consideration and the user can be prompted to input an appropriate weak range. As a result, input can be guided based on the current learning progress, making it possible to input an appropriate range. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's learning progress data to the generation AI and cause the generation AI to execute the input guide.

[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. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such 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, when the user is nervous, an interface with calm colors is provided to reduce visual stress. Also, when the user is enjoying himself, an interface with bright colors is provided to make input work enjoyable. Also, when the user is tired, a simple and highly visible interface is provided to make input work easier. This allows for more comfortable input by adjusting the design of the interface according to the user's emotion. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the interface design.

[0080] The reception unit can propose region-specific study tasks in consideration of the geographical location information of the user. The reception unit, for example, considers the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, and region code, but is not limited to such examples. The reception unit, for example, proposes region-specific study tasks in consideration of the geographical location information of the user. For example, a specific study task is proposed based on the educational curriculum of the region where the user lives. In addition, regional educational events and resources can be proposed based on the geographical location information of the user. In addition, appropriate study tasks can be proposed according to the educational level of the region where the user lives. In this way, region-specific study tasks can be proposed by considering the geographical location information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute the proposal of study tasks.

[0081] The reception unit can analyze the social media activity of the user and suggest the relevant weak range. The reception unit, for example, analyzes the social media activity of the user. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. The reception unit, for example, analyzes the social media activity of the user and suggests the relevant weak range. For example, the learning content shared by the user on social media is analyzed and the weak range is suggested. In addition, the reception unit can also suggest the relevant weak range based on the activity of the learning community on the user's social media. In addition, the content of the educational account that the user follows on social media can be analyzed and the weak range can be suggested. In this way, the weak range of the user can be identified by analyzing the social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the social media data of the user to the generation AI and cause the generation AI to execute the suggestion of the weak range.

[0082] The generation unit can estimate the user's emotion and adjust the way of expressing the problem based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The generation unit, for example, estimates the user's emotion and adjusts the way of expressing the problem based on the estimated user's emotion. For example, when the user is relaxed, a problem that proceeds at a leisurely pace is generated. Also, when the user is in a hurry, a concise problem that focuses on the main points can be generated. Also, when the user is excited, a problem with a visually stimulating effect can be generated. In this way, by adjusting the way of expressing the problem according to the user's emotion, a more appropriate problem can be provided. A part 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 the user's emotion data to the generation AI and cause the generation AI to adjust the way of expressing the problem.

[0083] The generating unit can customize the format of the questions according to the learning style of the user at the time of generation. The generating unit, for example, customizes the format of the questions according to the learning style of the user at the time of generation. The learning style includes, for example, visual type, auditory type, experiential type, etc., but is not limited to such examples. For example, if the user is a visual learner, the generating unit generates questions that make extensive use of figures and graphs. Also, if the user is an auditory learner, the generating unit can generate questions that include audio explanations. Also, if the user is an experiential learner, practical exercise questions can be generated. This enables more effective learning by customizing the format of the questions according to the learning style. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the learning style data of the user to the generating AI and cause the generating AI to customize the format of the questions.

[0084] The generating unit can generate more effective questions by referring to the past question generation history at the time of generation. The generating unit, for example, refers to the past question generation history at the time of generation. The question generation history includes, for example, questions generated in the past, the generation date and time, and the generation conditions, but is not limited to such examples. The generating unit, for example, analyzes the tendency of questions solved by the user in the past and generates similar questions. In addition, the generating unit can generate questions with adjusted difficulty based on questions that the user could not solve in the past. In addition, the generating unit can generate questions of appropriate difficulty by considering the solution time of the questions solved by the user in the past. In this way, more effective questions can be provided by referring to the past question generation history. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input past question generation history data to the generating AI and cause the generating AI to execute optimization of question generation.

[0085] The generation unit can estimate the user's emotion and adjust the difficulty of the problem based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The generation unit, for example, estimates the user's emotion and adjusts the difficulty of the problem based on the estimated user's emotion. For example, when the user is stressed, an easy problem is generated. Also, when the user is relaxed, a problem of moderate difficulty can be generated. Also, when the user is excited, a challenging problem can be generated. In this way, by adjusting the difficulty of the problem according to the user's emotion, a problem of appropriate difficulty can be provided. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the difficulty of the problem.

