System

Abstract

An object of the system according to the embodiment is to reduce the class preparation time of a teacher and to propose an optimal class for each student.SOLUTION: A system includes an instruction plan creation part, a personality analysis part, and a proposal part. An instruction plan preparing part prepares an instruction plan on the basis of the theme and target of a lesson inputted from a teacher. The personality analysis unit analyzes the personality of the student. The proposal unit proposes individually optimized lessons based on the personality analyzed by the personality analysis unit.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] With conventional technology, teachers had to spend a lot of time preparing for lessons, making it difficult to provide optimal instruction to each student.

[0005] The system according to the embodiment aims to reduce the time required for teachers to prepare lessons and to propose lessons that are optimal for each student. [Means for solving the problem]

[0006] The system according to the embodiment includes a lesson plan creation unit, a personality analysis unit, and a suggestion unit. The lesson plan creation unit creates lesson plans based on lesson themes and goals input by the teacher. The personality analysis unit analyzes the personalities of students. The suggestion unit suggests individually optimized lessons based on the personalities analyzed by the personality analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the time teachers spend preparing lessons and suggest lessons that are best suited to each student. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The lesson plan creation system according to an embodiment of the present invention uses AI to create lesson plans and analyze students' personalities. This system can reduce teachers' lesson preparation time and improve the quality of education. It can also analyze the personalities of each student and propose individually optimized lessons based on that analysis. This allows the lesson plan creation system to reduce teachers' lesson preparation time and improve the quality of education. It can also provide the most appropriate lessons for each student.

[0029] A lesson plan creation system according to an embodiment includes a lesson plan creation unit, a personality analysis unit, and a suggestion unit. The lesson plan creation unit creates a lesson plan based on the lesson theme and goals input by a teacher. For example, based on the theme "Teaching how to solve linear equations in a mathematics class for second-year junior high school students" input by a teacher, the generation AI proposes the lesson progress method, teaching materials to be used, and specific teaching content. The generation AI generates a lesson plan based on prompts containing instructions on what the teacher wants the generation AI to do. The personality analysis unit analyzes students' personalities. For example, the generation AI analyzes students' personality data and learning history to identify which teaching method is best suited to each student. The generation AI can analyze students' personalities using personality tests and psychological profiles. The suggestion unit proposes individually optimized lessons based on the personality analyzed by the personality analysis unit. For example, the generation AI proposes optimal lesson formats such as individual instruction, group learning, and online learning. This allows the lesson plan creation system according to an embodiment to reduce teachers' lesson preparation time and improve the quality of education. It also enables the provision of lessons that are optimal for each student.

[0030] The lesson plan creation unit can refer to past successes and failures to generate lesson plans that minimize risk. For example, the generation AI in the lesson plan creation unit analyzes past lesson data and learns patterns of successful and unsuccessful lessons. For example, if a particular teaching method was effective, it can incorporate that method into a new lesson plan. The generation AI can also generate lesson plans that avoid patterns of unsuccessful lessons. For example, it can avoid teaching methods that resulted in low student understanding in past lessons. This makes it possible to generate lesson plans that minimize risk.

[0031] The lesson plan creation unit can provide customized lesson plans that take into account the individual teaching style and preferences of each teacher. For example, the generation AI in the lesson plan creation unit analyzes the teacher's past teaching history and learns the teacher's teaching style and preferences. For example, the teaching materials and approaches preferred by a particular teacher can be reflected in a new lesson plan. The generation AI can also prioritize suggesting teaching methods preferred by the teacher. For example, if a teacher prefers discussion-style lessons, it can provide a lesson plan that incorporates that format. This makes it possible to provide lesson plans that suit the individual teaching style and preferences of each teacher.

[0032] The lesson plan creation unit can generate lesson plans that correspond to different educational systems and curricula. For example, the generation AI learns the requirements of different educational systems and curricula and generates lesson plans based on those. For example, it creates lesson plans that correspond to the International Baccalaureate (IB) or American curriculum. The generation AI can also provide lesson plans that correspond to Montessori education or standard curricula. For example, it suggests teaching materials and teaching methods that are suitable for a specific educational system. This makes it possible to generate lesson plans that correspond to different educational systems and curricula.

