system

Abstract

The system according to this embodiment aims to propose an optimal learning plan for each individual user and provide explanations in real time. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an explanation unit. The collection unit collects the user's problem-solving process and results. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. The explanation unit provides explanations to the user's questions in real time.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to propose an optimal learning plan for each individual user and to provide explanations in real time.

[0005] The system according to the embodiment aims to propose an optimal learning plan for each individual user and to provide explanations in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an explanation unit. The data collection unit collects the user's problem-solving process and results. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. The explanation unit provides explanations to the user's questions in real time. [Effects of the Invention]

[0007] The system according to this embodiment can propose an optimal learning plan for each individual user and provide explanations in real time. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The ultimate AI teacher system according to an embodiment of the present invention is a system for improving academic ability using generative AI. In this system, the user (student) inputs the process and results of solving problems on a tablet or smartphone into the generative AI. The generative AI analyzes data such as the speed of solving each problem, the content of the calculation formula, the score, and the correct answer rate, and comprehensively analyzes the user's weaknesses and challenges. Next, based on the analysis results, the generative AI proposes an optimal learning plan for the user. Furthermore, it provides detailed explanations of the solution method and related knowledge for each problem using diagrams, videos, and audio, refining the explanation method and explaining it repeatedly until the user understands. It also provides real-time explanations to questions from the user via voice. For example, if the ultimate AI teacher system receives a voice question such as "I don't know how to solve this problem," the generative AI analyzes the question and provides a detailed explanation of the solution using diagrams, videos, and audio. In addition, based on the user's problem-solving process and results, the generative AI comprehensively analyzes weaknesses and challenges and outputs a plan for future countermeasures. For example, if a user gets stuck on a particular calculation problem, the generative AI repeatedly explains the solution method for that problem and supports the user until they understand. In this way, the ultimate AI teacher system using generative AI can provide completely personalized support 24 / 7, 365 days a year, helping to improve children's academic performance. Furthermore, in cram schools, it can contribute to increased sales and alleviate teacher shortages by analyzing students' weaknesses and proposing optimal learning plans. This allows the ultimate AI teacher system to collect and analyze the user's problem-solving process and results, propose learning plans, and provide real-time explanations.

[0029] The ultimate AI teacher system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an explanation unit. The data collection unit collects the user's problem-solving process and results. For example, the data collection unit can collect the process and results of a user solving problems on a tablet or smartphone. The data collection unit can collect data such as the speed at which the user solves problems, the content of the calculation formulas, scores, and correct answer rates. For example, the data collection unit can record the speed at which a user solves problems on a tablet in seconds. The data collection unit can also collect the content of the calculation formulas for problems solved by a user on a smartphone as text data. Furthermore, the data collection unit can collect scores and correct answer rates for problems solved by a user as numerical data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the collected data using statistical analysis or machine learning algorithms. Based on data such as the user's solving speed, the content of the calculation formulas, scores, and correct answer rates, the analysis unit can comprehensively analyze the user's weaknesses and challenges. The analysis unit can, for example, analyze the distribution of users' problem-solving speeds and calculate the mean and standard deviation. It can also analyze the content of users' calculation formulas and identify error patterns. Furthermore, it can analyze users' scores and accuracy rates to identify weaknesses in certain types of problems. The suggestion unit proposes a learning plan based on the analysis results obtained by the analysis unit. For example, the suggestion unit can propose a learning plan tailored to the user's weaknesses and challenges. It can propose an optimal learning plan based on the user's learning goals and schedule. For example, it can propose a plan that focuses on problems the user struggles with. It can also propose an efficient learning plan that aligns with the user's learning schedule. Furthermore, the suggestion unit can suggest appropriate learning materials and resources according to the user's learning goals. The explanation unit provides real-time explanations to user questions. For example, it can analyze the content of user voice questions and provide detailed explanations of problem-solving methods and related knowledge using diagrams, videos, and audio. The explanation section can refine its explanation methods and repeat them until the user understands.The explanation unit can, for example, analyze a user's voice question such as, "I don't know how to solve this problem," and provide a detailed explanation of the solution using diagrams, videos, and audio. The explanation unit can also try different explanation methods until the user understands. Furthermore, it can adjust the content and method of explanation according to the user's level of understanding. As a result, the ultimate AI teacher system according to this embodiment can collect and analyze the user's problem-solving process and results, propose a learning plan, and provide explanations in real time.

[0030] The data collection unit collects the user's problem-solving process and results. For example, the unit can collect the process and results of a user solving a problem on a tablet or smartphone. Specifically, it records the speed at which a user solves a problem on a tablet in seconds and collects the content of the calculation formula as text data. It can also collect the score and correct answer rate of a problem solved by a user on a smartphone as numerical data. The data collection unit collects this data in real time and transmits it to a central database. Furthermore, the data collection unit can utilize the screen capture function of tablets and smartphones to record the user's solution process in detail. This allows for a visual confirmation of how the user solved the problem. The data collection unit centrally manages the user's solution data and can link with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively and improve the overall system performance. Furthermore, the data collection unit can anonymize or encrypt data to protect user privacy. This allows for the secure management of users' personal information and prevents unauthorized access to or leakage of data.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the collected data using statistical analysis and machine learning algorithms. Specifically, it comprehensively analyzes the user's weaknesses and challenges based on data such as the user's solving speed, the content of the calculation formulas, scores, and accuracy rates. The analysis unit can analyze the distribution of the user's solving speed and calculate the mean and standard deviation. It can also analyze the content of the user's calculation formulas and identify error patterns. Furthermore, it can analyze the user's scores and accuracy rates to identify the types of problems that are the user's weaknesses. Based on this data, the analysis unit can evaluate the user's learning performance and provide feedback tailored to individual learning needs. The analysis unit processes data in real time using AI to understand the user's learning status. For example, it can use machine learning algorithms to analyze the user's answer patterns and evaluate their understanding of specific problems and the accuracy of their answers. It can also use natural language processing technology to analyze the user's calculation formulas and answer content to identify the causes of errors and areas for improvement. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term learning trends and performance fluctuations. This allows the analysis unit to comprehensively evaluate the user's learning progress and provide feedback tailored to their individual learning needs.

[0032] The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. For example, the proposal unit can propose a learning plan tailored to the user's weaknesses and challenges. Specifically, it proposes an optimal learning plan based on the user's learning goals and schedule. The proposal unit can propose a plan that focuses on learning the user's weak points. It can also propose an efficient learning plan that matches the user's learning schedule. Furthermore, the proposal unit can suggest appropriate learning materials and resources according to the user's learning goals. The proposal unit uses AI to analyze the user's learning data and generate an optimal learning plan tailored to individual learning needs. For example, it uses machine learning algorithms to evaluate the user's learning performance and propose the optimal learning content and order. It can also use natural language processing technology to analyze the user's learning goals and feedback and propose learning materials and resources tailored to individual learning needs. Furthermore, the proposal unit can monitor the user's learning progress in real time and continuously adjust the learning plan. This allows the proposal unit to maximize the user's learning effectiveness and support efficient learning.

[0033] The explanation unit provides real-time explanations to user questions. For example, it can analyze a user's voice question and provide detailed explanations of problem solutions and related knowledge using diagrams, videos, and audio. Specifically, if a user asks a voice question like, "I don't know how to solve this problem," the unit will analyze the question and provide a detailed explanation of the solution using diagrams, videos, and audio. The explanation unit can also try different explanation methods until the user understands. Furthermore, the explanation unit can adjust the content and method of the explanation according to the user's level of understanding. The explanation unit uses AI to analyze user questions and provide the optimal explanation method. For example, it uses natural language processing technology to analyze the user's question and identify related knowledge and solutions. It can also use image recognition technology to analyze diagrams and graphs of problems and provide visual explanations. Furthermore, the explanation unit can monitor the user's level of understanding in real time and continuously adjust the explanation content. This allows the explanation unit to maximize the user's learning effectiveness and support efficient learning. The explanation unit can collect user feedback and continuously improve the accuracy and effectiveness of the explanation content. For example, based on user feedback, the explanation methods and content are reviewed to provide more effective explanations. Furthermore, the explanation team can reliably transmit information using multiple communication methods. For instance, important information is delivered reliably by using voice calls, chat, and email in combination. This allows the explanation team to provide explanations quickly and reliably to users, maximizing learning effectiveness.

[0034] The system includes an explanatory section in which the generating AI provides detailed explanations using diagrams, videos, and audio. The explanatory section allows the generating AI to provide detailed explanations using diagrams, videos, and audio. For example, the explanatory section can allow the generating AI to provide detailed explanations of problem solutions using diagrams. The explanatory section can also allow the generating AI to provide detailed explanations of problem solutions using videos. The explanatory section can also allow the generating AI to provide detailed explanations of problem solutions using audio. For example, when the generating AI provides detailed explanations of problem solutions using diagrams, the explanatory section can display the diagrams step by step while explaining. Also, when the generating AI provides detailed explanations of problem solutions using videos, the explanatory section can play the videos while explaining. Furthermore, when the generating AI provides detailed explanations of problem solutions using audio, the explanatory section can provide audio explanations while explaining. This allows the user to deepen their understanding by having the generating AI provide detailed explanations using diagrams, videos, and audio. The generating AI is, for example, a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the above-described processing in the explanatory section may be performed using AI, for example, or without AI. For example, the explanation section can input the solution to a problem into a generating AI, which can then generate an explanation using diagrams, videos, and audio.

[0035] The system includes an analysis unit that comprehensively analyzes users' weaknesses and challenges. The analysis unit can comprehensively analyze users' weaknesses and challenges. For example, it can comprehensively analyze users' weaknesses and challenges based on data such as the user's solving speed, the content of calculation formulas, scores, and accuracy rates. The analysis unit can analyze the distribution of users' solving speeds and calculate the mean and standard deviation. It can also analyze the content of users' calculation formulas and identify error patterns. Furthermore, the analysis unit can analyze users' scores and accuracy rates to identify trends in problems where users struggle. For example, by analyzing the distribution of users' solving speeds and calculating the mean and standard deviation, the analysis unit can grasp trends in users' solving speeds. It can also analyze the content of users' calculation formulas and identify error patterns to identify the causes of users' calculation errors. Furthermore, by analyzing users' scores and accuracy rates and identifying trends in problems where users struggle, the analysis unit can clarify users' learning challenges. This allows for a more effective learning plan to be proposed by comprehensively analyzing the user's weaknesses and challenges. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data and output the analysis results.

