System

Abstract

An object of a system according to an embodiment is to provide a personalized class that matches the personality and preferences of a user.SOLUTION: A system includes a reception unit, an analysis unit, a selection unit, a lesson unit, and a monitoring unit. The reception unit receives a personality questionnaire from a user. The analysis unit analyzes the data of the personality questionnaire received by the reception unit. The selection unit selects a AI teacher based on the information analyzed by the analysis unit. The lesson section conducts a lesson by the AI teacher selected by the selection section. The monitoring unit monitors the progress status of the lesson performed by the lesson unit in real time and adjusts the progress status as necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to provide personalized lessons tailored to the user's personality and preferences.

[0005] The system according to the embodiment aims to provide personalized lessons that match the user's personality and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a selection unit, a teaching unit, and a monitoring unit. The reception unit receives a personality questionnaire from a user. The analysis unit analyzes the personality questionnaire data received by the reception unit. The selection unit selects an AI teacher based on the data analyzed by the analysis unit. The teaching unit allows the AI ​​teacher selected by the selection unit to teach a lesson. The monitoring unit monitors the progress of the lesson conducted by the teaching unit in real time and makes adjustments as necessary. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized lessons that match the user's personality and preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention selects the most suitable AI teacher based on the user's personality and provides personalized lessons. This system accepts and analyzes a personality questionnaire from the user, selects the most suitable AI teacher, conducts lessons, monitors the progress of the lessons in real time, and makes adjustments as needed. This allows the system to select the most suitable AI teacher based on the user's personality and provide personalized lessons. For example, by providing lessons tailored to the user's personality, learning effectiveness can be improved. Furthermore, by monitoring the progress of lessons in real time and making adjustments as needed, more appropriate lessons can be provided.

[0029] The system according to the embodiment includes a reception unit, an analysis unit, a selection unit, a teaching unit, and a monitoring unit. The reception unit receives a personality questionnaire from a user. The personality questionnaire may include, but is not limited to, the type of questions and the format of answers. The reception unit receives, for example, personality questionnaire data entered by the user. The reception unit may also estimate the user's emotions and adjust the content of the personality questionnaire questions based on the estimated emotions. The analysis unit analyzes the personality questionnaire data received by the reception unit. The analysis is performed, for example, based on the algorithm used and the purpose of the analysis, for example, but is not limited to, the example. The analysis unit may, for example, analyze the personality questionnaire data to identify the user's personality. The analysis unit may also estimate the user's emotions and adjust the method of analyzing the personality questionnaire data based on the estimated emotions. The selection unit selects the optimal AI teacher based on the data analyzed by the analysis unit. The selection is performed, for example, based on the user's personality and preferences, for example, but is not limited to, the example. The selection unit selects the AI ​​teacher that best suits the user's personality. The selection unit can also estimate the user's emotions and adjust the selection criteria based on the estimated emotions. The teaching unit allows the AI ​​teacher selected by the selection unit to conduct a lesson. The lesson is conducted based on, for example, the lesson progress method and the teaching materials used, but is not limited to these examples. The teaching unit can adjust the lesson content based on, for example, the user's level of understanding. The teaching unit can also estimate the user's emotions and adjust the lesson content based on the estimated emotions. The monitoring unit monitors the progress of the lesson conducted by the teaching unit in real time and adjusts it as necessary. Monitoring can be performed based on, for example, the items to be monitored and the real-time adjustment method, but is not limited to these examples. The monitoring unit can monitor, for example, the progress of the lesson and adjust the progress of the lesson as necessary. The monitoring unit can also estimate the user's emotions and adjust the progress of the lesson based on the estimated emotions. As a result, the system according to the embodiment can select the optimal AI teacher based on the user's personality and provide personalized lessons. For example, providing lessons tailored to the user's personality can improve learning effectiveness.In addition, the progress of lessons can be monitored in real time and adjusted as needed to provide more appropriate lessons.

[0030] When accepting the personality questionnaire, the reception unit can adjust the order of questions by referring to the user's past answer history. For example, the reception unit presents questions in the most efficient order based on the order in which the user has previously answered questions. The reception unit can also prioritize questions that the user found easy to answer in the past, thereby reducing the burden of answering. The reception unit can also present highly relevant questions based on the user's past answer history first, thereby improving the accuracy of the answers. By referring to the past answer history, the order of questions can be optimized and the burden of answering can be reduced. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past answer history data into a generation AI and cause the generation AI to optimize the order of questions.

[0031] The reception unit can customize the content of questions based on the user's current situation (time of day, location, etc.) when accepting the personality questionnaire. For example, if the user takes the questionnaire at night, the reception unit can provide relaxed question content. Furthermore, if the user takes the questionnaire while out, the reception unit can also provide question content that can be answered in a short time. Furthermore, if the user takes the questionnaire at home, the reception unit can also provide a questionnaire that includes detailed questions. In this way, by customizing the question content according to the user's current situation, a more appropriate personality questionnaire can be conducted. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current situation data into the generation AI and cause the generation AI to customize the question content.

[0032] When accepting the personality questionnaire, the reception unit can select a reception means according to the user's input method. For example, if the user desires voice input, the reception unit can use voice recognition technology to accept questions. Furthermore, if the user desires text input, the reception unit can also provide a text box to accept questions. Furthermore, if the user desires image input, the reception unit can also use image analysis technology to accept questions. This allows the reception of the personality questionnaire to be carried out smoothly by selecting the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data into a generation AI and have the generation AI select the optimal reception means.

[0033] When accepting the personality questionnaire, the reception unit can prioritize presenting highly relevant questions taking into account the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize presenting questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize presenting questions related to the travel destination. Furthermore, if the user lives in a specific city, the reception unit can prioritize presenting questions related to that city. This improves the accuracy of the personality questionnaire by presenting highly relevant questions based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to present highly relevant questions.

[0034] When accepting the personality questionnaire, the reception unit can analyze the user's social media activity and present relevant questions. The reception unit can present relevant questions based on, for example, the content frequently posted by the user on social media. The reception unit can also analyze the user's friendships on social media and present relevant questions. The reception unit can also analyze the user's interests on social media and present relevant questions. This improves the accuracy of the personality questionnaire by presenting relevant questions based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to present relevant questions.

[0035] When accepting the personality questionnaire, the reception unit can customize the content of the questions based on the user's past feedback. The reception unit can, for example, improve the content of the questions based on feedback provided by the user in the past. The reception unit can also delete questions that the user found difficult to answer in the past and add questions that are easier to answer. The reception unit can also analyze the user's past feedback and optimize the content of the questions. This optimizes the content of the questions by reflecting the user's past feedback, thereby improving the accuracy of the personality questionnaire. Some or all of the above-mentioned processing by the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the content of the questions.