[0033] The lesson plan creation unit can provide lesson plans optimized for the device or platform used by the teacher. For example, the generation AI in the lesson plan creation unit generates lesson plans optimized for the device used by the teacher (e.g., tablet or smartphone). For example, it suggests interactive teaching materials for tablets. The generation AI can also provide lesson plans compatible with online learning platforms. For example, it suggests teaching methods that utilize specific online learning tools. This makes it possible to provide lesson plans optimized for the device or platform used by the teacher.

[0034] The personality analysis unit can create a student's personality profile using psychological tests and questionnaires. For example, the generation AI can create a student's personality profile using psychological tests and questionnaires. For example, it can conduct a Big Five personality trait test and generate a personality profile based on the results. The generation AI can also create a personality profile using MBTI or a self-assessment questionnaire. For example, it can analyze the student's responses and identify personality traits. This allows it to create a detailed personality profile of the student.

[0035] The personality analysis unit can conduct a comprehensive personality analysis by taking into account the family environment and social background. For example, the generative AI can conduct a comprehensive personality analysis by taking into account the student's family environment and social background. For example, the family's economic situation and the parents' educational level can be incorporated into the analysis. The generative AI can also conduct a personality analysis by taking into account the local culture and social background. For example, it can analyze a student's personality based on the characteristics of the area. This makes it possible to conduct a comprehensive personality analysis that takes into account the student's family environment and social background.

[0036] The personality analysis unit can perform personality analysis according to different age groups and grade levels. For example, the generation AI performs personality analysis according to different age groups and grade levels. For example, it creates personality profiles suitable for elementary school students, junior high school students, and high school students. The generation AI can also perform analysis taking into account personality characteristics according to age group and grade level. For example, it identifies learning styles and personality characteristics according to age. This makes it possible to perform personality analysis according to different age groups and grade levels.

[0037] The personality analysis unit can perform personality analysis that corresponds to students from different cultures and regions. For example, the generation AI performs personality analysis that corresponds to students from different cultures and regions. For example, it creates a personality profile taking into account cultural background and regional characteristics. The generation AI can also perform analysis that takes into account personality characteristics that correspond to different cultures and regions. For example, it performs personality analysis based on Asian culture and European culture. This makes it possible to perform personality analysis that corresponds to students from different cultures and regions.

[0038] The suggestion unit can identify the optimal learning method using the student's learning history and grade data. For example, the generative AI analyzes the student's learning history and grade data to identify the optimal learning method. For example, it can suggest an effective learning method based on past grades and learning patterns. The generative AI can also provide an individually optimized learning method based on the student's learning history. For example, it can suggest supplementary lessons or additional learning materials for subjects in which a particular student is weak. This makes it possible to identify the optimal learning method based on the student's learning history and grade data.

[0039] The suggestion unit can propose the optimal study method by taking into account the student's behavioral data while studying. For example, the suggestion unit uses a generation AI to analyze the student's behavioral data while studying and propose the optimal study method. For example, it creates an effective study schedule based on the student's concentration level and the timing of breaks. The generation AI can also adjust the progress of study based on the student's behavioral data. For example, if the student's concentration is declining, it can suggest a short break. This makes it possible to propose the optimal study method based on the student's behavioral data while studying.

[0040] The suggestion unit can analyze students' learning styles and suggest learning methods suited to different subjects. For example, the generative AI can analyze students' learning styles and suggest learning methods suited to different subjects. For example, it can distinguish between learning methods for mathematics and learning methods for English. The generative AI can also provide the optimal learning method for each subject. For example, it can suggest learning materials that make heavy use of diagrams and graphs to visual students, and audio materials to auditory students. This makes it possible to suggest learning methods suited to different subjects.

[0041] The suggestion unit can analyze students' learning styles and suggest learning methods that are suitable for online learning or hybrid learning. For example, the generative AI analyzes students' learning styles and suggests learning methods that are suitable for online learning or hybrid learning. For example, it suggests teaching materials and activities that are suitable for online classes. The generative AI can also provide teaching methods that are suitable for hybrid learning. For example, it suggests a learning plan that combines face-to-face classes and online classes. This makes it possible to suggest learning methods that are suitable for online learning or hybrid learning.

