System

Abstract

An object of the system according to the embodiment is to make it possible to freely select a class in a specialized field according to an interest or a learning need of a student.SOLUTION: A system includes an interest analysis unit, a lesson generation unit, and a proposal unit. The interest analysis unit analyzes student's interests and learning needs. A lesson generation part generates lesson contents of the special field on the basis of a result analyzed by the interest analysis part. A proposal part proposes the lesson contents generated by the lesson generation part to the student so that the student can freely select the lesson contents.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 does not adequately provide a system that allows students to freely select specialized classes that suit their interests and learning needs, and there is room for improvement.

[0005] The system according to the embodiment aims to enable students to freely select lessons in specialized fields that suit their interests and learning needs. [Means for solving the problem]

[0006] The system according to the embodiment includes an interest analysis unit, a lesson generation unit, and a suggestion unit. The interest analysis unit analyzes students' interests and learning needs. The lesson generation unit generates lesson content in a specialized field based on the results of the analysis by the interest analysis unit. The suggestion unit suggests the lesson content generated by the lesson generation unit to students, allowing them to freely select from the content. [Effects of the Invention]

[0007] The system according to the embodiment allows students to freely select lessons in specialized fields that suit their interests and learning needs. [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 content production system according to an embodiment of the present invention produces content that allows students to freely select specialized classes approximately once to three times a week. This system uses a generation AI to propose and generate content for specialized classes, such as language, music, and filmmaking, based on the student's interests and learning needs. This allows students to freely select specialized classes according to their interests and learning needs, allowing them to efficiently progress through their studies.

[0029] A content production system according to an embodiment includes an interest analysis unit, a lesson generation unit, and a suggestion unit. The interest analysis unit analyzes students' interests and learning needs. For example, the interest analysis unit collects and analyzes data such as classes students have taken in the past, topics they have shown interest in, and their learning progress. The interest analysis unit also performs analysis based on prompts generated by a generation AI containing information about students' interests and learning needs. The lesson generation unit generates lesson content in specialized fields based on the results of the analysis by the interest analysis unit. For example, the lesson generation unit generates teaching materials and exercises tailored to the students' level for language classes. For music classes, the lesson generation unit can also generate content related to instrument playing techniques and music theory. For filmmaking classes, the lesson generation unit can also provide teaching materials related to scriptwriting and filming techniques. The suggestion unit suggests lesson content generated by the lesson generation unit to students, allowing them to freely select from among them. For example, the suggestion unit allows the generation AI to suggest multiple lessons as "recommended lessons of the week," and students can select the lesson they are interested in from among them. The suggestion unit customizes the lessons selected by the student using the generation AI and provides individually optimized content. As a result, the content production system according to the embodiment allows students to freely select lessons in specialized fields according to their interests and learning needs and efficiently progress through their studies. For example, a student who selects a language class can effectively progress through their studies using customized teaching materials provided by the generation AI. A student who selects a music class can improve their skills by utilizing content related to performance techniques and music theory provided by the generation AI. A student who selects a filmmaking class can acquire practical skills using teaching materials related to scriptwriting and filming techniques provided by the generation AI.

[0030] The interest analysis unit analyzes students' social media and online activity history to gain a more detailed understanding of their interests and learning needs. For example, the interest analysis unit analyzes the content of students' social media posts to identify topics and activities in which they are interested. For example, it extracts frequently posted keywords and hashtags to understand interest trends. The interest analysis unit also analyzes the activity history of online communities and forums in which students participate to identify areas of interest. For example, it analyzes the frequency of comments and posts on specific topics. The interest analysis unit also analyzes students' online search history to understand their learning needs. For example, if a student frequently searches for a specific learning resource or teaching material, it determines that the student has a high interest in that field. This allows for a more detailed understanding of students' interests and learning needs.

[0031] The interest analysis unit can collect students' physiological data and analyze their concentration and stress levels while studying. For example, the interest analysis unit collects students' heart rate data and analyzes their concentration and stress levels while studying. For example, it identifies time periods when concentration is high based on fluctuations in heart rate. The interest analysis unit also analyzes students' brain wave data to understand their brain activity while studying. For example, it evaluates their concentration and relaxation levels based on fluctuations in alpha waves and beta waves. The interest analysis unit also monitors students' physiological data in real time to optimize the learning environment. For example, it suggests taking a break if their stress level is high. This makes it possible to analyze students' concentration and stress levels.

[0032] The interest analysis unit can collect the opinions of a student's family and friends and analyze their interests and learning needs while taking social influences into account. For example, the interest analysis unit can conduct a survey of a student's family and friends to collect opinions on the student's interests and learning needs. For example, it can identify the learning fields recommended by family members and the topics that friends are commonly interested in. The interest analysis unit can also analyze the content of conversations between a student and family members to identify their interests and learning needs while taking social influences into account. For example, it can analyze topics that frequently come up in conversations with family members. The interest analysis unit can also estimate a student's learning needs based on the learning history and interests of the student's family and friends. For example, it can determine that a student is likely to be interested in the fields that their family members are specializing in. This makes it possible to analyze interests and learning needs while taking social influences into account.