[0036] When analyzing the data from the personality questionnaire, the analysis unit can improve the accuracy of the analysis by referring to the user's past response data. For example, the analysis unit analyzes the current response data based on the user's past response data. The analysis unit can also improve the accuracy of the analysis by referring to the user's past response data. The analysis unit can also analyze the user's past response data and find correlations with the current response data. By referring to the past response data, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past response data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0037] When analyzing the data from the personality questionnaire, the analysis unit can perform the analysis based on the user's attribute information. For example, the analysis unit can perform data analysis based on the user's age to find trends by age group. The analysis unit can also perform data analysis based on the user's gender to find trends by gender. The analysis unit can also personalize the analysis results by taking the user's attribute information into consideration. This improves the accuracy of the analysis results by performing the analysis based on the user's attribute information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's attribute information data into a generation AI and have the generation AI perform data analysis.

[0038] When analyzing the data from the personality questionnaire, the analysis unit can weight the analysis based on the frequency of the user's responses. For example, the analysis unit can assign a higher weight to questions that the user frequently answers. The analysis unit can also assign a lower weight to questions that the user rarely answers. The analysis unit can also evaluate the reliability of the analysis results based on the frequency of the user's responses. Thus, weighting the analysis based on the frequency of the user's responses improves the reliability of the analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's response frequency data into a generation AI and have the generation AI perform the weighting of the analysis.

[0039] When analyzing the data from the personality questionnaire, the analysis unit can perform the analysis based on the geographical distribution of users. For example, the analysis unit performs data analysis based on the user's place of residence to identify regional trends. The analysis unit can also classify the analysis results by region, taking the geographical distribution of users into consideration. The analysis unit can also analyze regional trends based on the geographical distribution of users. In this way, by performing the analysis based on the geographical distribution of users, analysis results that reflect the characteristics of each region can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographical distribution data of users into a generation AI and have the generation AI perform data analysis.

[0040] When analyzing the personality questionnaire data, the analysis unit can improve the accuracy of the analysis by referring to related literature. For example, the analysis unit can refer to related literature to improve the reliability of the analysis results. The analysis unit can also improve the analysis method and improve accuracy based on the related literature. The analysis unit can also complement the analysis results by referring to related literature. In this way, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related literature data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0041] When analyzing the data from the personality questionnaire, the analysis unit can perform the analysis based on the user's market value. The analysis unit can perform data analysis based on the user's occupation and income, for example, to evaluate the market value. The analysis unit can also personalize the analysis results by taking the user's market value into consideration. The analysis unit can also evaluate the reliability of the analysis results based on the user's market value. As a result, performing the analysis based on the user's market value improves the reliability of the analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's market value data into a generation AI and have the generation AI perform data analysis.

[0042] When selecting an AI teacher, the selection unit can improve the accuracy of the selection by referring to the user's past learning history. The selection unit, for example, selects the most suitable AI teacher based on the user's past learning history. The selection unit can also improve the accuracy of the selection by referring to the user's past learning history. The selection unit can also analyze the user's past learning history and select the most effective AI teacher. In this way, by referring to the past learning history, the accuracy of the selection is improved. Some or all of the above-mentioned processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's past learning history data into the generation AI and cause the generation AI to improve the accuracy of the selection.

[0043] When selecting an AI teacher, the selection unit can make the selection based on the user's attribute information. For example, the selection unit can select an AI teacher based on the user's age and provide lessons appropriate for that age group. The selection unit can also select an AI teacher based on the user's gender and provide lessons appropriate for that gender. The selection unit can also select the most appropriate AI teacher by taking the user's attribute information into consideration. This makes it possible to select a more appropriate AI teacher by making a selection based on the user's attribute information. Some or all of the above-mentioned processing in the selection unit can be performed using AI, for example, or without AI. For example, the selection unit can input the user's attribute information data into the generation AI and have the generation AI perform the selection.

[0044] When selecting an AI teacher, the selection unit can weight the selection based on the user's learning frequency. For example, if the user studies frequently, the selection unit can select an AI teacher who provides detailed explanations. Furthermore, if the user studies infrequently, the selection unit can also select an AI teacher who provides concise, to-the-point explanations. Furthermore, the selection unit can select the optimal AI teacher based on the user's learning frequency to maximize the learning effect. Thus, by weighting the selection based on the user's learning frequency, a more appropriate AI teacher can be selected. Some or all of the above-described processing in the selection unit may be performed using, or without, AI. For example, the selection unit can input the user's learning frequency data into the generation AI and have the generation AI perform the selection weighting.

[0045] When selecting an AI teacher, the selection unit can make the selection based on the geographical distribution of users. For example, the selection unit can select an AI teacher based on the user's place of residence to meet regional needs. The selection unit can also select the most suitable AI teacher by taking the geographical distribution of users into consideration. The selection unit can also select an AI teacher that suits the characteristics of each region based on the geographical distribution of users. In this way, by making a selection based on the geographical distribution of users, an AI teacher that meets regional needs can be selected. Some or all of the above-mentioned processing by the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input users' geographical distribution data into the generation AI and have the generation AI perform the selection.

[0046] When selecting an AI teacher, the selection unit can improve the accuracy of the selection by referring to related literature. For example, the selection unit can refer to related literature and improve the selection criteria to improve accuracy. The selection unit can also optimize the selection method based on the related literature and select the optimal AI teacher. The selection unit can also improve the reliability of the selection results by referring to related literature. In this way, the accuracy of the selection is improved by referring to related literature. Some or all of the above-mentioned processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the selection.

[0047] When selecting an AI teacher, the selection unit can make the selection based on the user's market value. For example, the selection unit selects an AI teacher based on the user's occupation and income and evaluates the market value. The selection unit can also select the most suitable AI teacher by taking the user's market value into consideration. The selection unit can also evaluate the reliability of the selection result based on the user's market value. As a result, by making a selection based on the user's market value, the reliability of the selection result is improved. Some or all of the above-mentioned processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's market value data into the generation AI and have the generation AI perform the selection.

[0048] As the lesson progresses, the teaching unit can improve the accuracy of the lesson by referring to the user's past learning history. The teaching unit, for example, adjusts the lesson content based on the user's past learning history. The teaching unit can also improve the accuracy of the lesson by referring to the user's past learning history. The teaching unit can also analyze the user's past learning history and provide the most effective lesson content. In this way, the accuracy of the lesson is improved by referring to the past learning history. Some or all of the above-mentioned processing in the teaching unit may be performed using, for example, AI, or may be performed without using AI. For example, the teaching unit can input the user's past learning history data into the generation AI and have the generation AI improve the accuracy of the lesson.