[0042] When creating lesson plans for OJT sites, the lesson plan creation unit can analyze the work content and job requirements in detail and propose the optimal lesson plan. For example, when the generation AI creates lesson plans for OJT sites, the lesson plan creation unit analyzes the work content and job requirements in detail and proposes the optimal lesson plan. For example, when creating a training program for new employees, it proposes a lesson plan that matches the work content. The generation AI can also identify the necessary skills and knowledge based on the job requirements and provide a lesson plan based on that. For example, it proposes a lesson plan to strengthen the skills required for a specific work process. This makes it possible to propose the optimal lesson plan based on the work content and job requirements at OJT sites.

[0043] When creating lesson plans for OJT, the lesson plan creation unit can refer to past training data and feedback to generate effective lesson plans. For example, the generation AI in the lesson plan creation unit analyzes past training data and feedback to generate effective lesson plans. For example, it can incorporate teaching methods that were successful in past training into new lesson plans. The generation AI can also improve lesson plans based on trainee feedback. For example, it can prioritize suggesting teaching methods that were easy for trainees to understand. This makes it possible to generate effective lesson plans based on past training data and feedback.

[0044] The lesson plan creation unit can propose lesson plans tailored to different industries and occupations. For example, the generation AI learns data from different industries and occupations and proposes lesson plans based on that. For example, it distinguishes between training programs for the IT industry and manufacturing industry. The generation AI can also identify the skills and knowledge required for each industry and provide lesson plans based on that. For example, it proposes a lesson plan to strengthen customer service skills in the service industry. This makes it possible to propose lesson plans tailored to different industries and occupations.

[0045] The lesson plan creation unit can propose lesson plans that accommodate remote work and hybrid work. For example, the generation AI learns data on remote work and hybrid work and proposes lesson plans based on that data. For example, it creates a training program suitable for remote work. The generation AI can also provide teaching methods that accommodate hybrid work. For example, it proposes a program that combines face-to-face training and online training. This makes it possible to propose lesson plans that accommodate remote work and hybrid work.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The lesson plan creation unit can analyze teachers' past lesson evaluation data and incorporate highly rated lesson content into new lesson plans. For example, teaching methods and materials that received high marks from students in a particular lesson can be incorporated into new lesson plans. The generation AI can also make suggestions for improving lesson content that received low marks based on teachers' lesson evaluation data. For example, it can review the way a lesson that received low marks was conducted and propose more effective teaching methods. This makes it possible to provide lesson plans based on teachers' lesson evaluation data.

[0048] The lesson plan creation unit can provide lesson plans based on a teacher's area of ​​expertise and specialization. For example, it can create lesson plans based on the subjects or themes that a particular teacher specializes in. The generation AI can also suggest teaching methods that utilize the teacher's specialized knowledge. For example, it can provide lesson plans that incorporate the latest research and topics related to the teacher's area of ​​expertise. This makes it possible to provide lesson plans that are tailored to the teacher's area of ​​expertise and specialization.

[0049] The lesson plan creation unit can provide lesson plans to help teachers improve their skills. For example, the generative AI can evaluate a teacher's skill level and suggest the training and teaching materials necessary to improve their skills. The generative AI can also provide lesson plans that incorporate the latest educational technologies and methodologies to help teachers improve their skills. For example, it can suggest workshops and online courses for teachers to learn new educational technologies. This makes it possible to provide lesson plans that help teachers improve their skills.

[0050] The lesson plan creation unit can propose long-term teaching plans taking into account teachers' career paths. For example, the generation AI can analyze teachers' career goals and create lesson plans based on them. The generation AI can also provide lesson plans to strengthen skills and knowledge in line with teachers' career paths. For example, if a teacher aims to become a manager, it will propose lesson plans to strengthen leadership skills. This makes it possible to provide long-term teaching plans that are in line with teachers' career paths.

[0051] The lesson plan creation unit can take into account the teacher's communication style and provide lesson plans based on that. For example, the generation AI can analyze the teacher's communication style and suggest teaching methods that suit it. The generation AI can also provide teaching materials and activities that suit the teacher's communication style. For example, if the teacher prefers interactive lessons, it will suggest a lesson plan that emphasizes discussion. This makes it possible to provide lesson plans that suit the teacher's communication style.