[0036] The system includes a countermeasure unit that outputs future countermeasure plans. The countermeasure unit can output future countermeasure plans. For example, the countermeasure unit can output future countermeasure plans based on the user's weaknesses and challenges. The countermeasure unit can output optimal countermeasure plans based on the user's learning goals and schedule. For example, the countermeasure unit can output a countermeasure plan that focuses on learning the user's weak points. The countermeasure unit can also output an efficient countermeasure plan that matches the user's learning schedule. Furthermore, the countermeasure unit can output a countermeasure plan that suggests appropriate learning materials and resources according to the user's learning goals. For example, by outputting a countermeasure plan that focuses on learning the user's weak points, the user can learn efficiently. Furthermore, by outputting an efficient countermeasure plan that matches the user's learning schedule, the user can learn without difficulty. Furthermore, by outputting a countermeasure plan that suggests appropriate learning materials and resources according to the user's learning goals, the user can learn effectively. In this way, by outputting future countermeasure plans, the user can learn effectively. Some or all of the above processing in the countermeasure unit may be performed using AI, for example, or without using AI. For example, the countermeasures unit can input the user's weaknesses and challenges into a generating AI, which can then output the optimal countermeasure plan.

[0037] The data collection unit can collect the process and results of a user solving problems on a tablet or smartphone. For example, the data collection unit can collect the process and results when a user solves problems on a tablet. The data collection unit can also collect the process and results when a user solves problems on a smartphone. For example, the data collection unit can record the process of a user solving problems on a tablet in seconds. The data collection unit can also collect the content of the calculation formulas used by the user when solving problems on a smartphone as text data. Furthermore, the data collection unit can collect the score and correct answer rate of the problems solved by the user as numerical data. This makes data collection more efficient by collecting the process and results of a user solving problems on a tablet or smartphone. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from problems solved by the user on a tablet or smartphone into a generating AI, which can then analyze the data and output the collection results.

[0038] The analysis unit can analyze data such as the speed at which problems are solved, the content of the calculation formulas, the score, and the accuracy rate for each problem. For example, the analysis unit can analyze the speed at which problems are solved in seconds. The analysis unit can also analyze the content of the calculation formulas for each problem. The analysis unit can also analyze the score and accuracy rate for each problem. For example, the analysis unit can analyze the speed at which problems are solved in seconds and calculate the mean and standard deviation. The analysis unit can also analyze the content of the calculation formulas for each problem and identify patterns of errors. Furthermore, the analysis unit can analyze the score and accuracy rate for each problem and identify tendencies in problems that the user is weak at. In this way, by analyzing data such as the speed at which problems are solved, the content of the calculation formulas, the score, and the accuracy rate for each problem, the user's learning progress can be understood in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the collected data into a generating AI, and the generating AI can analyze the data and output the analysis results.

[0039] The data collection unit can analyze the user's past learning history and select the optimal data collection method. For example, the data collection unit can prioritize collecting learning methods that the user has frequently used in the past. The data collection unit can also suggest effective data collection methods based on the user's past learning history. The data collection unit can also analyze the user's learning history and determine the optimal data collection timing. For example, the data collection unit can select an effective data collection method for the user by prioritizing the collection of learning methods that the user has frequently used in the past. Furthermore, the data collection unit can improve the user's learning efficiency by suggesting effective data collection methods based on the user's past learning history. In addition, the data collection unit can maximize the user's learning effectiveness by analyzing the user's learning history and determining the optimal data collection timing. Thus, by analyzing the user's past learning history, the optimal data collection method can be selected. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's past learning history data into a generating AI, which can then analyze the data and select the optimal data collection method.

[0040] The data collection unit can filter problem-solving processes based on the user's current learning status and areas of interest. For example, the data collection unit can prioritize collecting data related to the subject the user is currently studying. The data collection unit can also collect relevant problem-solving processes based on the user's areas of interest. The data collection unit can also filter and collect appropriate data considering the user's learning status. For example, the data collection unit can improve the user's learning efficiency by prioritizing the collection of data related to the subject the user is currently studying. Furthermore, the data collection unit can pique the user's interest by collecting relevant problem-solving processes based on the user's areas of interest. In addition, the data collection unit can maximize the user's learning effectiveness by filtering and collecting appropriate data considering the user's learning status. This allows for the collection of highly relevant data by filtering based on the user's current learning status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current learning status and areas of interest into a generating AI, which can then analyze and filter the data.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting problem-solving processes. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. The data collection unit can also collect region-specific problem-solving processes based on the user's location information. The data collection unit can also collect optimal data by considering the user's geographical location information. For example, if the user is in a specific region, the data collection unit can collect region-specific problem-solving processes by prioritizing the collection of data related to that region. Furthermore, by collecting region-specific problem-solving processes based on the user's location information, the data collection unit can maximize the user's learning effect. In addition, by collecting optimal data while considering the user's geographical location information, the data collection unit can improve the user's learning efficiency. This allows for the collection of region-specific problem-solving processes by collecting highly relevant data while considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI, which can analyze the data and prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data when collecting problem-solving processes. For example, the data collection unit can collect data related to topics of interest from the user's social media activity. The data collection unit can also collect relevant problem-solving processes based on information shared by the user on social media. The data collection unit can also analyze the user's social media activity and collect optimal data. For example, the data collection unit can attract the user's interest by collecting data related to topics of interest from the user's social media activity. Furthermore, the data collection unit can maximize the user's learning effect by collecting relevant problem-solving processes based on information shared by the user on social media. In addition, the data collection unit can improve the user's learning efficiency by analyzing the user's social media activity and collecting optimal data. This allows for the collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then analyze the data and collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the problem during the analysis. For example, the analysis unit can perform a detailed analysis for important problems. For less important problems, the analysis unit can perform a concise analysis. The analysis unit can also adjust the level of detail of the analysis according to the importance of the problem. For example, by performing a detailed analysis for important problems, the analysis unit can deepen the user's understanding. Also, by performing a concise analysis for less important problems, the analysis unit can reduce the burden on the user. Furthermore, by adjusting the level of detail of the analysis according to the importance of the problem, the analysis unit can improve the user's learning efficiency. In this way, analysis can be performed efficiently by adjusting the level of detail of the analysis based on the importance of the problem. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input problem importance data into a generating AI, and the generating AI can analyze the data and adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the problem during analysis. For example, the analysis unit can apply a mathematical formula analysis algorithm to a mathematics problem. The analysis unit can also apply a grammatical analysis algorithm to an English problem. The analysis unit can also apply a scientific data analysis algorithm to a science problem. For example, the analysis unit can analyze the solution to a mathematics problem in detail by applying a mathematical formula analysis algorithm. Furthermore, the analysis unit can identify grammatical errors in an English problem by applying a grammatical analysis algorithm. In addition, the analysis unit can analyze experimental data by applying a scientific data analysis algorithm to a science problem. This allows for the provision of more appropriate analysis results by applying different analysis algorithms depending on the category of the problem. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input problem category data into an AI that generates data, and the generating AI can analyze the data and apply different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the submission date of the problems during the analysis process. For example, the analysis unit can prioritize the analysis of problems with approaching deadlines. The analysis unit can also postpone the analysis of problems with distant submission dates. The analysis unit can also adjust the priority of analysis according to the submission date of the problems. For example, by prioritizing the analysis of problems with approaching deadlines, the analysis unit can ensure that users meet their submission deadlines. Conversely, by postponing the analysis of problems with distant submission dates, the analysis unit can perform analysis efficiently. Furthermore, by adjusting the priority of analysis according to the submission date of the problems, the analysis unit can improve the learning efficiency of the users. This allows for efficient analysis by determining the priority of analysis based on the submission date of the problems. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input problem submission date data into a generating AI, which can analyze the data and determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the problems during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant problems. The analysis unit can also postpone the analysis of less relevant problems. The analysis unit can also adjust the order of analysis according to the relevance of the problems. For example, by prioritizing the analysis of highly relevant problems, the analysis unit can improve the user's learning efficiency. Furthermore, by postponing the analysis of less relevant problems, the analysis unit can perform analysis efficiently. In addition, by adjusting the order of analysis according to the relevance of the problems, the analysis unit can maximize the user's learning effect. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the problems. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input problem relevance data into a generating AI, and the generating AI can analyze the data and adjust the order of analysis.

[0047] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the learning plan. For example, the suggestion unit can provide detailed suggestions for important learning plans. For less important learning plans, the suggestion unit can provide concise suggestions. The suggestion unit can also adjust the level of detail of its suggestions according to the importance of the learning plan. For example, by providing detailed suggestions for important learning plans, the suggestion unit can deepen the user's understanding. Also, by providing concise suggestions for less important learning plans, the suggestion unit can reduce the user's burden. Furthermore, by adjusting the level of detail of its suggestions according to the importance of the learning plan, the suggestion unit can improve the user's learning efficiency. This allows for efficient suggestion generation by adjusting the level of detail of suggestions based on the importance of the learning plan. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input learning plan importance data into a generating AI, which can analyze the data and adjust the level of detail of the suggestions.

[0048] The proposal unit can apply different proposal algorithms depending on the category of the learning plan when making a proposal. For example, the proposal unit can apply a mathematical formula analysis algorithm to a mathematics learning plan. The proposal unit can also apply a grammatical analysis algorithm to an English learning plan. The proposal unit can also apply a scientific data analysis algorithm to a science learning plan. For example, the proposal unit can analyze the details of a mathematics learning plan by applying a mathematical formula analysis algorithm. Furthermore, the proposal unit can identify grammatical errors in an English learning plan by applying a grammatical analysis algorithm. In addition, the proposal unit can analyze experimental data in a science learning plan by applying a scientific data analysis algorithm. This allows for the provision of more appropriate proposals by applying different proposal algorithms depending on the category of the learning plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input learning plan category data into a generating AI, which can analyze the data and apply different proposal algorithms.

[0049] The proposal unit can determine the priority of proposals based on the submission timing of the learning plan. For example, the proposal unit can prioritize proposals for learning plans with approaching deadlines. It can also postpone proposals for learning plans with distant submission deadlines. The proposal unit can also adjust the priority of proposals according to the submission timing of the learning plan. For example, by prioritizing proposals for learning plans with approaching deadlines, the proposal unit can ensure that users meet their submission deadlines. Conversely, by postponing proposals for learning plans with distant submission deadlines, the proposal unit can efficiently make proposals. Furthermore, by adjusting the priority of proposals according to the submission timing of the learning plan, the proposal unit can improve the user's learning efficiency. Thus, by determining the priority of proposals based on the submission timing of the learning plan, proposals can be made efficiently. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input learning plan submission timing data into a generating AI, and the generating AI can analyze the data to determine the priority of proposals.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the learning plans. For example, the suggestion unit can prioritize suggesting highly relevant learning plans. The suggestion unit can also postpone suggesting less relevant learning plans. The suggestion unit can also adjust the order of suggestions according to the relevance of the learning plans. For example, by prioritizing highly relevant learning plans, the suggestion unit can improve the user's learning efficiency. Furthermore, by postponing suggesting less relevant learning plans, the suggestion unit can make suggestions efficiently. In addition, by adjusting the order of suggestions according to the relevance of the learning plans, the suggestion unit can maximize the user's learning effect. Thus, by adjusting the order of suggestions based on the relevance of the learning plans, suggestions can be made efficiently. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input learning plan relevance data into a generating AI, and the generating AI can analyze the data and adjust the order of suggestions.