[0033] The interest analysis unit can collect data related to students' hobbies and special skills and customize lesson content based on that. The interest analysis unit, for example, collects data related to students' hobbies and special skills and customizes lesson content based on that. For example, it suggests music theory or instrument playing lessons to a student whose hobby is music. The interest analysis unit also analyzes data related to a student's special skills and generates lesson content that makes use of those skills. For example, it suggests art or design lessons to a student who is good at painting. The interest analysis unit also builds a system that individually optimizes lesson content based on a student's hobbies and special skills. For example, it suggests sports science or training classes to a student who likes sports. This makes it possible to customize lesson content based on a student's hobbies and special skills.

[0034] The lesson generation unit can generate lesson content that matches the learning style of the student. For example, the lesson generation unit analyzes the student's learning style and provides visual learning materials to students who prefer visual learning. For example, it generates lesson content that uses diagrams or videos. The lesson generation unit also provides audio learning materials or podcast-style lesson content to students who prefer auditory learning. For example, it generates lessons with audio commentary or interview format. The lesson generation unit also provides practical exercises or hands-on lesson content to students who prefer tactile learning. For example, it generates experiment- or project-based lessons. In this way, lesson content can be generated that matches the learning style of the student.

[0035] The lesson generation unit can generate specialized teaching materials to reinforce individual weak points based on the student's past learning data. The lesson generation unit, for example, analyzes the student's past test results and assignment submission status to identify weak points. For example, if the student has a low level of understanding of a particular topic, it generates teaching materials specialized for that topic. The lesson generation unit also provides practice questions and exercises to reinforce individual weak points based on the student's learning history. For example, it generates additional practice questions for weak areas. The lesson generation unit also analyzes the student's learning data and provides customized lesson content to reinforce individual weak points. For example, it generates specialized teaching materials to improve specific skills. In this way, specialized teaching materials can be generated to reinforce individual weak points of the student.

[0036] The lesson generation unit can generate cross-disciplinary lesson content that combines different fields of expertise. The lesson generation unit generates, for example, lesson content that combines different fields of expertise. For example, in a lesson that combines music and mathematics, music theory is analyzed mathematically. The lesson generation unit also provides cross-disciplinary lesson content to arouse students' interest. For example, in a lesson that combines filmmaking and history, students learn about the production process of historical films. The lesson generation unit also generates lesson content that combines different fields of expertise to enrich students' learning experience. For example, in a lesson that combines language and culture, students learn about a language and its background culture at the same time. In this way, cross-disciplinary lesson content that combines different fields of expertise can be generated.

[0037] The lesson generation unit can generate practical project-based lesson content so that students can acquire skills that will be useful in their future careers. The lesson generation unit, for example, provides practical project-based lesson content so that students can acquire skills that will be useful in their future careers. For example, in a programming class, students may undertake a project to develop an actual application. The lesson generation unit also generates project-based lessons for acquiring skills that are directly related to careers. For example, in a business class, students may undertake a project to create an actual business plan. The lesson generation unit also provides project-based lesson content so that students can acquire practical skills. For example, in a design class, students may undertake a project to design an actual product. In this way, practical project-based lesson content can be generated so that students can acquire skills that will be useful in their future careers.

[0038] The suggestion unit can analyze a student's past selection history and develop the most effective lesson suggestion algorithm. The suggestion unit, for example, analyzes a student's past lesson selection history and develops the most effective lesson suggestion algorithm. For example, suggestions are made based on the evaluations and results of lessons selected in the past. The suggestion unit also builds an algorithm that makes effective lesson suggestions based on the student's selection history. For example, it analyzes the progress and level of understanding of the selected lesson and suggests the next lesson. The suggestion unit also analyzes the student's past selection history and develops an algorithm that suggests the most suitable lesson for the student. For example, it makes suggestions based on the content and format of the selected lesson. In this way, the student's past selection history can be analyzed and the most effective lesson suggestion algorithm can be developed.

[0039] The suggestion unit can propose long-term study plans based on the student's learning goals and future plans. For example, the suggestion unit proposes long-term study plans based on the student's learning goals and future plans. For example, for a student aiming to obtain a specific qualification, the suggestion unit provides a lesson plan including steps to achieve that qualification. The suggestion unit also proposes study plans that match the student's future career goals. For example, for a student aiming to become an engineer, the suggestion unit provides a plan that incorporates programming and mathematics classes. The suggestion unit also proposes long-term study plans that allow the student to acquire skills in stages based on the student's learning goals. For example, for language classes, the suggestion unit provides a plan that includes steps from beginner to advanced. This makes it possible to propose long-term study plans based on the student's learning goals and future plans.