[0052] The lesson plan creation unit can collect feedback from teachers after lessons and improve lesson plans based on that. For example, the generation AI can analyze the teacher's feedback after lessons and identify areas for improvement. The generation AI can also suggest improvement plans for the next lesson based on the teacher's feedback. For example, if a particular teaching method was not effective, it can suggest an alternative method. This makes it possible to provide lesson plans based on the teacher's feedback after lessons.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The lesson plan creation unit creates a lesson plan based on the lesson theme and goals input by the teacher. For example, based on the theme input by the teacher, "Teaching how to solve linear equations in a mathematics class for second-year junior high school students," the generation AI proposes how the lesson should proceed, the teaching materials to be used, and the specific content of the lesson. The generation AI generates a lesson plan based on prompts that include instructions on what the teacher wants the generation AI to do. Step 2: The personality analysis unit analyzes the student's personality. For example, the generative AI can analyze the student's personality data and learning history to determine what teaching method is appropriate for that student. The generative AI can analyze the student's personality using personality tests and psychological profiles. Step 3: The suggestion unit proposes individually optimized lessons based on the personality analyzed by the personality analysis unit. For example, the generation AI proposes optimal lesson formats such as individual instruction, group learning, and online learning. This allows the lesson plan creation system according to the embodiment to reduce teachers' lesson preparation time and improve the quality of education. It also makes it possible to provide optimal lessons for each student.

[0055] (Example 2) The lesson plan creation system according to an embodiment of the present invention uses AI to create lesson plans and analyze students' personalities. This system can reduce teachers' lesson preparation time and improve the quality of education. It can also analyze the personalities of each student and propose individually optimized lessons based on that analysis. This allows the lesson plan creation system to reduce teachers' lesson preparation time and improve the quality of education. It can also provide the most appropriate lessons for each student.

[0056] A lesson plan creation system according to an embodiment includes a lesson plan creation unit, a personality analysis unit, and a suggestion unit. The lesson plan creation unit creates a lesson plan based on the lesson theme and goals input by a teacher. For example, based on the theme "Teaching how to solve linear equations in a mathematics class for second-year junior high school students" input by a teacher, the generation AI proposes the lesson progress method, teaching materials to be used, and specific teaching content. The generation AI generates a lesson plan based on prompts containing instructions on what the teacher wants the generation AI to do. The personality analysis unit analyzes students' personalities. For example, the generation AI analyzes students' personality data and learning history to identify which teaching method is best suited to each student. The generation AI can analyze students' personalities using personality tests and psychological profiles. The suggestion unit proposes individually optimized lessons based on the personality analyzed by the personality analysis unit. For example, the generation AI proposes optimal lesson formats such as individual instruction, group learning, and online learning. This allows the lesson plan creation system according to an embodiment to reduce teachers' lesson preparation time and improve the quality of education. It also enables the provision of lessons that are optimal for each student.

[0057] The lesson plan creation unit can refer to past successes and failures to generate lesson plans that minimize risk. For example, the generation AI in the lesson plan creation unit analyzes past lesson data and learns patterns of successful and unsuccessful lessons. For example, if a particular teaching method was effective, it can incorporate that method into a new lesson plan. The generation AI can also generate lesson plans that avoid patterns of unsuccessful lessons. For example, it can avoid teaching methods that resulted in low student understanding in past lessons. This makes it possible to generate lesson plans that minimize risk.

[0058] The lesson plan creation unit can provide customized lesson plans that take into account the individual teaching style and preferences of each teacher. For example, the generation AI in the lesson plan creation unit analyzes the teacher's past teaching history and learns the teacher's teaching style and preferences. For example, the teaching materials and approaches preferred by a particular teacher can be reflected in a new lesson plan. The generation AI can also prioritize suggesting teaching methods preferred by the teacher. For example, if a teacher prefers discussion-style lessons, it can provide a lesson plan that incorporates that format. This makes it possible to provide lesson plans that suit the individual teaching style and preferences of each teacher.

[0059] The lesson plan creation unit can use the emotion estimation function to analyze the emotional state of the teacher and propose lesson plans to reduce stress. For example, the lesson plan creation unit can use the emotion estimation function to monitor the emotional state of the teacher in real time and propose a lesson plan that will help the teacher relax if stress is high. For example, the lesson plan creation unit can adjust the pace of the class to a slower pace. The emotion estimation function can also analyze the teacher's facial expressions and voice to determine the teacher's emotional state. For example, if the teacher is tired, the lesson plan creation unit can propose a simple activity. This makes it possible to propose a lesson plan to reduce the teacher's stress.