[0049] The teaching unit can conduct lessons based on the user's attribute information as the lesson progresses. For example, the teaching unit can adjust the lesson content based on the user's age and provide lessons appropriate for that age group. The teaching unit can also adjust the lesson content based on the user's gender and provide lessons appropriate for that gender. The teaching unit can also provide optimal lesson content by taking the user's attribute information into consideration. This makes it possible to provide more appropriate lessons by conducting lessons based on the user's attribute information. Some or all of the above-mentioned processing in the teaching unit can be performed using, or without, AI. For example, the teaching unit can input the user's attribute information data into a generation AI and have the generation AI adjust the lesson content.

[0050] The teaching unit can weight lessons based on the user's study frequency as the lessons progress. For example, if the user studies frequently, the teaching unit can provide lessons with detailed explanations. Furthermore, if the user studies infrequently, the teaching unit can also provide concise lessons that focus on the main points. Furthermore, the teaching unit can adjust the content of lessons based on the user's study frequency to maximize the learning effect. Thus, by weighting lessons based on the user's study frequency, more appropriate lessons can be provided. Some or all of the above-described processing in the teaching unit may be performed using, or without, AI, for example. For example, the teaching unit can input the user's study frequency data into a generation AI and have the generation AI weight the lessons.

[0051] The teaching unit can conduct lessons based on the geographical distribution of users as the lessons progress. For example, the teaching unit can adjust the lesson content based on the user's place of residence to meet regional needs. The teaching unit can also provide optimal lesson content by taking the users' geographical distribution into consideration. The teaching unit can also provide lessons that suit the characteristics of each region based on the users' geographical distribution. In this way, lessons can be conducted based on the users' geographical distribution to provide lessons that meet regional needs. Some or all of the above-mentioned processing in the teaching unit may be performed using, for example, AI, or may be performed without using AI. For example, the teaching unit can input users' geographical distribution data into a generation AI and have the generation AI adjust the lesson content.

[0052] The teaching unit can refer to related literature as the lesson progresses to improve the accuracy of the lesson. For example, the teaching unit can refer to related literature to improve the reliability of the lesson content. The teaching unit can also improve the teaching method and improve the accuracy based on the related literature. The teaching unit can also refer to related literature to complement the lesson content. In this way, the accuracy of the lesson is improved by referring to related literature. Some or all of the above-mentioned processing in the teaching unit may be performed using AI, for example, or may be performed without using AI. For example, the teaching unit can input related literature data into a generation AI and have the generation AI improve the accuracy of the lesson.

[0053] The teaching unit can conduct lessons based on the user's market value as the lesson progresses. The teaching unit can adjust the lesson content based on the user's occupation and income, for example, and evaluate the market value. The teaching unit can also provide optimal lesson content taking the user's market value into consideration. The teaching unit can also evaluate the reliability of the lesson content based on the user's market value. As a result, by conducting lessons based on the user's market value, the reliability of the lesson content is improved. Some or all of the above-mentioned processing in the teaching unit can be performed, for example, using AI, or can be performed without using AI. For example, the teaching unit can input the user's market value data into a generation AI and have the generation AI adjust the lesson content.

[0054] When monitoring the progress of a class, the monitoring unit can improve the accuracy of the monitoring by referring to the user's past learning history. The monitoring unit monitors the progress of a class, for example, based on the user's past learning history. The monitoring unit can also improve the accuracy of the monitoring by referring to the user's past learning history. The monitoring unit can also analyze the user's past learning history and provide the most effective monitoring method. By referring to the past learning history, the accuracy of the monitoring is improved. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past learning history data into the generation AI and cause the generation AI to improve the accuracy of the monitoring.

[0055] When monitoring the progress of a class, the monitoring unit can perform monitoring based on the user's attribute information. For example, the monitoring unit can monitor the progress of a class based on the user's age and perform monitoring appropriate for the age group. The monitoring unit can also monitor the progress of a class based on the user's gender and perform monitoring appropriate for the gender. The monitoring unit can also provide an optimal monitoring method by taking the user's attribute information into consideration. This allows for more appropriate monitoring by performing monitoring based on the user's attribute information. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input the user's attribute information data into the generation AI and have the generation AI perform monitoring.

[0056] When monitoring the progress of a lesson, the monitoring unit can weight the monitoring based on the user's learning frequency. For example, if the user studies frequently, the monitoring unit can perform detailed monitoring. Also, if the user studies infrequently, the monitoring unit can perform brief monitoring. The monitoring unit can also adjust the monitoring method based on the user's learning frequency to maximize the learning effect. Thus, more appropriate monitoring can be performed by weighting the monitoring based on the user's learning frequency. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's learning frequency data into the generation AI and have the generation AI perform the monitoring weighting.

[0057] When monitoring the progress of a class, the monitoring unit can perform monitoring based on the geographical distribution of users. For example, the monitoring unit can monitor the progress of a class based on the user's place of residence, thereby meeting regional needs. The monitoring unit can also provide an optimal monitoring method by taking the geographical distribution of users into consideration. The monitoring unit can also perform monitoring that suits the characteristics of each region based on the geographical distribution of users. In this way, monitoring based on the geographical distribution of users can meet regional needs. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input user geographical distribution data into a generation AI and have the generation AI perform monitoring.

[0058] When monitoring the progress of a class, the monitoring unit can improve the accuracy of the monitoring by referring to related literature. For example, the monitoring unit can refer to related literature and improve the monitoring method to improve accuracy. The monitoring unit can also optimize the monitoring method based on the related literature and provide an optimal monitoring method. The monitoring unit can also improve the reliability of the monitoring results by referring to related literature. In this way, the accuracy of monitoring is improved by referring to related literature. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input related literature data into the generation AI and have the generation AI improve the accuracy of monitoring.

[0059] When monitoring the progress of a class, the monitoring unit can perform monitoring based on the market value of the user. The monitoring unit monitors the progress of a class and evaluates the market value, for example, based on the user's occupation and income. The monitoring unit can also provide an optimal monitoring method taking the user's market value into consideration. The monitoring unit can also evaluate the reliability of the monitoring results based on the user's market value. As a result, monitoring based on the user's market value improves the reliability of the monitoring results. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's market value data into a generation AI and have the generation AI perform monitoring.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The reception unit can also monitor the user's health condition and adjust the questions in the personality questionnaire based on the user's health condition. For example, if the user is tired, the questions can be simplified and changed to a format that is easier to answer. If the user is healthy, more detailed questions can be included to conduct an in-depth personality analysis. Furthermore, if the user is ill, the number of questions can be reduced and focused on important questions. This allows the personality questionnaire to be conducted more appropriately by adjusting the questions according to the user's health condition.