[0051] The explanation unit can adjust the level of detail in its explanations based on the importance of the problem. For example, the explanation unit can provide detailed explanations for important problems. For less important problems, it can provide concise explanations. The explanation unit can also adjust the level of detail in its explanations according to the importance of the problem. For example, by providing detailed explanations for important problems, the explanation unit can deepen the user's understanding. Also, by providing concise explanations for less important problems, the explanation unit can reduce the user's burden. Furthermore, by adjusting the level of detail in its explanations according to the importance of the problem, the explanation unit can improve the user's learning efficiency. This allows for efficient explanations by adjusting the level of detail in the explanations based on the importance of the problem. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input problem importance data into a generating AI, and the generating AI can analyze the data and adjust the level of detail in the explanations.

[0052] The explanation unit can apply different explanation algorithms depending on the category of the problem. For example, for a mathematics problem, the explanation unit can provide an explanation using mathematical formulas. For an English problem, the explanation unit can provide an explanation using grammar and vocabulary. For a science problem, the explanation unit can provide an explanation using experimental data and diagrams. For example, for a mathematics problem, the explanation unit can explain the solution to the problem in detail by using mathematical formulas. For an English problem, the explanation unit can identify grammatical errors by providing an explanation using grammar and vocabulary. Furthermore, for a science problem, the explanation unit can analyze experimental results by providing an explanation using experimental data and diagrams. In this way, by applying different explanation algorithms depending on the category of the problem, a more appropriate explanation can be provided. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input problem category data into a generating AI, and the generating AI can analyze the data and apply a different explanation algorithm.

[0053] The explanation unit can determine the priority of explanations based on the submission date of the problems. For example, the explanation unit can prioritize explanations for problems with approaching deadlines. It can also postpone explanations for problems with distant submission dates. The explanation unit can also adjust the priority of explanations according to the submission date of the problems. For example, by prioritizing explanations for problems with approaching deadlines, the explanation unit can ensure that users meet their submission deadlines. Conversely, by postponing explanations for problems with distant submission dates, the explanation unit can provide explanations efficiently. Furthermore, by adjusting the priority of explanations according to the submission date of the problems, the explanation unit can improve the learning efficiency of users. This allows for efficient explanations by determining the priority of explanations based on the submission date of the problems. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input problem submission date data into a generating AI, which can analyze the data to determine the priority of explanations.

[0054] The explanation unit can adjust the order of explanations based on the relevance of the problems. For example, the explanation unit can prioritize explaining highly relevant problems. The explanation unit can also postpone explaining less relevant problems. The explanation unit can also adjust the order of explanations according to the relevance of the problems. For example, by prioritizing explanations for highly relevant problems, the explanation unit can improve the user's learning efficiency. Also, by postponing explanations for less relevant problems, the explanation unit can provide explanations efficiently. Furthermore, by adjusting the order of explanations according to the relevance of the problems, the explanation unit can maximize the user's learning effect. This allows for efficient explanations by adjusting the order of explanations based on the relevance of the problems. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input problem relevance data into a generating AI, and the generating AI can analyze the data to adjust the order of explanations.

[0055] The analysis unit can adjust the level of detail of its analysis based on the importance of the problem. For example, the analysis unit can perform a detailed analysis for important problems. For less important problems, the analysis unit can perform a concise analysis. The analysis unit can also adjust the level of detail of its analysis according to the importance of the problem. For example, by performing a detailed analysis for important problems, the analysis unit can deepen the user's understanding. Also, by performing a concise analysis for less important problems, the analysis unit can reduce the user's burden. Furthermore, by adjusting the level of detail of the analysis according to the importance of the problem, the analysis unit can improve the user's learning efficiency. In this way, analysis can be performed efficiently by adjusting the level of detail of the analysis based on the importance of the problem. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input problem importance data into a generating AI, and the generating AI can analyze the data and adjust the level of detail of the analysis.

[0056] The analysis unit can apply different analysis algorithms depending on the category of the problem during analysis. For example, the analysis unit can apply a mathematical formula analysis algorithm to a mathematics problem. The analysis unit can also apply a grammatical analysis algorithm to an English problem. The analysis unit can also apply a scientific data analysis algorithm to a science problem. For example, the analysis unit can analyze the solution to a mathematics problem in detail by applying a mathematical formula analysis algorithm. Furthermore, the analysis unit can identify grammatical errors in an English problem by applying a grammatical analysis algorithm. In addition, the analysis unit can analyze experimental data by applying a scientific data analysis algorithm to a science problem. This allows for the provision of more appropriate analysis results by applying different analysis algorithms depending on the category of the problem. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input problem category data into a generating AI, which can then analyze the data and apply different analysis algorithms.

[0057] The analysis unit can determine the priority of analysis based on the submission date of the problems. For example, the analysis unit can prioritize analysis of problems with approaching deadlines. The analysis unit can also postpone analysis of problems with distant submission dates. The analysis unit can also adjust the priority of analysis according to the submission date of the problems. For example, by prioritizing analysis of problems with approaching deadlines, the analysis unit can ensure that users meet their submission deadlines. Conversely, by postponing analysis of problems with distant submission dates, the analysis unit can perform analysis efficiently. Furthermore, by adjusting the priority of analysis according to the submission date of the problems, the analysis unit can improve the learning efficiency of users. Thus, by determining the priority of analysis based on the submission date of the problems, analysis can be performed efficiently. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input problem submission date data into a generating AI, and the generating AI can analyze the data to determine the priority of analysis.

[0058] The analysis unit can adjust the order of analysis based on the relevance of the problems during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant problems. The analysis unit can also postpone the analysis of less relevant problems. The analysis unit can also adjust the order of analysis according to the relevance of the problems. For example, by prioritizing the analysis of highly relevant problems, the analysis unit can improve the user's learning efficiency. Furthermore, by postponing the analysis of less relevant problems, the analysis unit can perform analysis efficiently. In addition, by adjusting the order of analysis according to the relevance of the problems, the analysis unit can maximize the user's learning effect. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the problems. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input problem relevance data into a generating AI, and the generating AI can analyze the data and adjust the order of analysis.

[0059] The countermeasures unit can adjust the level of detail in a countermeasure plan based on the importance of the problem when creating the plan. For example, the unit can create a detailed countermeasure plan for important problems. For less important problems, the unit can create a concise countermeasure plan. The countermeasures unit can also adjust the level of detail in a countermeasure plan according to the importance of the problem. For example, by creating a detailed countermeasure plan for important problems, the unit can deepen the user's understanding. Also, by creating a concise countermeasure plan for less important problems, the unit can reduce the burden on the user. Furthermore, by adjusting the level of detail in the countermeasures unit according to the importance of the problem, the unit can improve the user's learning efficiency. In this way, by adjusting the level of detail in the countermeasures unit based on the importance of the problem, countermeasures plans can be created efficiently. Some or all of the above processing in the countermeasures unit may be performed using AI, for example, or without using AI. For example, the countermeasures unit can input problem importance data into a generating AI, and the generating AI can analyze the data and adjust the level of detail in the countermeasures plan.

[0060] The countermeasures unit can apply different countermeasure algorithms depending on the problem category when creating a countermeasure plan. For example, the unit can apply a mathematical formula analysis algorithm to a mathematics problem. The unit can also apply a grammatical analysis algorithm to an English problem. The unit can also apply a scientific data analysis algorithm to a science problem. For example, the unit can analyze the solution to a mathematics problem in detail by applying a mathematical formula analysis algorithm. The unit can also identify grammatical errors in an English problem by applying a grammatical analysis algorithm. Furthermore, the unit can analyze experimental data by applying a scientific data analysis algorithm to a science problem. By applying different countermeasure algorithms depending on the problem category, a more appropriate countermeasure plan can be provided. Some or all of the above processing in the countermeasures unit may be performed using AI, for example, or without AI. For example, the countermeasures unit can input problem category data into a generating AI, and the generating AI can analyze the data and apply different countermeasure algorithms.

[0061] The task force can determine the priority of task plans based on the submission timing of the problems when creating task plans. For example, the task force can create task plans preferentially for problems with approaching deadlines. For problems with distant submission deadlines, the task force can also postpone creating task plans. The task force can also adjust the priority of task plans according to the submission timing of the problems. For example, by prioritizing task plans for problems with approaching deadlines, the task force can ensure that users meet their submission deadlines. Also, by postponing task plans for problems with distant submission deadlines, the task force can create task plans efficiently. Furthermore, by adjusting the priority of task plans according to the submission timing of the problems, the task force can improve the learning efficiency of users. In this way, task plans can be created efficiently by determining the priority of task plans based on the submission timing of the problems. Some or all of the above processes in the task force may be performed using AI, for example, or not using AI. For example, the task force can input problem submission timing data into a generating AI, and the generating AI can analyze the data to determine the priority of task plans.

[0062] The countermeasure unit can adjust the order of countermeasure plans based on the relevance of the problems when creating them. For example, the countermeasure unit can prioritize creating countermeasure plans for highly relevant problems. The countermeasure unit can also postpone creating countermeasure plans for less relevant problems. The countermeasure unit can also adjust the order of countermeasure plans according to the relevance of the problems. For example, by prioritizing the creation of countermeasure plans for highly relevant problems, the countermeasure unit can improve the user's learning efficiency. Also, by postponing the creation of countermeasure plans for less relevant problems, the countermeasure unit can create countermeasure plans efficiently. Furthermore, by adjusting the order of countermeasure plans according to the relevance of the problems, the countermeasure unit can maximize the user's learning effect. In this way, by adjusting the order of countermeasure plans based on the relevance of the problems, countermeasure plans can be created efficiently. Some or all of the above processing in the countermeasure unit may be performed using AI, for example, or without using AI. For example, the countermeasure unit can input problem relevance data into a generating AI, and the generating AI can analyze the data to adjust the order of countermeasure plans.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The ultimate AI teacher system can also include a learning style analysis unit that analyzes the user's learning style. This unit can determine whether the user has a visual, auditory, or tactile learning style and, based on the results, suggest the optimal learning method. For example, it can provide visual learners with materials that heavily utilize diagrams and videos, auditory learners with materials that emphasize audio explanations, and tactile learners with materials that encourage interactive problem-solving. This maximizes learning effectiveness by providing the optimal learning method tailored to the user's learning style.

[0065] The ultimate AI teacher system can also include an environment optimization unit to further optimize the user's learning environment. This unit can monitor the user's learning environment (e.g., lighting, volume, temperature) and provide advice to ensure an optimal learning environment. For example, if the lighting is dim, it can advise "turn up the lights," and if the volume is too loud, it can suggest "lower the volume." It can also provide advice such as "adjust the room temperature" if the temperature is inappropriate. This allows users to learn in an optimal environment, improving their learning effectiveness.

[0066] The ultimate AI teacher system can also include a progress visualization unit to visualize the user's learning progress. This unit can visually display the user's learning data in graphs and charts, allowing users to grasp their learning progress at a glance. For example, it can display the user's accuracy rate and answer speed trends in line graphs, clearly showing progress in each area. It can also display the degree of achievement towards learning goals in pie charts, visually showing progress toward goal achievement. Furthermore, by comparing current data with past learning data, users can feel their own growth. This makes it easier for users to understand their learning progress and increases their motivation to learn.