[0040] The suggestion unit can make suggestions for group learning so that students can select classes in collaboration with other students. The suggestion unit, for example, makes suggestions for group learning so that students can select classes in collaboration with other students. For example, it matches students with the same interests and allows them to select classes in collaboration. The suggestion unit also makes suggestions for group learning so that students can advance their studies in cooperation with other students. For example, it proposes project-based classes and has them work on them in groups. The suggestion unit also builds a system for students to select classes in collaboration with other students and advance their studies. For example, it proposes classes that include group discussions and joint assignments. This makes it possible to make suggestions for group learning so that students can select classes in collaboration with other students.

[0041] The suggestion unit can collect feedback on the lessons selected by students and reflect it in the next suggestion. For example, the suggestion unit collects feedback on the lessons selected by students and reflects it in the next lesson suggestion. For example, it adjusts the content of the suggestion based on the lesson evaluations and comments. The suggestion unit also builds a system that optimizes the next lesson suggestion based on the feedback. For example, it prioritizes suggesting lesson content that has received high ratings. The suggestion unit also collects student feedback in real time and reflects it in the next lesson suggestion. For example, it conducts a survey after the lesson ends and makes suggestions based on the results. In this way, it is possible to collect feedback on the lessons selected by students and reflect it in the next suggestion.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The interest analysis unit collects data related to students' hobbies and special skills and can customize lesson content based on that. For example, it can suggest music theory or instrument playing classes to a student whose hobby is music. The interest analysis unit also analyzes data related to a student's special skills and generates lesson content that makes use of those skills. For example, it can suggest art or design classes to a student who is good at painting. The interest analysis unit also builds a system that individually optimizes lesson content based on a student's hobbies and special skills. For example, it can suggest sports science or training classes to a student who likes sports. This makes it possible to customize lesson content based on a student's hobbies and special skills.

[0044] The interest analysis unit can collect students' physiological data and analyze their concentration and stress levels while studying. For example, it can collect students' heart rate data and analyze their concentration and stress levels while studying. For example, it can identify times of high concentration based on heart rate fluctuations. The interest analysis unit can also analyze students' brain wave data to understand their brain activity while studying. For example, it can evaluate their concentration and relaxation levels based on fluctuations in alpha and beta waves. The interest analysis unit can also monitor students' physiological data in real time to optimize the learning environment. For example, it can suggest taking a break if their stress level is high. This makes it possible to analyze students' concentration and stress levels.

[0045] The interest analysis unit can collect the opinions of a student's family and friends and analyze their interests and learning needs while taking social influences into account. For example, it can conduct a survey of a student's family and friends to collect their opinions on the student's interests and learning needs. For example, it can identify the learning fields recommended by family members and the topics that friends are commonly interested in. The interest analysis unit can also analyze the content of conversations between a student and family members to identify their interests and learning needs while taking social influences into account. For example, it can analyze topics that frequently come up in conversations with family members. The interest analysis unit can also estimate a student's learning needs based on the learning history and interests of the student's family and friends. For example, it can determine that a student is likely to be interested in the fields that their family members are specializing in. This makes it possible to analyze interests and learning needs while taking social influences into account.

[0046] The interest analysis unit analyzes students' social media and online activity history to gain a more detailed understanding of their interests and learning needs. For example, it analyzes the content of students' social media posts to identify topics and activities that interest them. For example, it extracts frequently posted keywords and hashtags to understand interest trends. The interest analysis unit also analyzes the activity history of online communities and forums in which students participate to identify areas of interest. For example, it analyzes the frequency of comments and posts on specific topics. The interest analysis unit also analyzes students' online search history to understand their learning needs. For example, if a student frequently searches for a specific learning resource or teaching material, it can determine that the student has a high interest in that field. This allows for a more detailed understanding of students' interests and learning needs.

[0047] The lesson generation unit can generate lesson content that matches the learning style of the student. For example, it can analyze the student's learning style and provide visual learning materials to students who prefer visual learning. For example, it can generate lesson content using diagrams or videos. The lesson generation unit can also provide audio learning materials or podcast-style lesson content to students who prefer auditory learning. For example, it can generate lessons with audio commentary or interview format. The lesson generation unit can also provide practical exercises or hands-on lesson content to students who prefer tactile learning. For example, it can generate experiment- or project-based lessons. This makes it possible to generate lesson content that matches the learning style of the student.

[0048] The lesson generation unit can generate specialized teaching materials to reinforce individual weaknesses based on the student's past learning data. For example, it can analyze the student's past test results and assignment submission status to identify weaknesses. For example, if the student has a low level of understanding of a particular topic, it can generate teaching materials specialized for that topic. The lesson generation unit also provides practice questions and exercises to reinforce individual weaknesses based on the student's learning history. For example, it generates additional practice questions for weak areas. The lesson generation unit also analyzes the student's learning data and provides customized lesson content to reinforce individual weaknesses. For example, it generates specialized teaching materials to improve specific skills. This makes it possible to generate specialized teaching materials to reinforce individual weaknesses of the student.