[0062] The analysis unit can also analyze the user's hobbies and interests and adjust the data analysis method for the personality questionnaire based on the analysis results. For example, if the user is interested in sports, questions related to sports can be analyzed preferentially. If the user is interested in music, questions related to music can be analyzed in detail. Furthermore, if the user is interested in reading, questions related to reading can be analyzed in depth. In this way, adjusting the data analysis method according to the user's hobbies and interests improves the analysis accuracy of the personality questionnaire.

[0063] The selection unit can also analyze the user's learning style and adjust the selection criteria for the optimal AI teacher based on the analysis results. For example, if the user has a visual learning style, it can select an AI teacher who uses a lot of visual learning materials. If the user has an auditory learning style, it can select an AI teacher who uses a lot of audio learning materials. Furthermore, if the user has an experiential learning style, it can select an AI teacher who gives practical lessons. In this way, by adjusting the selection criteria according to the user's learning style, it is possible to select a more appropriate AI teacher.

[0064] The lesson module can also set the user's learning goals and adjust the lesson content based on the set learning goals. For example, if the user wants to acquire a specific skill in a short period of time, lesson content specialized for that skill can be provided. Also, if the user has a long-term learning goal, lesson content for acquiring the skill in stages can be provided. Furthermore, if the user wants to pass a specific exam, lesson content corresponding to that exam can be provided. In this way, more effective lessons can be provided by adjusting the lesson content according to the user's learning goals.

[0065] The monitoring unit can also monitor the user's learning environment and adjust the progress of the lesson based on the learning environment. For example, if the user is learning in a quiet environment, the lesson can be conducted to improve concentration. If the user is learning in a noisy environment, the lesson can be conducted in a short period of time to cover the main points. Furthermore, if the user is learning while on the move, the lesson can be conducted in a concise and highly visible manner. This allows the progress of the lesson to be adjusted according to the user's learning environment, making it possible to provide more appropriate lessons.

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

[0067] Step 1: The reception unit receives a personality questionnaire from a user. The personality questionnaire includes, but is not limited to, the types of questions and the answer formats. The reception unit receives the personality questionnaire data entered by the user. The reception unit can also estimate the user's emotions and adjust the content of the personality questionnaire questions based on the estimated emotions. Step 2: The analysis unit analyzes the personality questionnaire data received by the reception unit. The analysis is performed based on, but not limited to, the algorithm used and the purpose of the analysis. The analysis unit analyzes the personality questionnaire data and identifies the user's personality. The analysis unit can also estimate the user's emotions and adjust the personality questionnaire data analysis method based on the estimated emotions. Step 3: The selection unit selects the optimal AI teacher based on the data analyzed by the analysis unit. The selection is based on, but not limited to, the user's personality and preferences. The selection unit selects the AI ​​teacher that best suits the user's personality. The selection unit can also estimate the user's emotions and adjust the selection criteria based on the estimated emotions. Step 4: The teaching department will have the AI ​​teacher selected by the selection department conduct the lesson. The lesson will be based on, but not limited to, the lesson progress method and the teaching materials used. The teaching department will adjust the lesson content according to the user's level of understanding. The teaching department can also estimate the user's emotions and adjust the lesson content based on the estimated emotions. Step 5: The monitoring unit monitors the progress of the lesson conducted by the teaching unit in real time and adjusts as necessary. The monitoring is performed based on, but not limited to, the items to be monitored and the real-time adjustment method. The monitoring unit monitors the progress of the lesson and adjusts the progress of the lesson as necessary. The monitoring unit can also estimate the user's emotions and adjust the progress of the lesson based on the estimated emotions.

[0068] (Example 2) A system according to an embodiment of the present invention selects the most suitable AI teacher based on the user's personality and provides personalized lessons. This system accepts and analyzes a personality questionnaire from the user, selects the most suitable AI teacher, conducts lessons, monitors the progress of the lessons in real time, and makes adjustments as needed. This allows the system to select the most suitable AI teacher based on the user's personality and provide personalized lessons. For example, by providing lessons tailored to the user's personality, learning effectiveness can be improved. Furthermore, by monitoring the progress of lessons in real time and making adjustments as needed, more appropriate lessons can be provided.

[0069] The system according to the embodiment includes a reception unit, an analysis unit, a selection unit, a teaching unit, and a monitoring unit. The reception unit receives a personality questionnaire from a user. The personality questionnaire may include, but is not limited to, the type of questions and the format of answers. The reception unit receives, for example, personality questionnaire data entered by the user. The reception unit may also estimate the user's emotions and adjust the content of the personality questionnaire questions based on the estimated emotions. The analysis unit analyzes the personality questionnaire data received by the reception unit. The analysis is performed, for example, based on the algorithm used and the purpose of the analysis, for example, but is not limited to, the example. The analysis unit may, for example, analyze the personality questionnaire data to identify the user's personality. The analysis unit may also estimate the user's emotions and adjust the method of analyzing the personality questionnaire data based on the estimated emotions. The selection unit selects the optimal AI teacher based on the data analyzed by the analysis unit. The selection is performed, for example, based on the user's personality and preferences, for example, but is not limited to, the example. The selection unit selects the AI ​​teacher that best suits the user's personality. The selection unit can also estimate the user's emotions and adjust the selection criteria based on the estimated emotions. The teaching unit allows the AI ​​teacher selected by the selection unit to conduct a lesson. The lesson is conducted based on, for example, the lesson progress method and the teaching materials used, but is not limited to these examples. The teaching unit can adjust the lesson content based on, for example, the user's level of understanding. The teaching unit can also estimate the user's emotions and adjust the lesson content based on the estimated emotions. The monitoring unit monitors the progress of the lesson conducted by the teaching unit in real time and adjusts it as necessary. Monitoring can be performed based on, for example, the items to be monitored and the real-time adjustment method, but is not limited to these examples. The monitoring unit can monitor, for example, the progress of the lesson and adjust the progress of the lesson as necessary. The monitoring unit can also estimate the user's emotions and adjust the progress of the lesson based on the estimated emotions. As a result, the system according to the embodiment can select the optimal AI teacher based on the user's personality and provide personalized lessons. For example, providing lessons tailored to the user's personality can improve learning effectiveness.In addition, the progress of lessons can be monitored in real time and adjusted as needed to provide more appropriate lessons.

[0070] The reception unit can estimate the user's emotions and adjust the content of the personality questionnaire questions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can simplify the content of the questions and change them to a format that is easier to answer. Furthermore, if the user is relaxed, the reception unit can also conduct an in-depth personality analysis, including detailed questions. Furthermore, if the user is in a hurry, the reception unit can reduce the number of questions and focus on important questions so that they can be answered in a short time. This allows for a more appropriate personality questionnaire to be conducted by adjusting the content of the questions according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0071] When accepting the personality questionnaire, the reception unit can adjust the order of questions by referring to the user's past answer history. For example, the reception unit presents questions in the most efficient order based on the order in which the user has previously answered questions. The reception unit can also prioritize questions that the user found easy to answer in the past, thereby reducing the burden of answering. The reception unit can also present highly relevant questions based on the user's past answer history first, thereby improving the accuracy of the answers. By referring to the past answer history, the order of questions can be optimized and the burden of answering can be reduced. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past answer history data into a generation AI and cause the generation AI to optimize the order of questions.