[0086] The generation unit can adjust the content of the problem based on the user's learning environment at the time of generation. The generation unit, for example, adjusts the content of the problem based on the user's learning environment at the time of generation. The learning environment includes, for example, the learning location, the device used, the surrounding noise level, etc., but is not limited to such examples. For example, when the user is studying in a quiet environment, the generation unit generates a problem that requires concentration. In addition, when the user is studying in a noisy environment, it can also generate a problem that can be solved in a short time. In addition, when the user is studying while moving, it can also generate a problem optimized for a mobile device. In this way, by adjusting the content of the problem based on the learning environment, it is possible to provide a more appropriate problem. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's learning environment data to the generation AI and cause the generation AI to adjust the content of the problem.

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

[0088] The providing unit can estimate the user's emotion and adjust the method of providing the questions based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The providing unit, for example, estimates the user's emotion and adjusts the method of providing the questions based on the estimated user's emotion. For example, when the user is nervous, a display method with simple and high visibility is provided. Also, when the user is relaxed, a display method including detailed information can be provided. Also, when the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide more appropriate questions by adjusting the provision method according to the user's emotion. A part 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 emotion data to the generation AI and cause the generation AI to adjust the provision method.

[0089] The providing unit can provide questions at optimal timing by referring to the user's learning history at the time of providing. The providing unit, for example, refers to the user's learning history at the time of providing. The learning history includes, for example, past learning content, learning time, learning results, etc., but is not limited to such examples. The providing unit provides questions at optimal timing, for example, based on the time period in which the user studied in the past. In addition, it is also possible to provide questions at a time period when the user's concentration is high based on the user's learning history. In addition, it is also possible to provide questions at an appropriate timing by taking into account the answering time of questions that the user answered in the past. In this way, it is possible to provide questions at optimal timing by referring to the learning history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's learning history data to the generating AI and cause the generating AI to execute optimization of the timing of provision.

[0090] The providing unit can provide the problem in an optimal format by considering the device information of the user at the time of providing. For example, the providing unit can consider the device information of the user at the time of providing. The device information includes, for example, the type of device, the screen size, the OS version, etc., but is not limited to such examples. For example, when the user is using a smartphone, the providing unit can provide a display method that matches the screen size. In addition, when the user is using a tablet, it can also provide a display method optimized for a large screen. In addition, when the user is using a personal computer, it can also provide a display method including detailed information. In this way, by considering the device information, it is possible to provide the problem in an optimal format. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information data of the user to the generating AI and cause the generating AI to execute optimization of the display format.

[0091] The providing unit can estimate the user's emotion and adjust the display order of the questions based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The providing unit, for example, estimates the user's emotion and adjusts the display order of the questions based on the estimated user's emotion. For example, when the user is nervous, the questions are displayed in order from easy to difficult. Also, when the user is relaxed, the questions can be displayed in order from difficult to difficult. Also, when the user is in a hurry, the questions can be displayed in order from the most important points. In this way, the display order can be adjusted according to the user's emotion, so that the questions can be provided in a more appropriate order. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display order.

[0092] The providing unit can provide region-specific problems by considering the geographical location information of the user at the time of providing. The providing unit, for example, considers the geographical location information of the user at the time of providing. The geographical location information includes, for example, GPS data, IP address, region code, etc., but is not limited to such examples. The providing unit, for example, provides specific problems based on the educational curriculum of the region where the user lives. In addition, it is also possible to provide regional educational events and resources based on the geographical location information of the user. In addition, it is also possible to provide appropriate problems according to the educational level of the region where the user lives. In this way, region-specific problems can be provided by considering the geographical location information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information data of the user to the generating AI and cause the generating AI to provide region-specific problems.

[0093] The providing unit can analyze the social media activity of the user at the time of providing and provide related questions. The providing unit, for example, analyzes the social media activity of the user at the time of providing. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. The providing unit, for example, analyzes the learning content shared by the user on social media and provides related questions. Also, related questions can be provided based on the activity of the learning community on the user's social media. Also, related questions can be provided by analyzing the content of educational accounts that the user follows on social media. In this way, related questions can be provided by analyzing social media activities. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the social media data of the user to the generating AI and cause the generating AI to provide related questions.