[0067] The ultimate AI teacher system can also include a performance enhancement unit to further improve the user's learning performance. Based on the user's learning data, the performance enhancement unit can provide advice to maximize learning efficiency. For example, if a user tends to concentrate better during certain times, it can suggest studying during those times. It can also advise taking appropriate breaks if the user is tired. Furthermore, if there are areas for improvement in the user's learning methods, it can suggest specific improvement measures. This allows users to improve their learning performance and progress more efficiently.

[0068] The ultimate AI teacher system can also include a predictive planning unit that forecasts future learning plans based on the user's learning history. This unit can analyze the user's past learning data and automatically generate future learning plans. For example, it can suggest a plan that focuses on areas the user previously struggled with, allowing for more efficient learning. It can also suggest an appropriate learning schedule based on the user's learning pace. Furthermore, it can clearly outline the steps to achieving the user's learning goals. This allows the user to clarify their future learning plan and proceed with learning efficiently.

[0069] The ultimate AI teacher system can also include a comparative analysis unit that compares a user's learning data with that of other users. This unit compares the user's learning data with that of other users, allowing them to understand their relative learning progress. For example, it can clearly identify areas where a user excels and areas where improvement is needed compared to users of the same age or grade level. It can also show how much progress a user has made compared to their past self. Furthermore, by referencing the success stories of other users, it can suggest effective learning methods. This allows users to objectively understand their own learning progress and find effective learning methods.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The data collection unit collects the user's problem-solving process and results. For example, it can collect the process and results of a user solving a problem on a tablet or smartphone. The data collection unit can collect data such as the speed at which the user solves the problem, the content of the calculation formula, the score, and the accuracy rate. For example, the data collection unit can record the speed at which a user solves a problem on a tablet in seconds. The data collection unit can also collect the content of the calculation formula for a problem solved by a user on a smartphone as text data. Furthermore, the data collection unit can collect the score and accuracy rate of the problems solved by the user as numerical data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using statistical analysis or machine learning algorithms, for example. Based on data such as the user's solving speed, the content of the calculation formulas, scores, and accuracy rates, the analysis unit can comprehensively analyze the user's weaknesses and challenges. For example, the analysis unit can analyze the distribution of the user's solving speed and calculate the mean and standard deviation. The analysis unit can also analyze the content of the user's calculation formulas and identify patterns of errors. Furthermore, the analysis unit can analyze the user's scores and accuracy rates to identify trends in problems that are weaknesses. Step 3: The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. For example, the proposal unit can propose a learning plan tailored to the user's weaknesses and challenges. The proposal unit can propose an optimal learning plan based on the user's learning goals and schedule. For example, the proposal unit can propose a plan that focuses on learning the user's weak points. The proposal unit can also propose an efficient learning plan that matches the user's learning schedule. Furthermore, the proposal unit can also propose appropriate learning materials and resources according to the user's learning goals. Step 4: The explanation unit provides real-time explanations to user questions. For example, the explanation unit can analyze a user's voice question and provide a detailed explanation of the problem's solution and related knowledge using diagrams, videos, and audio. The explanation unit can refine its explanation method and explain it repeatedly until the user understands. For example, if a user asks a voice question like, "I don't know how to solve this problem," the explanation unit can analyze the question and provide a detailed explanation of the problem's solution using diagrams, videos, and audio. The explanation unit can also try different explanation methods until the user understands. Furthermore, the explanation unit can adjust the content and method of the explanation according to the user's level of understanding.

[0072] (Example of form 2) The ultimate AI teacher system according to an embodiment of the present invention is a system for improving academic ability using generative AI. In this system, the user (student) inputs the process and results of solving problems on a tablet or smartphone into the generative AI. The generative AI analyzes data such as the speed of solving each problem, the content of the calculation formula, the score, and the correct answer rate, and comprehensively analyzes the user's weaknesses and challenges. Next, based on the analysis results, the generative AI proposes an optimal learning plan for the user. Furthermore, it provides detailed explanations of the solution method and related knowledge for each problem using diagrams, videos, and audio, refining the explanation method and explaining it repeatedly until the user understands. It also provides real-time explanations to questions from the user via voice. For example, if the ultimate AI teacher system receives a voice question such as "I don't know how to solve this problem," the generative AI analyzes the question and provides a detailed explanation of the solution using diagrams, videos, and audio. In addition, based on the user's problem-solving process and results, the generative AI comprehensively analyzes weaknesses and challenges and outputs a plan for future countermeasures. For example, if a user gets stuck on a particular calculation problem, the generative AI repeatedly explains the solution method for that problem and supports the user until they understand. In this way, the ultimate AI teacher system using generative AI can provide completely personalized support 24 / 7, 365 days a year, helping to improve children's academic performance. Furthermore, in cram schools, it can contribute to increased sales and alleviate teacher shortages by analyzing students' weaknesses and proposing optimal learning plans. This allows the ultimate AI teacher system to collect and analyze the user's problem-solving process and results, propose learning plans, and provide real-time explanations.

[0073] The ultimate AI teacher system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an explanation unit. The data collection unit collects the user's problem-solving process and results. For example, the data collection unit can collect the process and results of a user solving problems on a tablet or smartphone. The data collection unit can collect data such as the speed at which the user solves problems, the content of the calculation formulas, scores, and correct answer rates. For example, the data collection unit can record the speed at which a user solves problems on a tablet in seconds. The data collection unit can also collect the content of the calculation formulas for problems solved by a user on a smartphone as text data. Furthermore, the data collection unit can collect scores and correct answer rates for problems solved by a user as numerical data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the collected data using statistical analysis or machine learning algorithms. Based on data such as the user's solving speed, the content of the calculation formulas, scores, and correct answer rates, the analysis unit can comprehensively analyze the user's weaknesses and challenges. The analysis unit can, for example, analyze the distribution of users' problem-solving speeds and calculate the mean and standard deviation. It can also analyze the content of users' calculation formulas and identify error patterns. Furthermore, it can analyze users' scores and accuracy rates to identify weaknesses in certain types of problems. The suggestion unit proposes a learning plan based on the analysis results obtained by the analysis unit. For example, the suggestion unit can propose a learning plan tailored to the user's weaknesses and challenges. It can propose an optimal learning plan based on the user's learning goals and schedule. For example, it can propose a plan that focuses on problems the user struggles with. It can also propose an efficient learning plan that aligns with the user's learning schedule. Furthermore, the suggestion unit can suggest appropriate learning materials and resources according to the user's learning goals. The explanation unit provides real-time explanations to user questions. For example, it can analyze the content of user voice questions and provide detailed explanations of problem-solving methods and related knowledge using diagrams, videos, and audio. The explanation section can refine its explanation methods and repeat them until the user understands.The explanation unit can, for example, analyze a user's voice question such as, "I don't know how to solve this problem," and provide a detailed explanation of the solution using diagrams, videos, and audio. The explanation unit can also try different explanation methods until the user understands. Furthermore, it can adjust the content and method of explanation according to the user's level of understanding. As a result, the ultimate AI teacher system according to this embodiment can collect and analyze the user's problem-solving process and results, propose a learning plan, and provide explanations in real time.

[0074] The data collection unit collects the user's problem-solving process and results. For example, the unit can collect the process and results of a user solving a problem on a tablet or smartphone. Specifically, it records the speed at which a user solves a problem on a tablet in seconds and collects the content of the calculation formula as text data. It can also collect the score and correct answer rate of a problem solved by a user on a smartphone as numerical data. The data collection unit collects this data in real time and transmits it to a central database. Furthermore, the data collection unit can utilize the screen capture function of tablets and smartphones to record the user's solution process in detail. This allows for a visual confirmation of how the user solved the problem. The data collection unit centrally manages the user's solution data and can link with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively and improve the overall system performance. Furthermore, the data collection unit can anonymize or encrypt data to protect user privacy. This allows for the secure management of users' personal information and prevents unauthorized access to or leakage of data.

[0075] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the collected data using statistical analysis and machine learning algorithms. Specifically, it comprehensively analyzes the user's weaknesses and challenges based on data such as the user's solving speed, the content of the calculation formulas, scores, and accuracy rates. The analysis unit can analyze the distribution of the user's solving speed and calculate the mean and standard deviation. It can also analyze the content of the user's calculation formulas and identify error patterns. Furthermore, it can analyze the user's scores and accuracy rates to identify the types of problems that are the user's weaknesses. Based on this data, the analysis unit can evaluate the user's learning performance and provide feedback tailored to individual learning needs. The analysis unit processes data in real time using AI to understand the user's learning status. For example, it can use machine learning algorithms to analyze the user's answer patterns and evaluate their understanding of specific problems and the accuracy of their answers. It can also use natural language processing technology to analyze the user's calculation formulas and answer content to identify the causes of errors and areas for improvement. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term learning trends and performance fluctuations. This allows the analysis unit to comprehensively evaluate the user's learning progress and provide feedback tailored to their individual learning needs.

[0076] The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. For example, the proposal unit can propose a learning plan tailored to the user's weaknesses and challenges. Specifically, it proposes an optimal learning plan based on the user's learning goals and schedule. The proposal unit can propose a plan that focuses on learning the user's weak points. It can also propose an efficient learning plan that matches the user's learning schedule. Furthermore, the proposal unit can suggest appropriate learning materials and resources according to the user's learning goals. The proposal unit uses AI to analyze the user's learning data and generate an optimal learning plan tailored to individual learning needs. For example, it uses machine learning algorithms to evaluate the user's learning performance and propose the optimal learning content and order. It can also use natural language processing technology to analyze the user's learning goals and feedback and propose learning materials and resources tailored to individual learning needs. Furthermore, the proposal unit can monitor the user's learning progress in real time and continuously adjust the learning plan. This allows the proposal unit to maximize the user's learning effectiveness and support efficient learning.

[0077] The explanation unit provides real-time explanations to user questions. For example, it can analyze a user's voice question and provide detailed explanations of problem solutions and related knowledge using diagrams, videos, and audio. Specifically, if a user asks a voice question like, "I don't know how to solve this problem," the unit will analyze the question and provide a detailed explanation of the solution using diagrams, videos, and audio. The explanation unit can also try different explanation methods until the user understands. Furthermore, the explanation unit can adjust the content and method of the explanation according to the user's level of understanding. The explanation unit uses AI to analyze user questions and provide the optimal explanation method. For example, it uses natural language processing technology to analyze the user's question and identify related knowledge and solutions. It can also use image recognition technology to analyze diagrams and graphs of problems and provide visual explanations. Furthermore, the explanation unit can monitor the user's level of understanding in real time and continuously adjust the explanation content. This allows the explanation unit to maximize the user's learning effectiveness and support efficient learning. The explanation unit can collect user feedback and continuously improve the accuracy and effectiveness of the explanation content. For example, based on user feedback, the explanation methods and content are reviewed to provide more effective explanations. Furthermore, the explanation team can reliably transmit information using multiple communication methods. For instance, important information is delivered reliably by using voice calls, chat, and email in combination. This allows the explanation team to provide explanations quickly and reliably to users, maximizing learning effectiveness.