[0072] The reception unit can customize the content of questions based on the user's current situation (time of day, location, etc.) when accepting the personality questionnaire. For example, if the user takes the questionnaire at night, the reception unit can provide relaxed question content. Furthermore, if the user takes the questionnaire while out, the reception unit can also provide question content that can be answered in a short time. Furthermore, if the user takes the questionnaire at home, the reception unit can also provide a questionnaire that includes detailed questions. In this way, by customizing the question content according to the user's current situation, a more appropriate personality questionnaire can be conducted. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current situation data into the generation AI and cause the generation AI to customize the question content.

[0073] When accepting the personality questionnaire, the reception unit can select a reception means according to the user's input method. For example, if the user desires voice input, the reception unit can use voice recognition technology to accept questions. Furthermore, if the user desires text input, the reception unit can also provide a text box to accept questions. Furthermore, if the user desires image input, the reception unit can also use image analysis technology to accept questions. This allows the reception of the personality questionnaire to be carried out smoothly by selecting the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data into a generation AI and have the generation AI select the optimal reception means.

[0074] The reception unit can estimate the user's emotions and adjust the response method for the personality questionnaire based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple response method to reduce the burden of response. Furthermore, if the user is relaxed, the reception unit can provide a detailed response method and perform an in-depth personality analysis. Furthermore, if the user is in a hurry, the reception unit can simplify the response method so that the user can respond in a short time. This allows for a more appropriate personality questionnaire to be conducted by adjusting the response method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the response method.

[0075] When accepting the personality questionnaire, the reception unit can prioritize presenting highly relevant questions taking into account the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize presenting questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize presenting questions related to the travel destination. Furthermore, if the user lives in a specific city, the reception unit can prioritize presenting questions related to that city. This improves the accuracy of the personality questionnaire by presenting highly relevant questions based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to present highly relevant questions.

[0076] When accepting the personality questionnaire, the reception unit can analyze the user's social media activity and present relevant questions. The reception unit can present relevant questions based on, for example, the content frequently posted by the user on social media. The reception unit can also analyze the user's friendships on social media and present relevant questions. The reception unit can also analyze the user's interests on social media and present relevant questions. This improves the accuracy of the personality questionnaire by presenting relevant questions based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to present relevant questions.

[0077] When accepting the personality questionnaire, the reception unit can customize the content of the questions based on the user's past feedback. The reception unit can, for example, improve the content of the questions based on feedback provided by the user in the past. The reception unit can also delete questions that the user found difficult to answer in the past and add questions that are easier to answer. The reception unit can also analyze the user's past feedback and optimize the content of the questions. This optimizes the content of the questions by reflecting the user's past feedback, thereby improving the accuracy of the personality questionnaire. Some or all of the above-mentioned processing by the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the content of the questions.

[0078] The analysis unit can estimate the user's emotions and adjust the data analysis method of the personality questionnaire based on the estimated user emotions. For example, if the user is stressed, the analysis unit can perform analysis using a simple data analysis method. Furthermore, if the user is relaxed, the analysis unit can perform analysis using a detailed data analysis method. Furthermore, if the user is in a hurry, the analysis unit can use a simplified data analysis method to perform analysis quickly. This improves the analysis accuracy of the personality questionnaire by adjusting the data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the data analysis method.

[0079] When analyzing the data from the personality questionnaire, the analysis unit can improve the accuracy of the analysis by referring to the user's past response data. For example, the analysis unit analyzes the current response data based on the user's past response data. The analysis unit can also improve the accuracy of the analysis by referring to the user's past response data. The analysis unit can also analyze the user's past response data and find correlations with the current response data. By referring to the past response data, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past response data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0080] When analyzing the data from the personality questionnaire, the analysis unit can perform the analysis based on the user's attribute information. For example, the analysis unit can perform data analysis based on the user's age to find trends by age group. The analysis unit can also perform data analysis based on the user's gender to find trends by gender. The analysis unit can also personalize the analysis results by taking the user's attribute information into consideration. This improves the accuracy of the analysis results by performing the analysis based on the user's attribute information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's attribute information data into a generation AI and have the generation AI perform data analysis.

[0081] When analyzing the data from the personality questionnaire, the analysis unit can weight the analysis based on the frequency of the user's responses. For example, the analysis unit can assign a higher weight to questions that the user frequently answers. The analysis unit can also assign a lower weight to questions that the user rarely answers. The analysis unit can also evaluate the reliability of the analysis results based on the frequency of the user's responses. Thus, weighting the analysis based on the frequency of the user's responses improves the reliability of the analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's response frequency data into a generation AI and have the generation AI perform the weighting of the analysis.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This improves the visibility of the analysis results by adjusting the display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0083] When analyzing the data from the personality questionnaire, the analysis unit can perform the analysis based on the geographical distribution of users. For example, the analysis unit performs data analysis based on the user's place of residence to identify regional trends. The analysis unit can also classify the analysis results by region, taking the geographical distribution of users into consideration. The analysis unit can also analyze regional trends based on the geographical distribution of users. In this way, by performing the analysis based on the geographical distribution of users, analysis results that reflect the characteristics of each region can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographical distribution data of users into a generation AI and have the generation AI perform data analysis.

[0084] When analyzing the personality questionnaire data, the analysis unit can improve the accuracy of the analysis by referring to related literature. For example, the analysis unit can refer to related literature to improve the reliability of the analysis results. The analysis unit can also improve the analysis method and improve accuracy based on the related literature. The analysis unit can also complement the analysis results by referring to related literature. In this way, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related literature data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0085] When analyzing the data from the personality questionnaire, the analysis unit can perform the analysis based on the user's market value. The analysis unit can perform data analysis based on the user's occupation and income, for example, to evaluate the market value. The analysis unit can also personalize the analysis results by taking the user's market value into consideration. The analysis unit can also evaluate the reliability of the analysis results based on the user's market value. As a result, performing the analysis based on the user's market value improves the reliability of the analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's market value data into a generation AI and have the generation AI perform data analysis.

[0086] The selection unit can estimate the user's emotions and adjust the selection criteria for the optimal AI teacher based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit selects an AI teacher who helps the user relax. Furthermore, if the user is relaxed, the selection unit can select an AI teacher who provides detailed explanations. Furthermore, if the user is in a hurry, the selection unit can select an AI teacher who quickly proceeds with the lesson. By adjusting the selection criteria according to the user's emotions, a more appropriate AI teacher can be selected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit can be performed using, for example, an AI, or without an AI. For example, the selection unit can input the user's emotion data into the generation AI and have the generation AI adjust the selection criteria.