[0049] The lesson generation unit can generate cross-disciplinary lesson content that combines different fields of expertise. For example, a lesson that combines music and mathematics involves mathematically analyzing music theory. The lesson generation unit also provides cross-disciplinary lesson content to stimulate students' interest. For example, a lesson that combines filmmaking and history allows students to learn about the production process of historical films. The lesson generation unit also generates lesson content that blends different fields of expertise to enrich students' learning experiences. For example, a lesson that combines language and culture allows students to learn about a language and its background culture at the same time. This makes it possible to generate cross-disciplinary lesson content that combines different fields of expertise.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The interest analysis unit analyzes the student's interests and learning needs. For example, the interest analysis unit collects and analyzes data such as the classes the student has taken in the past, topics they have shown interest in, and their learning progress. The interest analysis unit also performs analysis based on prompts generated by the generation AI that contain information about the student's interests and learning needs. Step 2: The lesson generation unit generates lesson content for specialized fields based on the results of the analysis by the interest analysis unit. For example, in a language class, the lesson generation unit's generation AI creates teaching materials and exercises tailored to the student's level. In a music class, the lesson generation unit's generation AI can also generate content related to how to play an instrument or music theory. In a filmmaking class, the lesson generation unit's generation AI can also provide teaching materials on scriptwriting and filming techniques. Step 3: The suggestion unit proposes the lesson content generated by the lesson generation unit to the student, allowing the student to freely select. For example, the suggestion unit uses the generation AI to suggest multiple lessons as "recommended lessons of the week," and the student selects the lesson that interests them. The suggestion unit then customizes the lesson selected by the student using the generation AI, providing individually optimized content.

[0052] (Example 2) A content production system according to an embodiment of the present invention produces content that allows students to freely select specialized classes approximately once to three times a week. This system uses a generation AI to propose and generate content for specialized classes, such as language, music, and filmmaking, based on the student's interests and learning needs. This allows students to freely select specialized classes according to their interests and learning needs, allowing them to efficiently progress through their studies.

[0053] A content production system according to an embodiment includes an interest analysis unit, a lesson generation unit, and a suggestion unit. The interest analysis unit analyzes students' interests and learning needs. For example, the interest analysis unit collects and analyzes data such as classes students have taken in the past, topics they have shown interest in, and their learning progress. The interest analysis unit also performs analysis based on prompts generated by a generation AI containing information about students' interests and learning needs. The lesson generation unit generates lesson content in specialized fields based on the results of the analysis by the interest analysis unit. For example, the lesson generation unit generates teaching materials and exercises tailored to the students' level for language classes. For music classes, the lesson generation unit can also generate content related to instrument playing techniques and music theory. For filmmaking classes, the lesson generation unit can also provide teaching materials related to scriptwriting and filming techniques. The suggestion unit suggests lesson content generated by the lesson generation unit to students, allowing them to freely select from among them. For example, the suggestion unit allows the generation AI to suggest multiple lessons as "recommended lessons of the week," and students can select the lesson they are interested in from among them. The suggestion unit customizes the lessons selected by the student using the generation AI and provides individually optimized content. As a result, the content production system according to the embodiment allows students to freely select lessons in specialized fields according to their interests and learning needs and efficiently progress through their studies. For example, a student who selects a language class can effectively progress through their studies using customized teaching materials provided by the generation AI. A student who selects a music class can improve their skills by utilizing content related to performance techniques and music theory provided by the generation AI. A student who selects a filmmaking class can acquire practical skills using teaching materials related to scriptwriting and filming techniques provided by the generation AI.

[0054] The interest analysis unit analyzes students' social media and online activity history to gain a more detailed understanding of their interests and learning needs. For example, the interest analysis unit analyzes the content of students' social media posts to identify topics and activities in which they are interested. For example, it extracts frequently posted keywords and hashtags to understand interest trends. The interest analysis unit also analyzes the activity history of online communities and forums in which students participate to identify areas of interest. For example, it analyzes the frequency of comments and posts on specific topics. The interest analysis unit also analyzes students' online search history to understand their learning needs. For example, if a student frequently searches for a specific learning resource or teaching material, it determines that the student has a high interest in that field. This allows for a more detailed understanding of students' interests and learning needs.

[0055] The interest analysis unit can collect students' physiological data and analyze their concentration and stress levels while studying. For example, the interest analysis unit collects students' heart rate data and analyzes their concentration and stress levels while studying. For example, it identifies time periods when concentration is high based on fluctuations in heart rate. The interest analysis unit also analyzes students' brain wave data to understand their brain activity while studying. For example, it evaluates their concentration and relaxation levels based on fluctuations in alpha waves and beta waves. The interest analysis unit also monitors students' physiological data in real time to optimize the learning environment. For example, it suggests taking a break if their stress level is high. This makes it possible to analyze students' concentration and stress levels.