[0060] The lesson plan creation unit can generate lesson plans that correspond to different educational systems and curricula. For example, the generation AI learns the requirements of different educational systems and curricula and generates lesson plans based on those. For example, it creates lesson plans that correspond to the International Baccalaureate (IB) or American curriculum. The generation AI can also provide lesson plans that correspond to Montessori education or standard curricula. For example, it suggests teaching materials and teaching methods that are suitable for a specific educational system. This makes it possible to generate lesson plans that correspond to different educational systems and curricula.

[0061] The lesson plan creation unit can provide lesson plans optimized for the device or platform used by the teacher. For example, the generation AI in the lesson plan creation unit generates lesson plans optimized for the device used by the teacher (e.g., tablet or smartphone). For example, it suggests interactive teaching materials for tablets. The generation AI can also provide lesson plans compatible with online learning platforms. For example, it suggests teaching methods that utilize specific online learning tools. This makes it possible to provide lesson plans optimized for the device or platform used by the teacher.

[0062] The lesson plan creation unit can use the emotion estimation function to monitor students' emotional states in real time and dynamically adjust lesson plans accordingly. For example, the lesson plan creation unit can use the emotion estimation function to monitor students' emotional states in real time, and if a student feels stressed during class, suggest a relaxing activity. For example, the emotion estimation function can analyze a student's facial expressions and voice to determine their emotional state. The emotion estimation function can also collect students' biometric data (heart rate and electrodermal activity) using sensors to analyze their emotional state. For example, if a student is lacking concentration, it can suggest a break. This makes it possible to dynamically adjust lesson plans according to the student's emotional state.

[0063] The personality analysis unit can create a student's personality profile using psychological tests and questionnaires. For example, the generation AI can create a student's personality profile using psychological tests and questionnaires. For example, it can conduct a Big Five personality trait test and generate a personality profile based on the results. The generation AI can also create a personality profile using MBTI or a self-assessment questionnaire. For example, it can analyze the student's responses and identify personality traits. This allows it to create a detailed personality profile of the student.

[0064] The personality analysis unit can conduct a comprehensive personality analysis by taking into account the family environment and social background. For example, the generative AI can conduct a comprehensive personality analysis by taking into account the student's family environment and social background. For example, the family's economic situation and the parents' educational level can be incorporated into the analysis. The generative AI can also conduct a personality analysis by taking into account the local culture and social background. For example, it can analyze a student's personality based on the characteristics of the area. This makes it possible to conduct a comprehensive personality analysis that takes into account the student's family environment and social background.

[0065] The personality analysis unit can use the emotion estimation function to monitor the emotional state of a student in real time and suggest an emotionally appropriate teaching method. The personality analysis unit can, for example, use the emotion estimation function to monitor the emotional state of a student in real time and suggest an emotionally appropriate teaching method. For example, if a student is feeling stressed, it can suggest a relaxing activity. The emotion estimation function can also analyze a student's facial expressions and voice to determine the student's emotional state. For example, if a student is lacking concentration, it can suggest a break. This makes it possible to suggest a teaching method that suits the student's emotional state.

[0066] The personality analysis unit can perform personality analysis according to different age groups and grade levels. For example, the generation AI performs personality analysis according to different age groups and grade levels. For example, it creates personality profiles suitable for elementary school students, junior high school students, and high school students. The generation AI can also perform analysis taking into account personality characteristics according to age group and grade level. For example, it identifies learning styles and personality characteristics according to age. This makes it possible to perform personality analysis according to different age groups and grade levels.

[0067] The personality analysis unit can perform personality analysis that corresponds to students from different cultures and regions. For example, the generation AI performs personality analysis that corresponds to students from different cultures and regions. For example, it creates a personality profile taking into account cultural background and regional characteristics. The generation AI can also perform analysis that takes into account personality characteristics that correspond to different cultures and regions. For example, it performs personality analysis based on Asian culture and European culture. This makes it possible to perform personality analysis that corresponds to students from different cultures and regions.

[0068] The personality analysis unit can use the emotion estimation function to analyze the emotional state of students and make suggestions to provide an emotionally positive learning environment. The personality analysis unit, for example, uses the emotion estimation function to monitor the emotional state of students in real time and make suggestions to provide an emotionally positive learning environment. For example, it can suggest environmental settings that allow students to relax. The emotion estimation function can also analyze students' facial expressions and voices to determine their emotional state. For example, if a student is lacking concentration, it can suggest taking a break. This makes it possible to make suggestions to provide a positive learning environment according to the student's emotional state.