[0094] The analysis unit can estimate the user's emotion and adjust the answer analysis method based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The analysis unit, for example, estimates the user's emotion and adjusts the answer analysis method based on the estimated user's emotion. For example, when the user is nervous, a simple and highly visible analysis result is provided. Also, when the user is relaxed, a detailed analysis result can be provided. Also, when the user is in a hurry, an analysis result that covers the main points can be provided. In this way, by adjusting the answer analysis method according to the user's emotion, a more appropriate analysis result can be provided. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to 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 the analysis. The analysis unit, for example, refers to the user's past answer history during the analysis. The answer history includes, for example, correctness of the answer, answer time, details of the answer, and the like, but is not limited to such examples. The analysis unit, for example, analyzes the tendency of questions that the user has answered in the past, and improves the accuracy of the analysis. In addition, it is also possible to identify a specific mistake pattern from the user's past answer history and reflect it in the analysis. In addition, it is also possible to improve the accuracy of the analysis by taking into account the answer time of questions that the user has answered in the past. As a result, the accuracy of the analysis is improved by referring to the past answer history. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's past answer history data to the generation AI and cause the generation AI to execute optimization of the analysis algorithm.

[0096] The analysis unit can customize the analysis algorithm according to the learning style of the user at the time of analysis. The analysis unit, for example, customizes the analysis algorithm according to the learning style of the user at the time of analysis. The learning style includes, for example, visual type, auditory type, experiential type, etc., but is not limited to such examples. For example, if the user is a visual learner, the analysis unit provides an analysis result that makes extensive use of figures and graphs. Also, if the user is an auditory learner, the analysis unit can provide an analysis result that includes audio explanations. Also, if the user is an experiential learner, the analysis unit can provide an analysis result that includes practical exercises. This enables more effective analysis by customizing the analysis algorithm according to the learning style. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's learning style data to the generation AI and cause the generation AI to execute customization of the analysis algorithm.

[0097] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, a simple and highly visible display method is provided. Also, when the user is relaxed, a display method including detailed information can be provided. Also, when the user is in a hurry, a display method that focuses on the main points can be provided. As a result, by adjusting the display method according to the user's emotion, a more appropriate analysis result can be provided. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0098] The analysis unit can improve the accuracy of the analysis by considering the geographical location information of the user during the analysis. The analysis unit, for example, considers the geographical location information of the user during the analysis. The geographical location information includes, for example, GPS data, IP address, and area code, but is not limited to such examples. The analysis unit improves the accuracy of the analysis, for example, based on the educational curriculum of the area where the user lives. In addition, based on the geographical location information of the user, an analysis according to the educational level of the area can be performed. In addition, the accuracy of the analysis can be improved by considering educational events and resources in the area where the user lives. As a result, the accuracy of the analysis is improved by considering the geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute optimization of the analysis algorithm.

[0099] The analysis unit can analyze the social media activity of the user at the time of analysis and provide related analysis results. The analysis unit, for example, analyzes the social media activity of the user at the time of analysis. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. The analysis unit, for example, analyzes the learning content shared by the user on social media and provides related analysis results. In addition, the analysis unit can provide related analysis results based on the activity of the learning community on the user's social media. In addition, the analysis unit can analyze the content of the educational accounts that the user follows on social media and provide related analysis results. In this way, by analyzing the social media activity, related analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the social media data of the user to the generation AI and cause the generation AI to provide the analysis results.

[0100] The storage unit can estimate the user's emotion and select the data to be saved based on the estimated user's emotion. The storage unit, for example, estimates the user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The storage unit, for example, estimates the user's emotion and selects the data to be saved based on the estimated user's emotion. For example, when the user is stressed, only important data is saved. Also, when the user is relaxed, detailed data can be saved. Also, when the user is in a hurry, data with the main points can be saved. In this way, by selecting the data to be saved according to the user's emotion, more important data can be saved. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the user's emotion data to the generation AI and cause the generation AI to select the data to be saved.

[0101] The storage unit can optimize the storage algorithm by referring to the past stored data at the time of storage. The storage unit, for example, refers to the past stored data at the time of storage. The stored data includes, for example, the type of data stored in the past, the storage date and time, the storage conditions, and the like, but is not limited to such examples. The storage unit, for example, analyzes the tendency of data stored in the past by the user and proposes an optimal storage method. In addition, it is also possible to identify a specific pattern from the user's past stored data and optimize the storage algorithm. In addition, it is also possible to evaluate the importance of data stored in the past by the user and propose an optimal storage method. In this way, the storage algorithm can be optimized by referring to the past stored data. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the past stored data to the generation AI and cause the generation AI to execute optimization of the storage algorithm.