[0056] The interest analysis unit uses the emotion estimation function to analyze the emotions felt by students during class and can suggest lesson content that will elicit positive emotions. The interest analysis unit, for example, analyzes students' facial expressions during class and estimates their emotions. For example, it detects smiling or surprised expressions and suggests lesson content that will elicit positive emotions. The interest analysis unit also analyzes students' voice data and estimates their emotions. For example, it evaluates emotions based on changes in voice tone and speaking style and adjusts the lesson content. The interest analysis unit also collects students' emotion data and generates lesson content that will elicit positive emotions. For example, it structures lessons around topics with high emotion scores. This makes it possible to suggest lesson content that will elicit positive emotions in students.

[0057] The interest analysis unit can collect the opinions of a student's family and friends and analyze their interests and learning needs while taking social influences into account. For example, the interest analysis unit can conduct a survey of a student's family and friends to collect opinions on the student's interests and learning needs. For example, it can identify the learning fields recommended by family members and the topics that friends are commonly interested in. The interest analysis unit can also analyze the content of conversations between a student and family members to identify their interests and learning needs while taking social influences into account. For example, it can analyze topics that frequently come up in conversations with family members. The interest analysis unit can also estimate a student's learning needs based on the learning history and interests of the student's family and friends. For example, it can determine that a student is likely to be interested in the fields that their family members are specializing in. This makes it possible to analyze interests and learning needs while taking social influences into account.

[0058] The interest analysis unit can collect data related to students' hobbies and special skills and customize lesson content based on that. The interest analysis unit, for example, collects data related to students' hobbies and special skills and customizes lesson content based on that. For example, it suggests music theory or instrument playing lessons to a student whose hobby is music. The interest analysis unit also analyzes data related to a student's special skills and generates lesson content that makes use of those skills. For example, it suggests art or design lessons to a student who is good at painting. The interest analysis unit also builds a system that individually optimizes lesson content based on a student's hobbies and special skills. For example, it suggests sports science or training classes to a student who likes sports. This makes it possible to customize lesson content based on a student's hobbies and special skills.

[0059] The interest analysis unit uses the emotion estimation function to analyze the expectations and anxieties students feel when selecting classes, and can suggest the most suitable classes. For example, the interest analysis unit uses the emotion estimation function to analyze the expectations and anxieties students feel when selecting classes, and can suggest the most suitable classes. For example, it prioritizes suggesting classes that students have high expectations for. The interest analysis unit also makes suggestions to reduce anxiety when selecting classes based on students' emotion data. For example, it prioritizes suggesting class content that causes less anxiety. The interest analysis unit also uses the emotion estimation function to analyze the emotions students feel when selecting classes in real time, and can suggest the most suitable classes. For example, it can suggest classes that cause strong positive emotions. In this way, it is possible to analyze students' expectations and anxieties, and suggest the most suitable classes.

[0060] The lesson generation unit can generate lesson content that matches the learning style of the student. For example, the lesson generation unit analyzes the student's learning style and provides visual learning materials to students who prefer visual learning. For example, it generates lesson content that uses diagrams or videos. The lesson generation unit also provides audio learning materials or podcast-style lesson content to students who prefer auditory learning. For example, it generates lessons with audio commentary or interview format. The lesson generation unit also provides practical exercises or hands-on lesson content to students who prefer tactile learning. For example, it generates experiment- or project-based lessons. In this way, lesson content can be generated that matches the learning style of the student.

[0061] The lesson generation unit can generate specialized teaching materials to reinforce individual weak points based on the student's past learning data. The lesson generation unit, for example, analyzes the student's past test results and assignment submission status to identify weak points. For example, if the student has a low level of understanding of a particular topic, it generates teaching materials specialized for that topic. The lesson generation unit also provides practice questions and exercises to reinforce individual weak points based on the student's learning history. For example, it generates additional practice questions for weak areas. The lesson generation unit also analyzes the student's learning data and provides customized lesson content to reinforce individual weak points. For example, it generates specialized teaching materials to improve specific skills. In this way, specialized teaching materials can be generated to reinforce individual weak points of the student.

[0062] The lesson generation unit can use the emotion estimation function to generate lesson content centered on topics that students are most interested in. For example, the lesson generation unit analyzes students' emotion data to identify the topics that students are most interested in. For example, it generates lesson content centered on topics with high emotion scores. The lesson generation unit also uses the emotion estimation function to provide lesson content related to topics that students are interested in. For example, it organizes lessons centered on topics that evoke strong positive emotions. The lesson generation unit also analyzes students' emotional responses in real time to generate lesson content based on topics that students are interested in. For example, it prioritizes incorporating topics with high emotion scores into lessons. This makes it possible to generate lesson content centered on topics that students are most interested in.