[0078] The system includes an explanatory section in which the generating AI provides detailed explanations using diagrams, videos, and audio. The explanatory section allows the generating AI to provide detailed explanations using diagrams, videos, and audio. For example, the explanatory section can allow the generating AI to provide detailed explanations of problem solutions using diagrams. The explanatory section can also allow the generating AI to provide detailed explanations of problem solutions using videos. The explanatory section can also allow the generating AI to provide detailed explanations of problem solutions using audio. For example, when the generating AI provides detailed explanations of problem solutions using diagrams, the explanatory section can display the diagrams step by step while explaining. Also, when the generating AI provides detailed explanations of problem solutions using videos, the explanatory section can play the videos while explaining. Furthermore, when the generating AI provides detailed explanations of problem solutions using audio, the explanatory section can provide audio explanations while explaining. This allows the user to deepen their understanding by having the generating AI provide detailed explanations using diagrams, videos, and audio. The generating AI is, for example, a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the above-described processing in the explanatory section may be performed using AI, for example, or without AI. For example, the explanation section can input the solution to a problem into a generating AI, which can then generate an explanation using diagrams, videos, and audio.

[0079] The system includes an analysis unit that comprehensively analyzes users' weaknesses and challenges. The analysis unit can comprehensively analyze users' weaknesses and challenges. For example, it can comprehensively analyze users' weaknesses and challenges based on data such as the user's solving speed, the content of calculation formulas, scores, and accuracy rates. The analysis unit can analyze the distribution of users' solving speeds and calculate the mean and standard deviation. It can also analyze the content of users' calculation formulas and identify error patterns. Furthermore, the analysis unit can analyze users' scores and accuracy rates to identify trends in problems where users struggle. For example, by analyzing the distribution of users' solving speeds and calculating the mean and standard deviation, the analysis unit can grasp trends in users' solving speeds. It can also analyze the content of users' calculation formulas and identify error patterns to identify the causes of users' calculation errors. Furthermore, by analyzing users' scores and accuracy rates and identifying trends in problems where users struggle, the analysis unit can clarify users' learning challenges. This allows for a more effective learning plan to be proposed by comprehensively analyzing the user's weaknesses and challenges. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data and output the analysis results.

[0080] The system includes a countermeasure unit that outputs future countermeasure plans. The countermeasure unit can output future countermeasure plans. For example, the countermeasure unit can output future countermeasure plans based on the user's weaknesses and challenges. The countermeasure unit can output optimal countermeasure plans based on the user's learning goals and schedule. For example, the countermeasure unit can output a countermeasure plan that focuses on learning the user's weak points. The countermeasure unit can also output an efficient countermeasure plan that matches the user's learning schedule. Furthermore, the countermeasure unit can output a countermeasure plan that suggests appropriate learning materials and resources according to the user's learning goals. For example, by outputting a countermeasure plan that focuses on learning the user's weak points, the user can learn efficiently. Furthermore, by outputting an efficient countermeasure plan that matches the user's learning schedule, the user can learn without difficulty. Furthermore, by outputting a countermeasure plan that suggests appropriate learning materials and resources according to the user's learning goals, the user can learn effectively. In this way, by outputting future countermeasure plans, the user can learn effectively. Some or all of the above processing in the countermeasure unit may be performed using AI, for example, or without using AI. For example, the countermeasures unit can input the user's weaknesses and challenges into a generating AI, which can then output the optimal countermeasure plan.

[0081] The data collection unit can collect the process and results of a user solving problems on a tablet or smartphone. For example, the data collection unit can collect the process and results when a user solves problems on a tablet. The data collection unit can also collect the process and results when a user solves problems on a smartphone. For example, the data collection unit can record the process of a user solving problems on a tablet in seconds. The data collection unit can also collect the content of the calculation formulas used by the user when solving problems on a smartphone as text data. Furthermore, the data collection unit can collect the score and correct answer rate of the problems solved by the user as numerical data. This makes data collection more efficient by collecting the process and results of a user solving problems on a tablet or smartphone. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from problems solved by the user on a tablet or smartphone into a generating AI, which can then analyze the data and output the collection results.

[0082] The analysis unit can analyze data such as the speed at which problems are solved, the content of the calculation formulas, the score, and the accuracy rate for each problem. For example, the analysis unit can analyze the speed at which problems are solved in seconds. The analysis unit can also analyze the content of the calculation formulas for each problem. The analysis unit can also analyze the score and accuracy rate for each problem. For example, the analysis unit can analyze the speed at which problems are solved in seconds and calculate the mean and standard deviation. The analysis unit can also analyze the content of the calculation formulas for each problem and identify patterns of errors. Furthermore, the analysis unit can analyze the score and accuracy rate for each problem and identify tendencies in problems that the user is weak at. In this way, by analyzing data such as the speed at which problems are solved, the content of the calculation formulas, the score, and the accuracy rate for each problem, the user's learning progress can be understood in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the collected data into a generating AI, and the generating AI can analyze the data and output the analysis results.

[0083] The data collection unit can estimate the user's emotions and adjust the timing of data collection in the problem-solving process based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and wait until the user is relaxed. If the user is focused, the data collection unit can advance the collection timing to collect data efficiently. If the user is tired, the data collection unit can adjust the collection timing to collect data after a break. For example, if the user is stressed, the data collection unit can reduce the user's burden by delaying the collection timing and waiting until the user is relaxed. Also, if the user is focused, the data collection unit can leverage the user's concentration by advancing the collection timing to collect data efficiently. Furthermore, if the user is tired, the data collection unit can reduce user fatigue by adjusting the collection timing to collect data after a break. In this way, by adjusting the collection timing based on the user's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the timing of data collection.

[0084] The data collection unit can analyze the user's past learning history and select the optimal data collection method. For example, the data collection unit can prioritize collecting learning methods that the user has frequently used in the past. The data collection unit can also suggest effective data collection methods based on the user's past learning history. The data collection unit can also analyze the user's learning history and determine the optimal data collection timing. For example, the data collection unit can select an effective data collection method for the user by prioritizing the collection of learning methods that the user has frequently used in the past. Furthermore, the data collection unit can improve the user's learning efficiency by suggesting effective data collection methods based on the user's past learning history. In addition, the data collection unit can maximize the user's learning effectiveness by analyzing the user's learning history and determining the optimal data collection timing. Thus, by analyzing the user's past learning history, the optimal data collection method can be selected. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's past learning history data into a generating AI, which can then analyze the data and select the optimal data collection method.

[0085] The data collection unit can filter problem-solving processes based on the user's current learning status and areas of interest. For example, the data collection unit can prioritize collecting data related to the subject the user is currently studying. The data collection unit can also collect relevant problem-solving processes based on the user's areas of interest. The data collection unit can also filter and collect appropriate data considering the user's learning status. For example, the data collection unit can improve the user's learning efficiency by prioritizing the collection of data related to the subject the user is currently studying. Furthermore, the data collection unit can pique the user's interest by collecting relevant problem-solving processes based on the user's areas of interest. In addition, the data collection unit can maximize the user's learning effectiveness by filtering and collecting appropriate data considering the user's learning status. This allows for the collection of highly relevant data by filtering based on the user's current learning status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current learning status and areas of interest into a generating AI, which can then analyze and filter the data.

[0086] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can start collecting data from easy problems. If the user is relaxed, the data collection unit can start collecting data from difficult problems. If the user is focused, the data collection unit can prioritize collecting important data. For example, if the user is stressed, the data collection unit can reduce the user's burden by starting with easy problems. Also, if the user is relaxed, the data collection unit can maximize the user's learning effect by starting with difficult problems. Furthermore, if the user is focused, the data collection unit can leverage the user's concentration by prioritizing the collection of important data. In this way, data can be collected efficiently by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then estimate the emotion and determine the priority of the data to collect.

[0087] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting problem-solving processes. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. The data collection unit can also collect region-specific problem-solving processes based on the user's location information. The data collection unit can also collect optimal data by considering the user's geographical location information. For example, if the user is in a specific region, the data collection unit can collect region-specific problem-solving processes by prioritizing the collection of data related to that region. Furthermore, by collecting region-specific problem-solving processes based on the user's location information, the data collection unit can maximize the user's learning effect. In addition, by collecting optimal data while considering the user's geographical location information, the data collection unit can improve the user's learning efficiency. This allows for the collection of region-specific problem-solving processes by collecting highly relevant data while considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI, which can analyze the data and prioritize the collection of highly relevant data.

[0088] The data collection unit can analyze the user's social media activity and collect relevant data when collecting problem-solving processes. For example, the data collection unit can collect data related to topics of interest from the user's social media activity. The data collection unit can also collect relevant problem-solving processes based on information shared by the user on social media. The data collection unit can also analyze the user's social media activity and collect optimal data. For example, the data collection unit can attract the user's interest by collecting data related to topics of interest from the user's social media activity. Furthermore, the data collection unit can maximize the user's learning effect by collecting relevant problem-solving processes based on information shared by the user on social media. In addition, the data collection unit can improve the user's learning efficiency by analyzing the user's social media activity and collecting optimal data. This allows for the collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then analyze the data and collect relevant data.

[0089] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can provide concise analysis results. If the user is focused, the analysis unit can provide visually easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can deepen the user's understanding by providing detailed analysis results. Also, if the user is stressed, the analysis unit can reduce the user's burden by providing concise analysis results. Furthermore, if the user is focused, the analysis unit can leverage the user's concentration by providing visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the method of expression for the analysis.

[0090] The analysis unit can adjust the level of detail of the analysis based on the importance of the problem during the analysis. For example, the analysis unit can perform a detailed analysis for important problems. For less important problems, the analysis unit can perform a concise analysis. The analysis unit can also adjust the level of detail of the analysis according to the importance of the problem. For example, by performing a detailed analysis for important problems, the analysis unit can deepen the user's understanding. Also, by performing a concise analysis for less important problems, the analysis unit can reduce the burden on the user. Furthermore, by adjusting the level of detail of the analysis according to the importance of the problem, the analysis unit can improve the user's learning efficiency. In this way, analysis can be performed efficiently by adjusting the level of detail of the analysis based on the importance of the problem. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input problem importance data into a generating AI, and the generating AI can analyze the data and adjust the level of detail of the analysis.

[0091] The analysis unit can apply different analysis algorithms depending on the category of the problem during analysis. For example, the analysis unit can apply a mathematical formula analysis algorithm to a mathematics problem. The analysis unit can also apply a grammatical analysis algorithm to an English problem. The analysis unit can also apply a scientific data analysis algorithm to a science problem. For example, the analysis unit can analyze the solution to a mathematics problem in detail by applying a mathematical formula analysis algorithm. Furthermore, the analysis unit can identify grammatical errors in an English problem by applying a grammatical analysis algorithm. In addition, the analysis unit can analyze experimental data by applying a scientific data analysis algorithm to a science problem. This allows for the provision of more appropriate analysis results by applying different analysis algorithms depending on the category of the problem. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input problem category data into an AI that generates data, and the generating AI can analyze the data and apply different analysis algorithms.