[0087] When selecting an AI teacher, the selection unit can improve the accuracy of the selection by referring to the user's past learning history. The selection unit, for example, selects the most suitable AI teacher based on the user's past learning history. The selection unit can also improve the accuracy of the selection by referring to the user's past learning history. The selection unit can also analyze the user's past learning history and select the most effective AI teacher. In this way, by referring to the past learning history, the accuracy of the selection is improved. Some or all of the above-mentioned processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's past learning history data into the generation AI and cause the generation AI to improve the accuracy of the selection.

[0088] When selecting an AI teacher, the selection unit can make the selection based on the user's attribute information. For example, the selection unit can select an AI teacher based on the user's age and provide lessons appropriate for that age group. The selection unit can also select an AI teacher based on the user's gender and provide lessons appropriate for that gender. The selection unit can also select the most appropriate AI teacher by taking the user's attribute information into consideration. This makes it possible to select a more appropriate AI teacher by making a selection based on the user's attribute information. Some or all of the above-mentioned processing in the selection unit can be performed using AI, for example, or without AI. For example, the selection unit can input the user's attribute information data into the generation AI and have the generation AI perform the selection.

[0089] When selecting an AI teacher, the selection unit can weight the selection based on the user's learning frequency. For example, if the user studies frequently, the selection unit can select an AI teacher who provides detailed explanations. Furthermore, if the user studies infrequently, the selection unit can also select an AI teacher who provides concise, to-the-point explanations. Furthermore, the selection unit can select the optimal AI teacher based on the user's learning frequency to maximize the learning effect. Thus, by weighting the selection based on the user's learning frequency, a more appropriate AI teacher can be selected. Some or all of the above-described processing in the selection unit may be performed using, or without, AI. For example, the selection unit can input the user's learning frequency data into the generation AI and have the generation AI perform the selection weighting.

[0090] The selection unit can estimate the user's emotions and adjust the display method of the selection results based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the selection unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the selection unit can provide a display method that focuses on the main points. By adjusting the display method according to the user's emotions, the visibility of the selection results is improved. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the selection unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0091] When selecting an AI teacher, the selection unit can make the selection based on the geographical distribution of users. For example, the selection unit can select an AI teacher based on the user's place of residence to meet regional needs. The selection unit can also select the most suitable AI teacher by taking the geographical distribution of users into consideration. The selection unit can also select an AI teacher that suits the characteristics of each region based on the geographical distribution of users. In this way, by making a selection based on the geographical distribution of users, an AI teacher that meets regional needs can be selected. Some or all of the above-mentioned processing by the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input users' geographical distribution data into the generation AI and have the generation AI perform the selection.

[0092] When selecting an AI teacher, the selection unit can improve the accuracy of the selection by referring to related literature. For example, the selection unit can refer to related literature and improve the selection criteria to improve accuracy. The selection unit can also optimize the selection method based on the related literature and select the optimal AI teacher. The selection unit can also improve the reliability of the selection results by referring to related literature. In this way, the accuracy of the selection is improved by referring to related literature. Some or all of the above-mentioned processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the selection.

[0093] When selecting an AI teacher, the selection unit can make the selection based on the user's market value. For example, the selection unit selects an AI teacher based on the user's occupation and income and evaluates the market value. The selection unit can also select the most suitable AI teacher by taking the user's market value into consideration. The selection unit can also evaluate the reliability of the selection result based on the user's market value. As a result, by making a selection based on the user's market value, the reliability of the selection result is improved. Some or all of the above-mentioned processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's market value data into the generation AI and have the generation AI perform the selection.

[0094] The teaching unit can estimate the user's emotions and adjust the lesson content based on the estimated user emotions. For example, if the user is feeling stressed, the teaching unit can provide relaxing content. If the user is relaxed, the teaching unit can also provide content with detailed explanations. If the user is in a hurry, the teaching unit can also provide content that focuses on the main points. This allows the lesson content to be adjusted according to the user's emotions, thereby providing a more appropriate lesson. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the teaching unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the teaching unit can input the user's emotion data into the generation AI and have the generation AI adjust the lesson content.

[0095] As the lesson progresses, the teaching unit can improve the accuracy of the lesson by referring to the user's past learning history. The teaching unit, for example, adjusts the lesson content based on the user's past learning history. The teaching unit can also improve the accuracy of the lesson by referring to the user's past learning history. The teaching unit can also analyze the user's past learning history and provide the most effective lesson content. In this way, the accuracy of the lesson is improved by referring to the past learning history. Some or all of the above-mentioned processing in the teaching unit may be performed using, for example, AI, or may be performed without using AI. For example, the teaching unit can input the user's past learning history data into the generation AI and have the generation AI improve the accuracy of the lesson.

[0096] The teaching unit can conduct lessons based on the user's attribute information as the lesson progresses. For example, the teaching unit can adjust the lesson content based on the user's age and provide lessons appropriate for that age group. The teaching unit can also adjust the lesson content based on the user's gender and provide lessons appropriate for that gender. The teaching unit can also provide optimal lesson content by taking the user's attribute information into consideration. This makes it possible to provide more appropriate lessons by conducting lessons based on the user's attribute information. Some or all of the above-mentioned processing in the teaching unit can be performed using, or without, AI. For example, the teaching unit can input the user's attribute information data into a generation AI and have the generation AI adjust the lesson content.

[0097] The teaching unit can weight lessons based on the user's study frequency as the lessons progress. For example, if the user studies frequently, the teaching unit can provide lessons with detailed explanations. Furthermore, if the user studies infrequently, the teaching unit can also provide concise lessons that focus on the main points. Furthermore, the teaching unit can adjust the content of lessons based on the user's study frequency to maximize the learning effect. Thus, by weighting lessons based on the user's study frequency, more appropriate lessons can be provided. Some or all of the above-described processing in the teaching unit may be performed using, or without, AI, for example. For example, the teaching unit can input the user's study frequency data into a generation AI and have the generation AI weight the lessons.

[0098] The lesson unit can estimate the user's emotions and adjust the lesson display method based on the estimated user emotions. For example, if the user is feeling stressed, the lesson unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the lesson unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the lesson unit can provide a display method that focuses on the main points. This improves the visibility of the lesson by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the lesson unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the lesson unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0099] The teaching unit can conduct lessons based on the geographical distribution of users as the lessons progress. For example, the teaching unit can adjust the lesson content based on the user's place of residence to meet regional needs. The teaching unit can also provide optimal lesson content by taking the users' geographical distribution into consideration. The teaching unit can also provide lessons that suit the characteristics of each region based on the users' geographical distribution. In this way, lessons can be conducted based on the users' geographical distribution to provide lessons that meet regional needs. Some or all of the above-mentioned processing in the teaching unit may be performed using, for example, AI, or may be performed without using AI. For example, the teaching unit can input users' geographical distribution data into a generation AI and have the generation AI adjust the lesson content.