[0063] The lesson generation unit can generate cross-disciplinary lesson content that combines different fields of expertise. The lesson generation unit generates, for example, lesson content that combines different fields of expertise. For example, in a lesson that combines music and mathematics, music theory is analyzed mathematically. The lesson generation unit also provides cross-disciplinary lesson content to arouse students' interest. For example, in a lesson that combines filmmaking and history, students learn about the production process of historical films. The lesson generation unit also generates lesson content that combines different fields of expertise to enrich students' learning experience. For example, in a lesson that combines language and culture, students learn about a language and its background culture at the same time. In this way, cross-disciplinary lesson content that combines different fields of expertise can be generated.

[0064] The lesson generation unit can generate practical project-based lesson content so that students can acquire skills that will be useful in their future careers. The lesson generation unit, for example, provides practical project-based lesson content so that students can acquire skills that will be useful in their future careers. For example, in a programming class, students may undertake a project to develop an actual application. The lesson generation unit also generates project-based lessons for acquiring skills that are directly related to careers. For example, in a business class, students may undertake a project to create an actual business plan. The lesson generation unit also provides project-based lesson content so that students can acquire practical skills. For example, in a design class, students may undertake a project to design an actual product. In this way, practical project-based lesson content can be generated so that students can acquire skills that will be useful in their future careers.

[0065] The lesson generation unit can use the emotion estimation function to generate interactive content to increase the motivation felt by students during lessons. The lesson generation unit, for example, uses the emotion estimation function to provide interactive content to increase the motivation felt by students during lessons. For example, it generates lesson content that incorporates game elements. The lesson generation unit also generates interactive content to increase motivation based on students' emotion data. For example, it generates lesson content that provides feedback in real time. The lesson generation unit also uses the emotion estimation function to provide interactive lesson content to increase students' motivation. For example, it generates lesson content in the form of quizzes or challenges. This makes it possible to generate interactive content to increase the motivation felt by students during lessons.

[0066] The suggestion unit can analyze a student's past selection history and develop the most effective lesson suggestion algorithm. The suggestion unit, for example, analyzes a student's past lesson selection history and develops the most effective lesson suggestion algorithm. For example, suggestions are made based on the evaluations and results of lessons selected in the past. The suggestion unit also builds an algorithm that makes effective lesson suggestions based on the student's selection history. For example, it analyzes the progress and level of understanding of the selected lesson and suggests the next lesson. The suggestion unit also analyzes the student's past selection history and develops an algorithm that suggests the most suitable lesson for the student. For example, it makes suggestions based on the content and format of the selected lesson. In this way, the student's past selection history can be analyzed and the most effective lesson suggestion algorithm can be developed.

[0067] The suggestion unit can propose long-term study plans based on the student's learning goals and future plans. For example, the suggestion unit proposes long-term study plans based on the student's learning goals and future plans. For example, for a student aiming to obtain a specific qualification, the suggestion unit provides a lesson plan including steps to achieve that qualification. The suggestion unit also proposes study plans that match the student's future career goals. For example, for a student aiming to become an engineer, the suggestion unit provides a plan that incorporates programming and mathematics classes. The suggestion unit also proposes long-term study plans that allow the student to acquire skills in stages based on the student's learning goals. For example, for language classes, the suggestion unit provides a plan that includes steps from beginner to advanced. This makes it possible to propose long-term study plans based on the student's learning goals and future plans.

[0068] The suggestion unit can use the emotion estimation function to analyze the emotions felt by students when selecting a class, and suggest classes that will elicit the most positive emotions. For example, the suggestion unit can use the emotion estimation function to analyze the emotions felt by students when selecting a class, and suggest classes that will elicit the most positive emotions. For example, it can prioritize suggesting classes that make students feel happy or excited when selecting a class. The suggestion unit also makes suggestions to elicit positive emotions when selecting a class, based on the student's emotion data. For example, it can suggest classes that have received high ratings in the past. The suggestion unit also uses the emotion estimation function to analyze the emotions felt by students when selecting a class in real time, and suggest classes that will elicit the most positive emotions. For example, it can prioritize suggesting classes with high emotion scores. In this way, it is possible to analyze the emotions felt by students when selecting a class, and suggest classes that will elicit the most positive emotions.

[0069] The suggestion unit can make suggestions for group learning so that students can select classes in collaboration with other students. The suggestion unit, for example, makes suggestions for group learning so that students can select classes in collaboration with other students. For example, it matches students with the same interests and allows them to select classes in collaboration. The suggestion unit also makes suggestions for group learning so that students can advance their studies in cooperation with other students. For example, it proposes project-based classes and has them work on them in groups. The suggestion unit also builds a system for students to select classes in collaboration with other students and advance their studies. For example, it proposes classes that include group discussions and joint assignments. This makes it possible to make suggestions for group learning so that students can select classes in collaboration with other students.