[0092] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. If the user is focused, the analysis unit can provide a visually easy-to-understand analysis. For example, if the user is in a hurry, the analysis unit can save the user's time by providing a short, concise analysis. Also, if the user is relaxed, the analysis unit can deepen the user's understanding by providing a detailed analysis. Furthermore, if the user is focused, the analysis unit can leverage the user's concentration by providing a visually easy-to-understand analysis. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the length of the analysis.

[0093] The analysis unit can determine the priority of analysis based on the submission date of the problems during the analysis process. For example, the analysis unit can prioritize the analysis of problems with approaching deadlines. The analysis unit can also postpone the analysis of problems with distant submission dates. The analysis unit can also adjust the priority of analysis according to the submission date of the problems. For example, by prioritizing the analysis of problems with approaching deadlines, the analysis unit can ensure that users meet their submission deadlines. Conversely, by postponing the analysis of problems with distant submission dates, the analysis unit can perform analysis efficiently. Furthermore, by adjusting the priority of analysis according to the submission date of the problems, the analysis unit can improve the learning efficiency of the users. This allows for efficient analysis by determining the priority of analysis based on the submission date of the problems. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input problem submission date data into a generating AI, which can analyze the data and determine the priority of analysis.

[0094] The analysis unit can adjust the order of analysis based on the relevance of the problems during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant problems. The analysis unit can also postpone the analysis of less relevant problems. The analysis unit can also adjust the order of analysis according to the relevance of the problems. For example, by prioritizing the analysis of highly relevant problems, the analysis unit can improve the user's learning efficiency. Furthermore, by postponing the analysis of less relevant problems, the analysis unit can perform analysis efficiently. In addition, by adjusting the order of analysis according to the relevance of the problems, the analysis unit can maximize the user's learning effect. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the problems. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input problem relevance data into a generating AI, and the generating AI can analyze the data and adjust the order of analysis.

[0095] The suggestion section can estimate the user's emotions and adjust the way suggestions are presented based on the estimated emotions. For example, if the user is relaxed, the suggestion section can provide detailed suggestions. If the user is stressed, the suggestion section can provide concise suggestions. If the user is focused, the suggestion section can provide visually easy-to-understand suggestions. For example, if the user is relaxed, the suggestion section can deepen the user's understanding by providing detailed suggestions. Also, if the user is stressed, the suggestion section can reduce the user's burden by providing concise suggestions. Furthermore, if the user is focused, the suggestion section can leverage the user's concentration by providing visually easy-to-understand suggestions. In this way, by adjusting the way suggestions are presented based on the user's emotions, suggestions that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion section may be performed using AI, for example, or without AI. For example, the proposal unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the way the proposal is expressed.

[0096] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the learning plan. For example, the suggestion unit can provide detailed suggestions for important learning plans. For less important learning plans, the suggestion unit can provide concise suggestions. The suggestion unit can also adjust the level of detail of its suggestions according to the importance of the learning plan. For example, by providing detailed suggestions for important learning plans, the suggestion unit can deepen the user's understanding. Also, by providing concise suggestions for less important learning plans, the suggestion unit can reduce the user's burden. Furthermore, by adjusting the level of detail of its suggestions according to the importance of the learning plan, the suggestion unit can improve the user's learning efficiency. This allows for efficient suggestion generation by adjusting the level of detail of suggestions based on the importance of the learning plan. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input learning plan importance data into a generating AI, which can analyze the data and adjust the level of detail of the suggestions.

[0097] The proposal unit can apply different proposal algorithms depending on the category of the learning plan when making a proposal. For example, the proposal unit can apply a mathematical formula analysis algorithm to a mathematics learning plan. The proposal unit can also apply a grammatical analysis algorithm to an English learning plan. The proposal unit can also apply a scientific data analysis algorithm to a science learning plan. For example, the proposal unit can analyze the details of a mathematics learning plan by applying a mathematical formula analysis algorithm. Furthermore, the proposal unit can identify grammatical errors in an English learning plan by applying a grammatical analysis algorithm. In addition, the proposal unit can analyze experimental data in a science learning plan by applying a scientific data analysis algorithm. This allows for the provision of more appropriate proposals by applying different proposal algorithms depending on the category of the learning plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input learning plan category data into a generating AI, which can analyze the data and apply different proposal algorithms.

[0098] The suggestion section can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion section can provide short, concise suggestions. If the user is relaxed, the suggestion section can provide detailed suggestions. If the user is focused, the suggestion section can provide visually easy-to-understand suggestions. For example, if the user is in a hurry, the suggestion section can save the user's time by providing short, concise suggestions. If the user is relaxed, the suggestion section can deepen the user's understanding by providing detailed suggestions. Furthermore, if the user is focused, the suggestion section can leverage the user's concentration by providing visually easy-to-understand suggestions. By adjusting the length of suggestions based on the user's emotions, the suggestion section can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion section may be performed using AI, for example, or without AI. For example, the suggestion unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the length of the suggestion.

[0099] The proposal unit can determine the priority of proposals based on the submission timing of the learning plan. For example, the proposal unit can prioritize proposals for learning plans with approaching deadlines. It can also postpone proposals for learning plans with distant submission deadlines. The proposal unit can also adjust the priority of proposals according to the submission timing of the learning plan. For example, by prioritizing proposals for learning plans with approaching deadlines, the proposal unit can ensure that users meet their submission deadlines. Conversely, by postponing proposals for learning plans with distant submission deadlines, the proposal unit can efficiently make proposals. Furthermore, by adjusting the priority of proposals according to the submission timing of the learning plan, the proposal unit can improve the user's learning efficiency. Thus, by determining the priority of proposals based on the submission timing of the learning plan, proposals can be made efficiently. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input learning plan submission timing data into a generating AI, and the generating AI can analyze the data to determine the priority of proposals.

[0100] The suggestion unit can adjust the order of suggestions based on the relevance of the learning plans. For example, the suggestion unit can prioritize suggesting highly relevant learning plans. The suggestion unit can also postpone suggesting less relevant learning plans. The suggestion unit can also adjust the order of suggestions according to the relevance of the learning plans. For example, by prioritizing highly relevant learning plans, the suggestion unit can improve the user's learning efficiency. Furthermore, by postponing suggesting less relevant learning plans, the suggestion unit can make suggestions efficiently. In addition, by adjusting the order of suggestions according to the relevance of the learning plans, the suggestion unit can maximize the user's learning effect. Thus, by adjusting the order of suggestions based on the relevance of the learning plans, suggestions can be made efficiently. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input learning plan relevance data into a generating AI, and the generating AI can analyze the data and adjust the order of suggestions.

[0101] The explanation section can estimate the user's emotions and adjust the way the explanation is presented based on the estimated emotions. For example, if the user is relaxed, the explanation section can provide a detailed explanation. If the user is stressed, the explanation section can provide a concise explanation. If the user is focused, the explanation section can provide a visually easy-to-understand explanation. For example, if the user is relaxed, the explanation section can deepen the user's understanding by providing a detailed explanation. Also, if the user is stressed, the explanation section can reduce the user's burden by providing a concise explanation. Furthermore, if the user is focused, the explanation section can leverage the user's concentration by providing a visually easy-to-understand explanation. In this way, by adjusting the way the explanation is presented based on the user's emotions, it is possible to provide an explanation that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the explanation section may be performed using AI, for example, or without using AI. For example, the commentary section can input user emotion data into a generating AI, which can then estimate the emotion and adjust the way the commentary is expressed.

[0102] The explanation unit can adjust the level of detail in its explanations based on the importance of the problem. For example, the explanation unit can provide detailed explanations for important problems. For less important problems, it can provide concise explanations. The explanation unit can also adjust the level of detail in its explanations according to the importance of the problem. For example, by providing detailed explanations for important problems, the explanation unit can deepen the user's understanding. Also, by providing concise explanations for less important problems, the explanation unit can reduce the user's burden. Furthermore, by adjusting the level of detail in its explanations according to the importance of the problem, the explanation unit can improve the user's learning efficiency. This allows for efficient explanations by adjusting the level of detail in the explanations based on the importance of the problem. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input problem importance data into a generating AI, and the generating AI can analyze the data and adjust the level of detail in the explanations.

[0103] The explanation unit can apply different explanation algorithms depending on the category of the problem. For example, for a mathematics problem, the explanation unit can provide an explanation using mathematical formulas. For an English problem, the explanation unit can provide an explanation using grammar and vocabulary. For a science problem, the explanation unit can provide an explanation using experimental data and diagrams. For example, for a mathematics problem, the explanation unit can explain the solution to the problem in detail by using mathematical formulas. For an English problem, the explanation unit can identify grammatical errors by providing an explanation using grammar and vocabulary. Furthermore, for a science problem, the explanation unit can analyze experimental results by providing an explanation using experimental data and diagrams. In this way, by applying different explanation algorithms depending on the category of the problem, a more appropriate explanation can be provided. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input problem category data into a generating AI, and the generating AI can analyze the data and apply a different explanation algorithm.

[0104] The explanation section can estimate the user's emotions and adjust the length of the explanation based on the estimated emotions. For example, if the user is in a hurry, the explanation section can provide a short, concise explanation. If the user is relaxed, the explanation section can provide a detailed explanation. If the user is focused, the explanation section can provide a visually easy-to-understand explanation. For example, if the user is in a hurry, the explanation section can save the user's time by providing a short, concise explanation. Also, if the user is relaxed, the explanation section can deepen the user's understanding by providing a detailed explanation. Furthermore, if the user is focused, the explanation section can leverage the user's concentration by providing a visually easy-to-understand explanation. In this way, by adjusting the length of the explanation based on the user's emotions, the explanation can be made optimal for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the explanation section may be performed using AI, for example, or without AI. For example, the commentary section can input user emotion data into a generating AI, which can then estimate the emotion and adjust the length of the commentary accordingly.

[0105] The explanation unit can determine the priority of explanations based on the submission date of the problems. For example, the explanation unit can prioritize explanations for problems with approaching deadlines. It can also postpone explanations for problems with distant submission dates. The explanation unit can also adjust the priority of explanations according to the submission date of the problems. For example, by prioritizing explanations for problems with approaching deadlines, the explanation unit can ensure that users meet their submission deadlines. Conversely, by postponing explanations for problems with distant submission dates, the explanation unit can provide explanations efficiently. Furthermore, by adjusting the priority of explanations according to the submission date of the problems, the explanation unit can improve the learning efficiency of users. This allows for efficient explanations by determining the priority of explanations based on the submission date of the problems. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input problem submission date data into a generating AI, which can analyze the data to determine the priority of explanations.