[0102] The storage unit can estimate the user's emotion and determine the priority of the stored data based on the estimated user's emotion. The storage unit, for example, estimates the user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The storage unit, for example, estimates the user's emotion and determines the priority of the stored data based on the estimated user's emotion. For example, when the user is stressed, important data is preferentially saved. Also, when the user is relaxed, detailed data can be preferentially saved. Also, when the user is in a hurry, data with the main points can be preferentially saved. In this way, important data can be preferentially saved by determining the priority of the stored data according to the user's emotion. A part 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 the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the stored data.

[0103] The storage unit can weight the stored data in consideration of the geographical location information of the user at the time of storage. The storage unit, for example, considers the geographical location information of the user at the time of storage. The geographical location information includes, for example, GPS data, IP address, and area code, but is not limited to such examples. The storage unit, for example, weights the stored data based on the educational curriculum of the area where the user lives. In addition, the storage unit can weight the stored data according to the educational level of the area based on the geographical location information of the user. In addition, the stored data can be weighted in consideration of educational events and resources in the area where the user lives. In this way, the weighting of the stored data can be optimized by considering the geographical location information. Part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute the weighting of 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 that the user has had difficulty with in the past based on the user's learning history and assist with input. For example, the reception unit can analyze the accuracy rate and answer time of questions that the user has answered in the past to evaluate the degree of difficulty in a specific area. 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 a specific topic from the user's learning history and automatically display areas that the user has difficulty with. This makes it possible to more accurately identify areas that the user has difficulty with by utilizing the user's past learning history and assist with input.

[0106] The generator can customize the question format according to the user's learning style. For example, questions that make extensive use of figures and graphs can be generated for visual learners, and questions that include audio explanations can be generated 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 makes it possible to provide optimal questions according to the user's learning style and improve the effectiveness of learning.

[0107] The generation unit can select the theme of the questions based on the user's interests. For example, if the user is interested in sports, questions related to sports are generated, and if the user is interested in music, questions related to music are generated. Also, if the user is interested in science, questions related to science can be generated. Furthermore, the user's past learning history and social media activity can be analyzed to provide questions based on the user's interests. This makes it possible to provide questions according to the user's interests and increase the user's motivation to learn.

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

[0109] The analysis unit can improve the accuracy of the analysis by referring to the user's past answer history. For example, the analysis unit can analyze the correctness and answer time of questions that the user has answered in the past to identify specific mistake patterns. The analysis unit can also evaluate the user's understanding of a specific topic from the user's past answer history and reflect this in the analysis. Furthermore, the analysis unit can analyze the trends of questions that the user has answered in the past to optimize the analysis algorithm. In this way, the analysis accuracy 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 the difficult range based on the estimated user's emotions. For example, when the user is stressed, a simple interface is provided to minimize the input procedure. Also, when the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, when the user is in a hurry, voice input can be prioritized to enable the user to quickly input the difficult range. In this way, by adjusting the input method according to the user's emotions, more appropriate input is possible.

[0111] The generation unit can estimate the user's emotions and adjust the way questions are presented based on the estimated user's 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, more appropriate questions can be provided by adjusting the way questions are presented according to the user's emotions.

[0112] The providing unit can estimate the user's emotions and adjust the way questions are provided based on the estimated user's emotions. For example, if the user is nervous, a simple and 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 method of provision according to the user's emotions, more appropriate question provision is possible.

[0113] The analysis unit can estimate the user's emotions and adjust the answer analysis method based on the estimated user's emotions. For example, if the user is nervous, a simple and 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's emotions. For example, if the user is stressed, only important data is saved. Also, if the user is relaxed, detailed data can be saved. Furthermore, if the user is in a hurry, data with 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 process flow of the second embodiment will be briefly described below.

[0116] Step 1: The reception unit inputs the user's weak areas. The weak areas of the user include, but are not limited to, specific areas or topics of mathematics. The reception unit provides, for example, an interface for the user to input the specific area of ​​mathematics. Step 2: The generation unit uses the 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 range of mathematics. For example, the generation AI uses an algorithm for generating questions related to a specific range of mathematics. 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 portion to the generated problem. For example, the providing unit adds an introductory portion 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice 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<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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of 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 specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such 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 above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0120] 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 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 the range in which the user is weak. 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 answer and adjusts the difficulty level. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes 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, the RAM 30, and the 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 the voice according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average 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 performed in a secure state.