[0070] The suggestion unit can collect feedback on the lessons selected by students and reflect it in the next suggestion. For example, the suggestion unit collects feedback on the lessons selected by students and reflects it in the next lesson suggestion. For example, it adjusts the content of the suggestion based on the lesson evaluations and comments. The suggestion unit also builds a system that optimizes the next lesson suggestion based on the feedback. For example, it prioritizes suggesting lesson content that has received high ratings. The suggestion unit also collects student feedback in real time and reflects it in the next lesson suggestion. For example, it conducts a survey after the lesson ends and makes suggestions based on the results. In this way, it is possible to collect feedback on the lessons selected by students and reflect it in the next suggestion.

[0071] The suggestion unit uses the emotion estimation function to analyze the level of satisfaction felt by students after selecting a class and can utilize this in the next suggestion. The suggestion unit, for example, uses the emotion estimation function to analyze the level of satisfaction felt by students after selecting a class and utilizes this in the next suggestion. For example, it prioritizes suggesting classes with high satisfaction. The suggestion unit also evaluates the level of satisfaction felt by students after selecting a class based on the students' emotion data and reflects this in the next suggestion. For example, it proposes classes that evoke strong positive emotions. The suggestion unit also uses the emotion estimation function to analyze the level of satisfaction felt by students in real time and utilizes this in the next suggestion. For example, it makes suggestions based on the content of classes that generate high satisfaction. This makes it possible to analyze the level of satisfaction felt by students after selecting a class and utilize this in the next suggestion.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The interest analysis unit collects data related to students' hobbies and special skills and can customize lesson content based on that. For example, it can suggest music theory or instrument playing classes to a student whose hobby is music. The interest analysis unit also analyzes data related to a student's special skills and generates lesson content that makes use of those skills. For example, it can suggest art or design classes to a student who is good at painting. The interest analysis unit also builds a system that individually optimizes lesson content based on a student's hobbies and special skills. For example, it can suggest sports science or training classes to a student who likes sports. This makes it possible to customize lesson content based on a student's hobbies and special skills.

[0074] The interest analysis unit can collect students' physiological data and analyze their concentration and stress levels while studying. For example, it can collect students' heart rate data and analyze their concentration and stress levels while studying. For example, it can identify times of high concentration based on heart rate fluctuations. The interest analysis unit can also analyze students' brain wave data to understand their brain activity while studying. For example, it can evaluate their concentration and relaxation levels based on fluctuations in alpha and beta waves. The interest analysis unit can also monitor students' physiological data in real time to optimize the learning environment. For example, it can suggest taking a break if their stress level is high. This makes it possible to analyze students' concentration and stress levels.

[0075] The interest analysis unit can collect the opinions of a student's family and friends and analyze their interests and learning needs while taking social influences into account. For example, it can conduct a survey of a student's family and friends to collect their opinions on the student's interests and learning needs. For example, it can identify the learning fields recommended by family members and the topics that friends are commonly interested in. The interest analysis unit can also analyze the content of conversations between a student and family members to identify their interests and learning needs while taking social influences into account. For example, it can analyze topics that frequently come up in conversations with family members. The interest analysis unit can also estimate a student's learning needs based on the learning history and interests of the student's family and friends. For example, it can determine that a student is likely to be interested in the fields that their family members are specializing in. This makes it possible to analyze interests and learning needs while taking social influences into account.

[0076] The interest analysis unit uses the emotion estimation function to analyze the emotions felt by students during class and can suggest lesson content that will elicit positive emotions. For example, it analyzes students' facial expressions during class to estimate their emotions. For example, it detects smiling or surprised expressions and suggests lesson content that will elicit positive emotions. The interest analysis unit also analyzes students' voice data to estimate their emotions. For example, it evaluates emotions based on changes in voice tone and speaking style and adjusts the lesson content. The interest analysis unit also collects students' emotional data and generates lesson content that will elicit positive emotions. For example, it structures lessons around topics with high emotion scores. This makes it possible to suggest lesson content that will elicit positive emotions in students.

[0077] The interest analysis unit analyzes students' social media and online activity history to gain a more detailed understanding of their interests and learning needs. For example, it analyzes the content of students' social media posts to identify topics and activities that interest them. For example, it extracts frequently posted keywords and hashtags to understand interest trends. The interest analysis unit also analyzes the activity history of online communities and forums in which students participate to identify areas of interest. For example, it analyzes the frequency of comments and posts on specific topics. The interest analysis unit also analyzes students' online search history to understand their learning needs. For example, if a student frequently searches for a specific learning resource or teaching material, it can determine that the student has a high interest in that field. This allows for a more detailed understanding of students' interests and learning needs.

[0078] The lesson generation unit can generate lesson content that matches the learning style of the student. For example, it can analyze the student's learning style and provide visual learning materials to students who prefer visual learning. For example, it can generate lesson content using diagrams or videos. The lesson generation unit can also provide audio learning materials or podcast-style lesson content to students who prefer auditory learning. For example, it can generate lessons with audio commentary or interview format. The lesson generation unit can also provide practical exercises or hands-on lesson content to students who prefer tactile learning. For example, it can generate experiment- or project-based lessons. This makes it possible to generate lesson content that matches the learning style of the student.