[0106] The explanation unit can adjust the order of explanations based on the relevance of the problems. For example, the explanation unit can prioritize explaining highly relevant problems. The explanation unit can also postpone explaining less relevant problems. The explanation unit can also adjust the order of explanations according to the relevance of the problems. For example, by prioritizing explanations for highly relevant problems, the explanation unit can improve the user's learning efficiency. Also, by postponing explanations for less relevant problems, the explanation unit can provide explanations efficiently. Furthermore, by adjusting the order of explanations according to the relevance of the problems, the explanation unit can maximize the user's learning effect. This allows for efficient explanations by adjusting the order of explanations based on the relevance of the problems. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input problem relevance data into a generating AI, and the generating AI can analyze the data to adjust the order of explanations.

[0107] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can provide concise analysis results. If the user is focused, the analysis unit can provide visually easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can deepen the user's understanding by providing detailed analysis results. Also, if the user is stressed, the analysis unit can reduce the user's burden by providing concise analysis results. Furthermore, if the user is focused, the analysis unit can leverage the user's concentration by providing visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the way the analysis is expressed.

[0108] The analysis unit can adjust the level of detail of its analysis based on the importance of the problem. For example, the analysis unit can perform a detailed analysis for important problems. For less important problems, the analysis unit can perform a concise analysis. The analysis unit can also adjust the level of detail of its analysis according to the importance of the problem. For example, by performing a detailed analysis for important problems, the analysis unit can deepen the user's understanding. Also, by performing a concise analysis for less important problems, the analysis unit can reduce the user's burden. Furthermore, by adjusting the level of detail of the analysis according to the importance of the problem, the analysis unit can improve the user's learning efficiency. In this way, analysis can be performed efficiently by adjusting the level of detail of the analysis based on the importance of the problem. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input problem importance data into a generating AI, and the generating AI can analyze the data and adjust the level of detail of the analysis.

[0109] The analysis unit can apply different analysis algorithms depending on the category of the problem during analysis. For example, the analysis unit can apply a mathematical formula analysis algorithm to a mathematics problem. The analysis unit can also apply a grammatical analysis algorithm to an English problem. The analysis unit can also apply a scientific data analysis algorithm to a science problem. For example, the analysis unit can analyze the solution to a mathematics problem in detail by applying a mathematical formula analysis algorithm. Furthermore, the analysis unit can identify grammatical errors in an English problem by applying a grammatical analysis algorithm. In addition, the analysis unit can analyze experimental data by applying a scientific data analysis algorithm to a science problem. This allows for the provision of more appropriate analysis results by applying different analysis algorithms depending on the category of the problem. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input problem category data into a generating AI, which can then analyze the data and apply different analysis algorithms.

[0110] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. If the user is focused, the analysis unit can provide a visually easy-to-understand analysis. For example, if the user is in a hurry, the analysis unit can save the user's time by providing a short, concise analysis. If the user is relaxed, the analysis unit can deepen the user's understanding by providing a detailed analysis. Furthermore, if the user is focused, the analysis unit can leverage the user's concentration by providing a visually easy-to-understand analysis. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the length of the analysis.

[0111] The analysis unit can determine the priority of analysis based on the submission date of the problems. For example, the analysis unit can prioritize analysis of problems with approaching deadlines. The analysis unit can also postpone analysis of problems with distant submission dates. The analysis unit can also adjust the priority of analysis according to the submission date of the problems. For example, by prioritizing analysis of problems with approaching deadlines, the analysis unit can ensure that users meet their submission deadlines. Conversely, by postponing analysis of problems with distant submission dates, the analysis unit can perform analysis efficiently. Furthermore, by adjusting the priority of analysis according to the submission date of the problems, the analysis unit can improve the learning efficiency of users. Thus, by determining the priority of analysis based on the submission date of the problems, analysis can be performed efficiently. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input problem submission date data into a generating AI, and the generating AI can analyze the data to determine the priority of analysis.

[0112] The analysis unit can adjust the order of analysis based on the relevance of the problems during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant problems. The analysis unit can also postpone the analysis of less relevant problems. The analysis unit can also adjust the order of analysis according to the relevance of the problems. For example, by prioritizing the analysis of highly relevant problems, the analysis unit can improve the user's learning efficiency. Furthermore, by postponing the analysis of less relevant problems, the analysis unit can perform analysis efficiently. In addition, by adjusting the order of analysis according to the relevance of the problems, the analysis unit can maximize the user's learning effect. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the problems. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input problem relevance data into a generating AI, and the generating AI can analyze the data and adjust the order of analysis.

[0113] The mitigation unit can estimate the user's emotions and adjust the presentation of the mitigation plan based on the estimated emotions. For example, if the user is relaxed, the mitigation unit can provide a detailed mitigation plan. If the user is stressed, the mitigation unit can provide a concise mitigation plan. If the user is focused, the mitigation unit can provide a visually easy-to-understand mitigation plan. For example, if the user is relaxed, the mitigation unit can deepen the user's understanding by providing a detailed mitigation plan. Also, if the user is stressed, the mitigation unit can reduce the user's burden by providing a concise mitigation plan. Furthermore, if the user is focused, the mitigation unit can leverage the user's concentration by providing a visually easy-to-understand mitigation plan. In this way, by adjusting the presentation of the mitigation plan based on the user's emotions, it is possible to provide a mitigation plan that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the countermeasures unit may be performed using AI, for example, or without AI. For example, the countermeasures unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust how the countermeasures plan is expressed.

[0114] The countermeasures unit can adjust the level of detail in a countermeasure plan based on the importance of the problem when creating the plan. For example, the unit can create a detailed countermeasure plan for important problems. For less important problems, the unit can create a concise countermeasure plan. The countermeasures unit can also adjust the level of detail in a countermeasure plan according to the importance of the problem. For example, by creating a detailed countermeasure plan for important problems, the unit can deepen the user's understanding. Also, by creating a concise countermeasure plan for less important problems, the unit can reduce the burden on the user. Furthermore, by adjusting the level of detail in the countermeasures unit according to the importance of the problem, the unit can improve the user's learning efficiency. In this way, by adjusting the level of detail in the countermeasures unit based on the importance of the problem, countermeasures plans can be created efficiently. Some or all of the above processing in the countermeasures unit may be performed using AI, for example, or without using AI. For example, the countermeasures unit can input problem importance data into a generating AI, and the generating AI can analyze the data and adjust the level of detail in the countermeasures plan.

[0115] The countermeasures unit can apply different countermeasure algorithms depending on the problem category when creating a countermeasure plan. For example, the unit can apply a mathematical formula analysis algorithm to a mathematics problem. The unit can also apply a grammatical analysis algorithm to an English problem. The unit can also apply a scientific data analysis algorithm to a science problem. For example, the unit can analyze the solution to a mathematics problem in detail by applying a mathematical formula analysis algorithm. The unit can also identify grammatical errors in an English problem by applying a grammatical analysis algorithm. Furthermore, the unit can analyze experimental data by applying a scientific data analysis algorithm to a science problem. By applying different countermeasure algorithms depending on the problem category, a more appropriate countermeasure plan can be provided. Some or all of the above processing in the countermeasures unit may be performed using AI, for example, or without AI. For example, the countermeasures unit can input problem category data into a generating AI, and the generating AI can analyze the data and apply different countermeasure algorithms.

[0116] The response unit can estimate the user's emotions and adjust the length of the response plan based on the estimated emotions. For example, if the user is in a hurry, the response unit can provide a short, concise response plan. If the user is relaxed, the response unit can provide a detailed response plan. If the user is focused, the response unit can provide a visually easy-to-understand response plan. For example, if the user is in a hurry, the response unit can save the user's time by providing a short, concise response plan. Also, if the user is relaxed, the response unit can deepen the user's understanding by providing a detailed response plan. Furthermore, if the user is focused, the response unit can leverage the user's concentration by providing a visually easy-to-understand response plan. In this way, by adjusting the length of the response plan based on the user's emotions, the optimal response plan can be provided to the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the countermeasures unit may be performed using AI, for example, or without AI. For example, the countermeasures unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the length of the countermeasures plan.

[0117] The task force can determine the priority of task plans based on the submission timing of the problems when creating task plans. For example, the task force can create task plans preferentially for problems with approaching deadlines. For problems with distant submission deadlines, the task force can also postpone creating task plans. The task force can also adjust the priority of task plans according to the submission timing of the problems. For example, by prioritizing task plans for problems with approaching deadlines, the task force can ensure that users meet their submission deadlines. Also, by postponing task plans for problems with distant submission deadlines, the task force can create task plans efficiently. Furthermore, by adjusting the priority of task plans according to the submission timing of the problems, the task force can improve the learning efficiency of users. In this way, task plans can be created efficiently by determining the priority of task plans based on the submission timing of the problems. Some or all of the above processes in the task force may be performed using AI, for example, or not using AI. For example, the task force can input problem submission timing data into a generating AI, and the generating AI can analyze the data to determine the priority of task plans.

[0118] The countermeasure unit can adjust the order of countermeasure plans based on the relevance of the problems when creating them. For example, the countermeasure unit can prioritize creating countermeasure plans for highly relevant problems. The countermeasure unit can also postpone creating countermeasure plans for less relevant problems. The countermeasure unit can also adjust the order of countermeasure plans according to the relevance of the problems. For example, by prioritizing the creation of countermeasure plans for highly relevant problems, the countermeasure unit can improve the user's learning efficiency. Also, by postponing the creation of countermeasure plans for less relevant problems, the countermeasure unit can create countermeasure plans efficiently. Furthermore, by adjusting the order of countermeasure plans according to the relevance of the problems, the countermeasure unit can maximize the user's learning effect. In this way, by adjusting the order of countermeasure plans based on the relevance of the problems, countermeasure plans can be created efficiently. Some or all of the above processing in the countermeasure unit may be performed using AI, for example, or without using AI. For example, the countermeasure unit can input problem relevance data into a generating AI, and the generating AI can analyze the data to adjust the order of countermeasure plans.

[0119] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0120] The ultimate AI teacher system can also include a learning style analysis unit that analyzes the user's learning style. This unit can determine whether the user has a visual, auditory, or tactile learning style and, based on the results, suggest the optimal learning method. For example, it can provide visual learners with materials that heavily utilize diagrams and videos, auditory learners with materials that emphasize audio explanations, and tactile learners with materials that encourage interactive problem-solving. This maximizes learning effectiveness by providing the optimal learning method tailored to the user's learning style.

[0121] The ultimate AI teacher system can also include a motivation maintenance unit to keep users motivated. This unit can provide praise and encouragement messages at appropriate times according to the user's learning progress. For example, when a user solves a difficult problem, it can display a message like "Well done!" to boost their motivation. If learning stagnates, it can also provide encouraging messages like "Let's keep going!". Furthermore, special messages or badges can be displayed when the user achieves their learning goals, giving them a sense of accomplishment. This helps maintain user motivation and encourages continued learning.

[0122] The ultimate AI teacher system can also include an environment optimization unit to further optimize the user's learning environment. This unit can monitor the user's learning environment (e.g., lighting, volume, temperature) and provide advice to ensure an optimal learning environment. For example, if the lighting is dim, it can advise "turn up the lights," and if the volume is too loud, it can suggest "lower the volume." It can also provide advice such as "adjust the room temperature" if the temperature is inappropriate. This allows users to learn in an optimal environment, improving their learning effectiveness.