[0100] The teaching unit can refer to related literature as the lesson progresses to improve the accuracy of the lesson. For example, the teaching unit can refer to related literature to improve the reliability of the lesson content. The teaching unit can also improve the teaching method and improve the accuracy based on the related literature. The teaching unit can also refer to related literature to complement the lesson content. In this way, the accuracy of the lesson is improved by referring to related literature. Some or all of the above-mentioned processing in the teaching unit may be performed using AI, for example, or may be performed without using AI. For example, the teaching unit can input related literature data into a generation AI and have the generation AI improve the accuracy of the lesson.

[0101] The teaching unit can conduct lessons based on the user's market value as the lesson progresses. The teaching unit can adjust the lesson content based on the user's occupation and income, for example, and evaluate the market value. The teaching unit can also provide optimal lesson content taking the user's market value into consideration. The teaching unit can also evaluate the reliability of the lesson content based on the user's market value. As a result, by conducting lessons based on the user's market value, the reliability of the lesson content is improved. Some or all of the above-mentioned processing in the teaching unit can be performed, for example, using AI, or can be performed without using AI. For example, the teaching unit can input the user's market value data into a generation AI and have the generation AI adjust the lesson content.

[0102] The monitoring unit can estimate the user's emotions and adjust the progress of the lesson based on the estimated user's emotions. For example, if the user is feeling stressed, the monitoring unit can slow down the progress of the lesson to allow the user to relax. The monitoring unit can also smoothly proceed with the lesson if the user is relaxed. The monitoring unit can also speed up the progress of the lesson if the user is in a hurry. This allows the progress of the lesson to be adjusted according to the user's emotions, thereby providing a more appropriate lesson. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit can be performed using an AI, for example, or without an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the progress.

[0103] When monitoring the progress of a class, the monitoring unit can improve the accuracy of the monitoring by referring to the user's past learning history. The monitoring unit monitors the progress of a class, for example, based on the user's past learning history. The monitoring unit can also improve the accuracy of the monitoring by referring to the user's past learning history. The monitoring unit can also analyze the user's past learning history and provide the most effective monitoring method. By referring to the past learning history, the accuracy of the monitoring is improved. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past learning history data into the generation AI and cause the generation AI to improve the accuracy of the monitoring.

[0104] When monitoring the progress of a class, the monitoring unit can perform monitoring based on the user's attribute information. For example, the monitoring unit can monitor the progress of a class based on the user's age and perform monitoring appropriate for the age group. The monitoring unit can also monitor the progress of a class based on the user's gender and perform monitoring appropriate for the gender. The monitoring unit can also provide an optimal monitoring method by taking the user's attribute information into consideration. This allows for more appropriate monitoring by performing monitoring based on the user's attribute information. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input the user's attribute information data into the generation AI and have the generation AI perform monitoring.

[0105] When monitoring the progress of a lesson, the monitoring unit can weight the monitoring based on the user's learning frequency. For example, if the user studies frequently, the monitoring unit can perform detailed monitoring. Also, if the user studies infrequently, the monitoring unit can perform brief monitoring. The monitoring unit can also adjust the monitoring method based on the user's learning frequency to maximize the learning effect. Thus, more appropriate monitoring can be performed by weighting the monitoring based on the user's learning frequency. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's learning frequency data into the generation AI and have the generation AI perform the monitoring weighting.

[0106] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the monitoring unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can provide a display method that focuses on the main points. This improves the visibility of the monitoring results by adjusting the display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit can be performed using, for example, an AI, or without an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0107] When monitoring the progress of a class, the monitoring unit can perform monitoring based on the geographical distribution of users. For example, the monitoring unit can monitor the progress of a class based on the user's place of residence, thereby meeting regional needs. The monitoring unit can also provide an optimal monitoring method by taking the geographical distribution of users into consideration. The monitoring unit can also perform monitoring that suits the characteristics of each region based on the geographical distribution of users. In this way, monitoring based on the geographical distribution of users can meet regional needs. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input user geographical distribution data into a generation AI and have the generation AI perform monitoring.

[0108] When monitoring the progress of a class, the monitoring unit can improve the accuracy of the monitoring by referring to related literature. For example, the monitoring unit can refer to related literature and improve the monitoring method to improve accuracy. The monitoring unit can also optimize the monitoring method based on the related literature and provide an optimal monitoring method. The monitoring unit can also improve the reliability of the monitoring results by referring to related literature. In this way, the accuracy of monitoring is improved by referring to related literature. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input related literature data into the generation AI and have the generation AI improve the accuracy of monitoring.

[0109] When monitoring the progress of a class, the monitoring unit can perform monitoring based on the market value of the user. The monitoring unit monitors the progress of a class and evaluates the market value, for example, based on the user's occupation and income. The monitoring unit can also provide an optimal monitoring method taking the user's market value into consideration. The monitoring unit can also evaluate the reliability of the monitoring results based on the user's market value. As a result, monitoring based on the user's market value improves the reliability of the monitoring results. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's market value data into a generation AI and have the generation AI perform monitoring. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, selection unit, teaching unit, and monitoring unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the selection unit is realized by the specific processing unit 290 of the data processing device 12. For example, the teaching unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, selection unit, teaching unit, and monitoring unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the selection unit is realized by the specific processing unit 290 of the data processing device 12. For example, the teaching unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, selection unit, teaching unit, and monitoring unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the selection unit is realized by the specific processing unit 290 of the data processing device 12. For example, the teaching unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, selection unit, teaching unit, and monitoring unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the selection unit is realized by the specific processing unit 290 of the data processing device 12. For example, the teaching unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the monitoring unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The reception unit can also monitor the user's health condition and adjust the questions in the personality questionnaire based on the user's health condition. For example, if the user is tired, the questions can be simplified and changed to a format that is easier to answer. If the user is healthy, more detailed questions can be included to conduct an in-depth personality analysis. Furthermore, if the user is ill, the number of questions can be reduced and focused on important questions. This allows the personality questionnaire to be conducted more appropriately by adjusting the questions according to the user's health condition.

[0112] The analysis unit can also analyze the user's hobbies and interests and adjust the data analysis method for the personality questionnaire based on the analysis results. For example, if the user is interested in sports, questions related to sports can be analyzed preferentially. If the user is interested in music, questions related to music can be analyzed in detail. Furthermore, if the user is interested in reading, questions related to reading can be analyzed in depth. In this way, adjusting the data analysis method according to the user's hobbies and interests improves the analysis accuracy of the personality questionnaire.