[0069] The suggestion unit can identify the optimal learning method using the student's learning history and grade data. For example, the generative AI analyzes the student's learning history and grade data to identify the optimal learning method. For example, it can suggest an effective learning method based on past grades and learning patterns. The generative AI can also provide an individually optimized learning method based on the student's learning history. For example, it can suggest supplementary lessons or additional learning materials for subjects in which a particular student is weak. This makes it possible to identify the optimal learning method based on the student's learning history and grade data.

[0070] The suggestion unit can propose the optimal study method by taking into account the student's behavioral data while studying. For example, the suggestion unit uses a generation AI to analyze the student's behavioral data while studying and propose the optimal study method. For example, it creates an effective study schedule based on the student's concentration level and the timing of breaks. The generation AI can also adjust the progress of study based on the student's behavioral data. For example, if the student's concentration is declining, it can suggest a short break. This makes it possible to propose the optimal study method based on the student's behavioral data while studying.

[0071] The suggestion unit can use the emotion estimation function to monitor the emotional state of the student in real time and suggest an emotionally appropriate study method. For example, the suggestion unit can use the emotion estimation function to monitor the emotional state of the student in real time and suggest an emotionally appropriate study method. For example, if the student is feeling stressed, the suggestion unit can suggest a relaxing activity. The emotion estimation function can also analyze the student's facial expressions and voice to determine the student's emotional state. For example, if the student is lacking concentration, the suggestion unit can suggest a break. This makes it possible to suggest a study method that suits the student's emotional state.

[0072] The suggestion unit can analyze students' learning styles and suggest learning methods suited to different subjects. For example, the generative AI can analyze students' learning styles and suggest learning methods suited to different subjects. For example, it can distinguish between learning methods for mathematics and learning methods for English. The generative AI can also provide the optimal learning method for each subject. For example, it can suggest learning materials that make heavy use of diagrams and graphs to visual students, and audio materials to auditory students. This makes it possible to suggest learning methods suited to different subjects.

[0073] The suggestion unit can analyze students' learning styles and suggest learning methods that are suitable for online learning or hybrid learning. For example, the generative AI analyzes students' learning styles and suggests learning methods that are suitable for online learning or hybrid learning. For example, it suggests teaching materials and activities that are suitable for online classes. The generative AI can also provide teaching methods that are suitable for hybrid learning. For example, it suggests a learning plan that combines face-to-face classes and online classes. This makes it possible to suggest learning methods that are suitable for online learning or hybrid learning.

[0074] The suggestion unit can use the emotion estimation function to analyze the emotional state of the student and make suggestions to provide an emotionally positive learning environment. For example, the suggestion unit can use the emotion estimation function to monitor the student's emotional state in real time and make suggestions to provide an emotionally positive learning environment. For example, the suggestion unit can suggest environmental settings that allow the student to relax. The emotion estimation function can also analyze the student's facial expressions and voice to determine the student's emotional state. For example, if the student is lacking concentration, the suggestion unit can suggest taking a break. This makes it possible to make suggestions to provide a positive learning environment according to the student's emotional state.

[0075] When creating lesson plans for OJT sites, the lesson plan creation unit can analyze the work content and job requirements in detail and propose the optimal lesson plan. For example, when the generation AI creates lesson plans for OJT sites, the lesson plan creation unit analyzes the work content and job requirements in detail and proposes the optimal lesson plan. For example, when creating a training program for new employees, it proposes a lesson plan that matches the work content. The generation AI can also identify the necessary skills and knowledge based on the job requirements and provide a lesson plan based on that. For example, it proposes a lesson plan to strengthen the skills required for a specific work process. This makes it possible to propose the optimal lesson plan based on the work content and job requirements at OJT sites.

[0076] When creating lesson plans for OJT, the lesson plan creation unit can refer to past training data and feedback to generate effective lesson plans. For example, the generation AI in the lesson plan creation unit analyzes past training data and feedback to generate effective lesson plans. For example, it can incorporate teaching methods that were successful in past training into new lesson plans. The generation AI can also improve lesson plans based on trainee feedback. For example, it can prioritize suggesting teaching methods that were easy for trainees to understand. This makes it possible to generate effective lesson plans based on past training data and feedback.