[0128] Fig. 4 shows an example of 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of 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 specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0136] 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 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 the range in which the user is weak. 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 answer and adjusts the difficulty level. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes 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, the RAM 30, and the 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 the voice according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average 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 performed 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0147] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) 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 a voice indicating a user input for 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.

[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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of 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 specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[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 the range in which the user is weak. 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 answer and adjusts the difficulty level. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes 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, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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, the RAM 30, and the 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 the voice according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of 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 performed in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0164] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) 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 a voice indicating a user input for 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of 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 specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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 the range in which the user is weak. 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 answer and adjusts the difficulty level. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0171] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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 inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0175] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," 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 Universal Serial Bus (USB) 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] In addition, 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 upon 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] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0182] The hardware resource that executes the specific process may be one of these various processors, or may be 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 configuration using one 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 configuration using a processor that realizes the functions of the 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, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0186] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0188] (Appendix 1) A reception unit for inputting the user's weak points; a generator that generates questions based on the information input by the reception unit; 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 is Generate problems using generative AI 2. The system of claim 1, (Appendix 3) The generation unit is 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 introduction to the generated question. (Appendix 5) The analysis unit is Analyze user's answers and adjust the 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 a user's answer history. (Appendix 7) The reception unit is The system estimates the user's emotions and adjusts the input method for difficult areas based on the estimated user emotions. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past learning history and automatically suggest areas of difficulty 2. The system of claim 1, (Appendix 9) The reception unit is Guide users through difficult areas based on their current learning progress 2. The system of claim 1, (Appendix 10) The reception unit is Estimating user emotions and adjusting the design of the input interface based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The reception unit is Considering the user's geographic location, it suggests location-specific learning tasks 2. The system of claim 1, (Appendix 12) The reception unit is Analyze users' social media activity and suggest relevant weaknesses 2. The system of claim 1, (Appendix 13) The generation unit is Infer user emotions and adjust problem wording based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The generation unit is Customize question format at generation time according to user's learning style 2. The system of claim 1, (Appendix 15) The generation unit is When generating questions, refer to the past question generation history to generate more effective questions. 2. The system of claim 1, (Appendix 16) The generation unit is 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 is At generation time, the content of the questions is adjusted based on the user's learning environment 2. The system of claim 1, (Appendix 18) The generation unit is 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 is 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 is When providing a question, the system refers to the user's learning history and provides the question at the optimal timing. 2. The system of claim 1, (Appendix 21) The providing unit is 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 is Infer user sentiment and adjust question display order based on the inferred user sentiment 2. The system of claim 1, (Appendix 23) The providing unit is At the time of delivery, the app takes into account the user's geographic location to provide location-specific questions 2. The system of claim 1, (Appendix 24) The providing unit is When provided, analyze your social media activity and provide you with relevant issues 2. The system of claim 1, (Appendix 25) The analysis unit is Inferring user emotions and adjusting answer analysis methods based on the estimated user emotions 2. The system of claim 1, (Appendix 26) The analysis unit is 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 is 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 is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 29) The analysis unit is 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 is 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 includes: Inferring user emotions and selecting data to store based on the estimated user emotions 2. The system of claim 1, (Appendix 32) The storage unit includes: When saving, optimize the saving algorithm by referencing past saved data. 2. The system of claim 1, (Appendix 33) The storage unit includes: Inferring user emotions and prioritizing stored data based on the estimated user emotions 2. The system of claim 1, (Appendix 34) The storage unit includes: When storing, the stored 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 for inputting the user's weak points; a generator that generates questions based on the information input by the reception unit; 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:

2. The generation unit is Generate problems using generative AI 2. The system of claim 1 .

3. The generation unit is Generate questions based on specific math areas 2. The system of claim 1 .

4. The providing unit is Add an introduction to the problem generated by the generator 2. The system of claim 1 .

5. The generation unit is Selecting a topic based on the user's interests 2. The system of claim 1 .

6. The reception unit is Analyze the user's social media activity and suggest relevant weaknesses 2. The system of claim 1 .

7. The reception unit is The user's emotion is estimated, and an input method for a difficult range is adjusted based on the estimated emotion of the user.

2. The system of claim 1 .

8. The reception unit is Analyze the user's past learning history and automatically suggest areas of difficulty 2. The system of claim 1 .

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