[0123] The ultimate AI teacher system can also include a progress visualization unit to visualize the user's learning progress. This unit can visually display the user's learning data in graphs and charts, allowing users to grasp their learning progress at a glance. For example, it can display the user's accuracy rate and answer speed trends in line graphs, clearly showing progress in each area. It can also display the degree of achievement towards learning goals in pie charts, visually showing progress toward goal achievement. Furthermore, by comparing current data with past learning data, users can feel their own growth. This makes it easier for users to understand their learning progress and increases their motivation to learn.

[0124] The ultimate AI teacher system can also include a performance enhancement unit to further improve the user's learning performance. Based on the user's learning data, the performance enhancement unit can provide advice to maximize learning efficiency. For example, if a user tends to concentrate better during certain times, it can suggest studying during those times. It can also advise taking appropriate breaks if the user is tired. Furthermore, if there are areas for improvement in the user's learning methods, it can suggest specific improvement measures. This allows users to improve their learning performance and progress more efficiently.

[0125] The ultimate AI teacher system can further incorporate an emotion adjustment unit that estimates the user's emotions and adjusts the learning content based on those emotions. This unit can provide easy, relaxing problems when the user is stressed, and more challenging problems when the user is relaxed. For example, if the user is stressed, it can provide relaxing puzzles or game-style problems to reduce the learning burden. Conversely, if the user is relaxed, it can provide challenging problems to maximize learning effectiveness. Furthermore, if the user is focused, it can provide problems that leverage their concentration to improve learning efficiency. In this way, by providing learning content tailored to the user's emotions, learning effectiveness can be maximized.

[0126] The ultimate AI teacher system can also include a predictive planning unit that forecasts future learning plans based on the user's learning history. This unit can analyze the user's past learning data and automatically generate future learning plans. For example, it can suggest a plan that focuses on areas the user previously struggled with, allowing for more efficient learning. It can also suggest an appropriate learning schedule based on the user's learning pace. Furthermore, it can clearly outline the steps to achieving the user's learning goals. This allows the user to clarify their future learning plan and proceed with learning efficiently.

[0127] The ultimate AI teacher system can further incorporate a progress adjustment unit that estimates the user's emotions and adjusts the learning progress based on those emotions. This unit can slow down the learning progress when the user is stressed and accelerate it when the user is relaxed. For example, if the user is stressed, the learning progress can be slowed and relaxing problems can be provided to reduce the learning burden. If the user is relaxed, the learning progress can be accelerated and challenging problems can be provided to maximize learning effectiveness. Furthermore, if the user is focused, problems that leverage their concentration can be provided to improve learning efficiency. In this way, the learning effect can be maximized by adjusting the learning progress according to the user's emotions.

[0128] The ultimate AI teacher system can also include a comparative analysis unit that compares a user's learning data with that of other users. This unit compares the user's learning data with that of other users, allowing them to understand their relative learning progress. For example, it can clearly identify areas where a user excels and areas where improvement is needed compared to users of the same age or grade level. It can also show how much progress a user has made compared to their past self. Furthermore, by referencing the success stories of other users, it can suggest effective learning methods. This allows users to objectively understand their own learning progress and find effective learning methods.

[0129] The ultimate AI teacher system can further incorporate a feedback adjustment unit that estimates the user's emotions and adjusts learning feedback based on those emotions. This unit can provide gentle feedback when the user is stressed and detailed feedback when the user is relaxed. For example, if the user is stressed, it can provide encouraging feedback in gentle words to increase their motivation to learn. If the user is relaxed, it can provide detailed feedback and indicate specific areas for improvement. Furthermore, if the user is focused, it can provide visually clear feedback to improve learning efficiency. This maximizes learning effectiveness by providing feedback tailored to the user's emotions.

[0130] The following briefly describes the processing flow for example form 2.

[0131] Step 1: The data collection unit collects the user's problem-solving process and results. For example, it can collect the process and results of a user solving a problem on a tablet or smartphone. The data collection unit can collect data such as the speed at which the user solves the problem, the content of the calculation formula, the score, and the accuracy rate. For example, the data collection unit can record the speed at which a user solves a problem on a tablet in seconds. The data collection unit can also collect the content of the calculation formula for a problem solved by a user on a smartphone as text data. Furthermore, the data collection unit can collect the score and accuracy rate of the problems solved by the user as numerical data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using statistical analysis or machine learning algorithms, for example. Based on data such as the user's solving speed, the content of the calculation formulas, scores, and accuracy rates, the analysis unit can comprehensively analyze the user's weaknesses and challenges. For example, the analysis unit can analyze the distribution of the user's solving speed and calculate the mean and standard deviation. The analysis unit can also analyze the content of the user's calculation formulas and identify patterns of errors. Furthermore, the analysis unit can analyze the user's scores and accuracy rates to identify trends in problems that are weaknesses. Step 3: The proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit. For example, the proposal unit can propose a learning plan tailored to the user's weaknesses and challenges. The proposal unit can propose an optimal learning plan based on the user's learning goals and schedule. For example, the proposal unit can propose a plan that focuses on learning the user's weak points. The proposal unit can also propose an efficient learning plan that matches the user's learning schedule. Furthermore, the proposal unit can also propose appropriate learning materials and resources according to the user's learning goals. Step 4: The explanation unit provides real-time explanations to user questions. For example, the explanation unit can analyze a user's voice question and provide a detailed explanation of the problem's solution and related knowledge using diagrams, videos, and audio. The explanation unit can refine its explanation method and explain it repeatedly until the user understands. For example, if a user asks a voice question like, "I don't know how to solve this problem," the explanation unit can analyze the question and provide a detailed explanation of the problem's solution using diagrams, videos, and audio. The explanation unit can also try different explanation methods until the user understands. Furthermore, the explanation unit can adjust the content and method of the explanation according to the user's level of understanding.

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0134] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, explanation unit, analysis unit, and countermeasure unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's problem-solving process and results using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes a learning plan using the specific processing unit 290 of the data processing unit 12 based on the analysis results. The explanation unit provides a detailed explanation using diagrams, videos, and audio with the display 40A and speaker 40B of the smart device 14. The analysis unit comprehensively analyzes the user's weaknesses and challenges using the specific processing unit 290 of the data processing unit 12. The countermeasure unit outputs a future countermeasure plan using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0136] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0137] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0140] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, explanation unit, analysis unit, and countermeasure unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's problem-solving process and results using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes a learning plan using the specific processing unit 290 of the data processing unit 12 based on the analysis results. The explanation unit provides a detailed explanation using diagrams, videos, and audio with the display and speaker 240 of the smart glasses 214. The analysis unit comprehensively analyzes the user's weaknesses and challenges using the specific processing unit 290 of the data processing unit 12. The countermeasure unit outputs a future countermeasure plan using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0152] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0153] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0155] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0156] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0159] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, explanation unit, analysis unit, and countermeasure unit, is implemented by at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's problem-solving process and results using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes a learning plan using the specific processing unit 290 of the data processing unit 12 based on the analysis results. The explanation unit provides a detailed explanation using diagrams, videos, and audio with the display 343 and speaker 240 of the headset terminal 314. The analysis unit comprehensively analyzes the user's weaknesses and challenges using the specific processing unit 290 of the data processing unit 12. The countermeasure unit outputs a future countermeasure plan using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0169] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0170] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0171] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0172] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0173] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0174] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0175] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0176] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0177] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0178] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0179] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0180] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0181] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0182] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0183] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0184] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, explanation unit, analysis unit, and countermeasure unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects the user's problem-solving process and results using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes a learning plan based on the analysis results using the specific processing unit 290 of the data processing unit 12. The explanation unit provides a detailed explanation using diagrams, videos, and audio with the display and speaker 240 of the robot 414. The analysis unit comprehensively analyzes the user's weaknesses and challenges using the specific processing unit 290 of the data processing unit 12. The countermeasure unit outputs a future countermeasure plan using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0185] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0186] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0187] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0188] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0189] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0190] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0191] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0192] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0193] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0194] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0195] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0196] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0197] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0198] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0199] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0200] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0201] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0202] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0203] (Note 1) A collection unit that collects the user's problem-solving process and results, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit, It includes an explanation unit that provides real-time explanations to user questions. A system characterized by the following features. (Note 2) It features an explanatory section where the generating AI provides detailed explanations using diagrams, videos, and audio. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes an analysis department that comprehensively analyzes users' weaknesses and challenges. The system described in Appendix 1, characterized by the features described herein. (Note 4) We have a department that can output future countermeasure plans. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect the process and results of users solving problems on tablets and smartphones. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We analyze data such as the speed at which problems are solved, the content of the calculations, the score, and the correct answer rate for each problem. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of data collection in the problem-solving process based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past learning history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting problem-solving processes, filtering is performed based on the user's current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data for the problem-solving process, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data for the problem-solving process, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the problem was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the issues. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the category of the learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on the submission deadline for the learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on their relevance in the learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned explanatory section is, The system estimates the user's emotions and adjusts the way explanations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned explanatory section is, During the explanation, adjust the level of detail based on the importance of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned explanatory section is, When providing explanations, different explanation algorithms are applied depending on the category of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned explanatory section is, It estimates the user's emotions and adjusts the length of the explanation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned explanatory section is, When providing explanations, we will prioritize the explanations based on when the problems were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned explanatory section is, During the explanation, adjust the order of explanations based on the relevance of the issues. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the issue. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the category of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned analysis unit is During the analysis, prioritize the analysis based on when the problem was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of the issues. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned countermeasures unit, The system estimates the user's emotions and adjusts how the countermeasure plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned countermeasures unit, When creating a countermeasure plan, adjust the level of detail in the plan based on the severity of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned countermeasures unit, When creating a countermeasure plan, apply different countermeasure algorithms depending on the category of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned countermeasures unit, The system estimates the user's emotions and adjusts the length of the countermeasure plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned countermeasures unit, When creating a countermeasure plan, prioritize the countermeasure plan based on the submission date of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned countermeasures unit, When creating a countermeasure plan, adjust the order of the countermeasures based on the relationships between the problems. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects the user's problem-solving process and results, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit proposes a learning plan based on the analysis results obtained by the analysis unit, It includes an explanation unit that provides real-time explanations to user questions. A system characterized by the following features.

2. It features an explanatory section where the generating AI provides detailed explanations using diagrams, videos, and audio. The system according to feature 1.

3. It includes an analysis department that comprehensively analyzes users' weaknesses and challenges. The system according to feature 1.

4. We have a department that can output future countermeasure plans. The system according to feature 1.

5. The aforementioned collection unit is Collect the process and results of users solving problems on tablets and smartphones. The system according to feature 1.

6. The aforementioned analysis unit, We analyze data such as the speed at which problems are solved, the content of the calculations, the score, and the correct answer rate for each problem. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of data collection in the problem-solving process based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past learning history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting problem-solving processes, filtering is performed based on the user's current learning status and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

11. The aforementioned collection unit is When collecting data for the problem-solving process, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system according to feature 1.

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