[0077] The lesson plan creation unit can use the emotion estimation function to monitor the emotional state of the trainee in real time and propose an emotionally appropriate lesson plan. The lesson plan creation unit can, for example, use the emotion estimation function to monitor the emotional state of the trainee in real time and propose an emotionally appropriate lesson plan. For example, if the trainee is feeling stressed, it can suggest a relaxing activity. The emotion estimation function can also analyze the trainee's facial expressions and voice to determine the trainee's emotional state. For example, if the trainee is lacking concentration, it can suggest a break. This makes it possible to propose a lesson plan that suits the trainee's emotional state.

[0078] The lesson plan creation unit can propose lesson plans tailored to different industries and occupations. For example, the generation AI learns data from different industries and occupations and proposes lesson plans based on that. For example, it distinguishes between training programs for the IT industry and manufacturing industry. The generation AI can also identify the skills and knowledge required for each industry and provide lesson plans based on that. For example, it proposes a lesson plan to strengthen customer service skills in the service industry. This makes it possible to propose lesson plans tailored to different industries and occupations.

[0079] The lesson plan creation unit can propose lesson plans that accommodate remote work and hybrid work. For example, the generation AI learns data on remote work and hybrid work and proposes lesson plans based on that data. For example, it creates a training program suitable for remote work. The generation AI can also provide teaching methods that accommodate hybrid work. For example, it proposes a program that combines face-to-face training and online training. This makes it possible to propose lesson plans that accommodate remote work and hybrid work.

[0080] The lesson plan creation unit can use the emotion estimation function to analyze the emotional state of the trainee and make suggestions to provide an emotionally positive training environment. The lesson plan creation unit, for example, uses the emotion estimation function to monitor the emotional state of the trainee in real time and make suggestions to provide an emotionally positive training environment. For example, it can suggest environmental settings that allow the trainee to relax. The emotion estimation function can also analyze the trainee's facial expressions and voice to determine the trainee's emotional state. For example, if the trainee is lacking concentration, it can suggest taking a break. This makes it possible to make suggestions to provide a positive training environment according to the trainee's emotional state.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The lesson plan creation unit can analyze teachers' past lesson evaluation data and incorporate highly rated lesson content into new lesson plans. For example, teaching methods and materials that received high marks from students in a particular lesson can be incorporated into new lesson plans. The generation AI can also make suggestions for improving lesson content that received low marks based on teachers' lesson evaluation data. For example, it can review the way a lesson that received low marks was conducted and propose more effective teaching methods. This makes it possible to provide lesson plans based on teachers' lesson evaluation data.

[0083] The lesson plan creation unit can provide lesson plans based on a teacher's area of ​​expertise and specialization. For example, it can create lesson plans based on the subjects or themes that a particular teacher specializes in. The generation AI can also suggest teaching methods that utilize the teacher's specialized knowledge. For example, it can provide lesson plans that incorporate the latest research and topics related to the teacher's area of ​​expertise. This makes it possible to provide lesson plans that are tailored to the teacher's area of ​​expertise and specialization.

[0084] The lesson plan creation unit can use the emotion estimation function to analyze the emotional state of students and suggest emotionally appropriate teaching materials. For example, it can suggest themes or activities that are likely to interest students. The emotion estimation function can also analyze students' facial expressions and voices to determine their emotional state. For example, if a student is bored, it can suggest teaching materials that will pique their interest. This makes it possible to suggest teaching materials that match the student's emotional state.

[0085] The lesson plan creation unit can provide lesson plans to help teachers improve their skills. For example, the generative AI can evaluate a teacher's skill level and suggest the training and teaching materials necessary to improve their skills. The generative AI can also provide lesson plans that incorporate the latest educational technologies and methodologies to help teachers improve their skills. For example, it can suggest workshops and online courses for teachers to learn new educational technologies. This makes it possible to provide lesson plans that help teachers improve their skills.

[0086] The lesson plan creation unit can use the emotion estimation function to analyze the emotional state of the teacher and propose an emotionally appropriate lesson schedule. For example, if the teacher is tired, it can propose a schedule that includes breaks. The emotion estimation function can also determine the teacher's emotional state by analyzing their facial expressions and voice. For example, if the teacher is feeling stressed, it can suggest incorporating relaxing activities into the lesson. This makes it possible to propose a lesson schedule that suits the teacher's emotional state.