[0079] The lesson generation unit can use the emotion estimation function to generate lesson content centered on topics that students are most interested in. For example, it analyzes students' emotion data to identify the topics that students are most interested in. For example, it generates lesson content centered on topics with high emotion scores. The lesson generation unit also uses the emotion estimation function to provide lesson content related to topics that students are interested in. For example, it organizes lessons centered on topics that evoke strong positive emotions. The lesson generation unit also analyzes students' emotional responses in real time and generates lesson content based on topics that students are interested in. For example, it prioritizes incorporating topics with high emotion scores into lessons. This makes it possible to generate lesson content centered on topics that students are most interested in.

[0080] The lesson generation unit can generate specialized teaching materials to reinforce individual weaknesses based on the student's past learning data. For example, it can analyze the student's past test results and assignment submission status to identify weaknesses. For example, if the student has a low level of understanding of a particular topic, it can generate teaching materials specialized for that topic. The lesson generation unit also provides practice questions and exercises to reinforce individual weaknesses based on the student's learning history. For example, it generates additional practice questions for weak areas. The lesson generation unit also analyzes the student's learning data and provides customized lesson content to reinforce individual weaknesses. For example, it generates specialized teaching materials to improve specific skills. This makes it possible to generate specialized teaching materials to reinforce individual weaknesses of the student.

[0081] The lesson generation unit can generate cross-disciplinary lesson content that combines different fields of expertise. For example, a lesson that combines music and mathematics involves mathematically analyzing music theory. The lesson generation unit also provides cross-disciplinary lesson content to stimulate students' interest. For example, a lesson that combines filmmaking and history allows students to learn about the production process of historical films. The lesson generation unit also generates lesson content that blends different fields of expertise to enrich students' learning experiences. For example, a lesson that combines language and culture allows students to learn about a language and its background culture at the same time. This makes it possible to generate cross-disciplinary lesson content that combines different fields of expertise.

[0082] The lesson generation unit can use the emotion estimation function to generate interactive content to increase the motivation felt by students during lessons. For example, the emotion estimation function can be used to provide interactive content to increase the motivation felt by students during lessons. For example, lesson content incorporating game elements can be generated. The lesson generation unit can also generate interactive content to increase motivation based on students' emotion data. For example, lesson content that provides feedback in real time can be generated. The lesson generation unit can also use the emotion estimation function to provide interactive lesson content to increase students' motivation. For example, lesson content in the form of quizzes or challenges can be generated. This makes it possible to generate interactive content to increase the motivation felt by students during lessons.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The interest analysis unit analyzes the student's interests and learning needs. For example, the interest analysis unit collects and analyzes data such as the classes the student has taken in the past, topics they have shown interest in, and their learning progress. The interest analysis unit also performs analysis based on prompts generated by the generation AI that contain information about the student's interests and learning needs. Step 2: The lesson generation unit generates lesson content for specialized fields based on the results of the analysis by the interest analysis unit. For example, in a language class, the lesson generation unit's generation AI creates teaching materials and exercises tailored to the student's level. In a music class, the lesson generation unit's generation AI can also generate content related to how to play an instrument or music theory. In a filmmaking class, the lesson generation unit's generation AI can also provide teaching materials on scriptwriting and filming techniques. Step 3: The suggestion unit proposes the lesson content generated by the lesson generation unit to the student, allowing the student to freely select. For example, the suggestion unit uses the generation AI to suggest multiple lessons as "recommended lessons of the week," and the student selects the lesson that interests them. The suggestion unit then customizes the lesson selected by the student using the generation AI, providing individually optimized content.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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 AI 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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 AI 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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. [Explanation of symbols]

[0152] 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. It is a system that uses generative AI to create content that allows students to freely choose lessons in their specialized fields about once to three times a week. An interest analysis department that analyzes students' interests and learning needs; a lesson generation unit that generates lesson content in a specialized field based on the results of the analysis by the interest analysis unit; a suggestion unit that suggests lesson contents generated by the lesson generation unit to students and allows students to freely select the lesson contents. A system characterized by:

2. The interest analysis unit Analyze the student's social media and online activity history to gain a more detailed understanding of their interests and learning needs.

2. The system of claim 1.

3. The interest analysis unit Collecting physiological data from students and analyzing their concentration levels and stress levels during learning 2. The system of claim 1.

4. The interest analysis unit Analyze the emotions felt during the lesson and propose lesson content that will elicit positive emotions.

2. The system of claim 1.

5. The interest analysis unit Collect opinions from the students' families and friends to analyze their interests and learning needs, taking into account social influences.

2. The system of claim 1.

6. The interest analysis unit Collect data related to the student's hobbies and special skills and customize lesson content accordingly 2. The system of claim 1.

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