[0113] The selection unit can also analyze the user's learning style and adjust the selection criteria for the optimal AI teacher based on the analysis results. For example, if the user has a visual learning style, it can select an AI teacher who uses a lot of visual learning materials. If the user has an auditory learning style, it can select an AI teacher who uses a lot of audio learning materials. Furthermore, if the user has an experiential learning style, it can select an AI teacher who gives practical lessons. In this way, by adjusting the selection criteria according to the user's learning style, it is possible to select a more appropriate AI teacher.

[0114] The lesson module can also set the user's learning goals and adjust the lesson content based on the set learning goals. For example, if the user wants to acquire a specific skill in a short period of time, lesson content specialized for that skill can be provided. Also, if the user has a long-term learning goal, lesson content for acquiring the skill in stages can be provided. Furthermore, if the user wants to pass a specific exam, lesson content corresponding to that exam can be provided. In this way, more effective lessons can be provided by adjusting the lesson content according to the user's learning goals.

[0115] The monitoring unit can also monitor the user's learning environment and adjust the progress of the lesson based on the learning environment. For example, if the user is learning in a quiet environment, the lesson can be conducted to improve concentration. If the user is learning in a noisy environment, the lesson can be conducted in a short period of time to cover the main points. Furthermore, if the user is learning while on the move, the lesson can be conducted in a concise and highly visible manner. This allows the progress of the lesson to be adjusted according to the user's learning environment, making it possible to provide more appropriate lessons.

[0116] The reception unit can estimate the user's emotions and adjust the response method for the personality questionnaire based on the estimated user emotions. For example, if the user is feeling stressed, a simple response method is provided to reduce the burden of answering. Furthermore, if the user is relaxed, the reception unit can provide a detailed response method to perform an in-depth personality analysis. Furthermore, if the user is in a hurry, the reception unit can simplify the response method so that the user can answer in a short amount of time. In this way, by adjusting the response method according to the user's emotions, a more appropriate personality questionnaire can be conducted.

[0117] The analysis unit can estimate the user's emotions and adjust the data analysis method of the personality questionnaire based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can perform analysis using a simple data analysis method. If the user is relaxed, the analysis unit can also perform analysis using a detailed data analysis method. If the user is in a hurry, the analysis unit can also use a simplified data analysis method to perform analysis quickly. In this way, by adjusting the data analysis method according to the user's emotions, the analysis accuracy of the personality questionnaire can be improved.

[0118] The selection unit can estimate the user's emotions and adjust the selection criteria for the optimal AI teacher based on the estimated user emotions. For example, if the user is feeling stressed, it can select an AI teacher who helps the user relax. The selection unit can also select an AI teacher who provides detailed explanations if the user is relaxed. The selection unit can also select an AI teacher who can quickly proceed with the lesson if the user is in a hurry. In this way, by adjusting the selection criteria according to the user's emotions, it is possible to select a more appropriate AI teacher.

[0119] The lesson module can estimate the user's emotions and adjust the lesson content based on the estimated user's emotions. For example, if the user is feeling stressed, the lesson module can provide relaxing content. If the user is relaxed, the lesson module can also provide content with detailed explanations. If the user is in a hurry, the lesson module can also provide content that focuses on the main points. In this way, by adjusting the lesson content according to the user's emotions, more appropriate lessons can be provided.

[0120] The monitoring unit can estimate the user's emotions and adjust the progress of the lesson based on the estimated user's emotions. For example, if the user is feeling stressed, the progress of the lesson can be slowed down to allow the user to relax. The monitoring unit can also smoothly proceed with the lesson if the user is relaxed. The monitoring unit can also speed up the lesson if the user is in a hurry. In this way, by adjusting the progress of the lesson according to the user's emotions, it is possible to provide more appropriate lessons.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The reception unit receives a personality questionnaire from a user. The personality questionnaire includes, but is not limited to, the types of questions and the answer formats. The reception unit receives the personality questionnaire data entered by the user. The reception unit can also estimate the user's emotions and adjust the content of the personality questionnaire questions based on the estimated emotions. Step 2: The analysis unit analyzes the personality questionnaire data received by the reception unit. The analysis is performed based on, but not limited to, the algorithm used and the purpose of the analysis. The analysis unit analyzes the personality questionnaire data and identifies the user's personality. The analysis unit can also estimate the user's emotions and adjust the personality questionnaire data analysis method based on the estimated emotions. Step 3: The selection unit selects the optimal AI teacher based on the data analyzed by the analysis unit. The selection is based on, but not limited to, the user's personality and preferences. The selection unit selects the AI ​​teacher that best suits the user's personality. The selection unit can also estimate the user's emotions and adjust the selection criteria based on the estimated emotions. Step 4: The teaching department will have the AI ​​teacher selected by the selection department conduct the lesson. The lesson will be based on, but not limited to, the lesson progress method and the teaching materials used. The teaching department will adjust the lesson content according to the user's level of understanding. The teaching department can also estimate the user's emotions and adjust the lesson content based on the estimated emotions. Step 5: The monitoring unit monitors the progress of the lesson conducted by the teaching unit in real time and adjusts as necessary. The monitoring is performed based on, but not limited to, the items to be monitored and the real-time adjustment method. The monitoring unit monitors the progress of the lesson and adjusts the progress of the lesson as necessary. The monitoring unit can also estimate the user's emotions and adjust the progress of the lesson based on the estimated emotions.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] 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.

[0186] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0187] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0188] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a personality questionnaire from a user; an analysis unit that analyzes the personality questionnaire data received by the reception unit; A selection unit that selects an AI teacher based on the data analyzed by the analysis unit; A teaching department in which the AI ​​teacher selected by the selection department teaches a class; a monitoring unit that monitors the progress of the lesson conducted by the teaching unit in real time and adjusts it as necessary. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the content of personality questionnaire questions based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit When accepting personality questionnaires, adjust the order of questions by referring to the user's past response history.

2. The system of claim 1.

4. The reception unit When accepting personality surveys, customize the questions based on the user's current situation (time of day, location, etc.) 2. The system of claim 1.

5. The reception unit When accepting a personality questionnaire, select the acceptance method according to the user's input method.

2. The system of claim 1.

6. The reception unit Estimating a user's emotions and adjusting the way they answer a personality questionnaire based on the estimated user's emotions 2. The system of claim 1.

7. The reception unit When taking personality surveys, the system prioritizes relevant questions based on the user's geographic location.

2. The system of claim 1.

8. The reception unit When accepting personality surveys, analyze users' social media activity and present relevant questions 2. The system of claim 1.

Images