[0087] The lesson plan creation unit can propose long-term teaching plans taking into account teachers' career paths. For example, the generation AI can analyze teachers' career goals and create lesson plans based on them. The generation AI can also provide lesson plans to strengthen skills and knowledge in line with teachers' career paths. For example, if a teacher aims to become a manager, it will propose lesson plans to strengthen leadership skills. This makes it possible to provide long-term teaching plans that are in line with teachers' career paths.

[0088] The lesson plan creation unit can use the emotion estimation function to analyze a student's emotional state and provide emotionally appropriate feedback. For example, if a student is working hard, it can suggest words of encouragement. The emotion estimation function can also analyze a student's facial expressions and voice to determine their emotional state. For example, if a student is feeling down, it can suggest providing positive feedback. This makes it possible to provide feedback that is appropriate for the student's emotional state.

[0089] The lesson plan creation unit can take into account the teacher's communication style and provide lesson plans based on that. For example, the generation AI can analyze the teacher's communication style and suggest teaching methods that suit it. The generation AI can also provide teaching materials and activities that suit the teacher's communication style. For example, if the teacher prefers interactive lessons, it will suggest a lesson plan that emphasizes discussion. This makes it possible to provide lesson plans that suit the teacher's communication style.

[0090] The lesson plan creation unit can use the emotion estimation function to analyze the emotional state of the teacher and suggest lesson content that is emotionally appropriate. For example, if the teacher is relaxed, it will suggest more difficult content. The emotion estimation function can also determine the teacher's emotional state by analyzing their facial expressions and voice. For example, if the teacher is nervous, it will suggest starting with easy content. This makes it possible to suggest lesson content that suits the teacher's emotional state.

[0091] The lesson plan creation unit can collect feedback from teachers after lessons and improve lesson plans based on that. For example, the generation AI can analyze the teacher's feedback after lessons and identify areas for improvement. The generation AI can also suggest improvement plans for the next lesson based on the teacher's feedback. For example, if a particular teaching method was not effective, it can suggest an alternative method. This makes it possible to provide lesson plans based on the teacher's feedback after lessons.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The lesson plan creation unit creates a lesson plan based on the lesson theme and goals input by the teacher. For example, based on the theme input by the teacher, "Teaching how to solve linear equations in a mathematics class for second-year junior high school students," the generation AI proposes how the lesson should proceed, the teaching materials to be used, and the specific content of the lesson. The generation AI generates a lesson plan based on prompts that include instructions on what the teacher wants the generation AI to do. Step 2: The personality analysis unit analyzes the student's personality. For example, the generative AI can analyze the student's personality data and learning history to determine what teaching method is appropriate for that student. The generative AI can analyze the student's personality using personality tests and psychological profiles. Step 3: The suggestion unit proposes individually optimized lessons based on the personality analyzed by the personality analysis unit. For example, the generation AI proposes optimal lesson formats such as individual instruction, group learning, and online learning. This allows the lesson plan creation system according to the embodiment to reduce teachers' lesson preparation time and improve the quality of education. It also makes it possible to provide optimal lessons for each student.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

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

[0130] The 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.

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

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A lesson plan creation section creates lesson plans based on lesson themes and goals input by teachers; A personality analysis department that analyzes students' personalities, a suggestion unit that suggests individually optimized lessons based on the personality analyzed by the personality analysis unit. A system characterized by:

2. The lesson plan creation unit Refer to past successes and failures to generate lesson plans that minimize risk 2. The system of claim 1.

3. The lesson plan creation unit Generate lesson plans that correspond to different educational systems and curricula 2. The system of claim 1.

4. The personality analysis unit Creating a personality profile of said student using psychological tests and questionnaires 2. The system of claim 1.

5. The proposal unit Identify the best learning method using the student's learning history and grade data 2. The system of claim 1.

6. The lesson plan creation unit Analyze the emotional state of the teacher and propose teaching plans to reduce stress 2. The system of claim 1.

7. The personality analysis unit Monitor the student's emotional state in real time and suggest emotionally appropriate teaching methods.

2. The system of claim 1.

8. The proposal unit Monitor the student's emotional state in real time and suggest emotionally appropriate learning methods.

2. The system of claim 1.

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