System

Abstract

An object of the system according to the embodiment is to automate creation of an individual education program and a learning print for a student with a disability.SOLUTION: A system according to an embodiment includes an education program generation unit and a learning print generation unit. The educational program generation unit automatically generates an educational program based on the degree of student's disability. The learning print generation unit automatically creates a learning print based on the educational program generated by the educational program generation 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] Conventional technology has had the problem that creating individual educational programs and study materials for students with disabilities is a burden for teachers.

[0005] The system according to the embodiment aims to automate the creation of individualized educational programs and study materials for students with disabilities. [Means for solving the problem]

[0006] The system according to the embodiment includes an educational program generation unit and a study print generation unit. The educational program generation unit automatically generates an educational program based on the degree of disability of a student. The study print generation unit automatically creates study prints based on the educational program generated by the educational program generation unit. [Effects of the Invention]

[0007] An embodiment of the system can automate the creation of individualized educational programs and worksheets for students with disabilities. [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 education support system according to the embodiment of the present invention is a system that automates the creation of daily education programs and actual study handouts tailored to the degree of disability of students with disabilities, thereby reducing the burden on teachers and enabling more effective education.

[0029] An educational support system according to an embodiment includes an educational program generation unit and a study print generation unit. The educational program generation unit automatically generates an educational program based on the degree of disability of a student. For example, the educational program generation unit generates a program with braille and audio guides for a visually impaired student. The educational program generation unit also generates a program that makes extensive use of visually easy-to-understand diagrams and illustrations for a hearing impaired student. The educational program generation unit also generates a program with simple content that is easy to understand for a student with an intellectual disability. For example, teaching materials using braille and audio guides are provided for a visually impaired student. Teaching materials that make extensive use of visually easy-to-understand diagrams and illustrations are provided for a hearing impaired student. Teaching materials with simple content that is easy to understand are provided for a student with an intellectual disability. The study print generation unit automatically creates study prints based on the educational program generated by the educational program generation unit. For example, the study print generation unit creates study prints with braille and audio guides for a visually impaired student. The study print generation unit also creates study prints that make extensive use of visually easy-to-understand diagrams and illustrations for a hearing impaired student. In addition, the study print generation unit creates prints with simple content that are easy to understand for students with intellectual disabilities. For example, prints using Braille or audio guides are provided for students with visual impairments. Prints that make extensive use of visually easy-to-understand diagrams and illustrations are provided for students with hearing impairments. Prints with simple content that are easy to understand are provided for students with intellectual disabilities. This enables the education support system according to the embodiment to automatically create educational programs and study prints based on the degree of disability. For example, the education support system can reduce the burden on teachers and provide more effective education.

[0030] The educational program generation unit analyzes students' past learning history and performance data to suggest the optimal learning pace and content for each individual student. For example, the educational program generation unit analyzes a student's past test results and homework submission status, and the generation AI suggests the optimal learning pace for each individual student. For example, if a student has weaknesses in a particular subject, the generation AI generates a program that focuses on that area. Furthermore, the educational program generation unit identifies a student's strengths and weaknesses based on the student's past learning history and suggests learning content accordingly. For example, it provides more advanced content for strong subjects and content that starts from the basics for weak subjects. Furthermore, the educational program generation unit analyzes students' performance data, and the generation AI suggests a program tailored to the student's learning style. For example, it provides materials that make extensive use of diagrams and graphs for students who are good at visual learning, and audio guides for students who are good at auditory learning. This makes it possible to suggest the optimal learning pace and content based on a student's past learning history and performance data.

[0031] The educational program generation unit can collect data on a student's home environment and lifestyle habits and personalize the educational program based on that data. For example, the educational program generation unit collects data on a student's home environment, and the generation AI personalizes the educational program based on that data. For example, a student with limited study time at home can be provided with a program that strengthens their learning at school. The educational program generation unit also collects data on a student's lifestyle habits, and the generation AI adjusts the educational program based on that data. For example, for a student with a night-owl lifestyle, the generation AI can change morning lessons to more relaxing content. The educational program generation unit also suggests a program to reduce a student's stress level based on data on the student's home environment and lifestyle habits. For example, for a student with high stress at home, relaxing activities can be added. This makes it possible to personalize the educational program based on a student's home environment and lifestyle habits.

[0032] The educational program generation unit generates a program that promotes cooperative learning between students with different disabilities, thereby deepening mutual understanding. The educational program generation unit generates, for example, a program that allows students with different disabilities to learn together cooperatively. For example, a student with a visual impairment and a student with a hearing impairment are paired together to solve a problem by leveraging each other's strengths. The educational program generation unit also generates a program in which students with different disabilities work together on a project. For example, a student with a visual impairment writes a document, and a student with a hearing impairment puts that document together into a presentation. The educational program generation unit also generates a program in which students with different disabilities learn together cooperatively to deepen mutual understanding. For example, a student with a visual impairment creates tactile learning materials, and a student with a hearing impairment uses those materials to learn. This makes it possible to promote cooperative learning between students with different disabilities and deepen mutual understanding.

[0033] The educational program generation unit can incorporate elements of intercultural understanding and diversity education into educational programs to foster a global perspective. For example, the educational program generation unit incorporates elements of intercultural understanding into educational programs, and the generation AI provides students with opportunities to learn about different cultures and customs. For example, it creates teaching materials about festivals and traditions of different cultures. The educational program generation unit also generates educational programs that incorporate elements of diversity education to provide students with opportunities to understand diverse values ​​and backgrounds. For example, it creates teaching materials to learn about the lives and experiences of people with different disabilities. The educational program generation unit also incorporates international topics into educational programs to foster a global perspective. For example, it generates teaching materials to learn about global environmental issues and human rights issues. In this way, it is possible to foster a global perspective by incorporating elements of intercultural understanding and diversity education.

[0034] The study print generation unit can personalize the content of study prints based on students' interests and concerns, thereby improving learning effectiveness. For example, the study print generation unit analyzes students' interests and concerns, and the generation AI personalizes the content of study prints based on that. For example, it provides questions related to themes that students like. The study print generation unit also generates study prints that incorporate topics that interest students. For example, it creates questions related to sports or hobbies that students are interested in. The study print generation unit also adjusts the content of study prints based on students' interests and concerns. For example, it provides learning materials related to science experiments or historical events that students are interested in. This makes it possible to personalize study prints based on students' interests and concerns.

[0035] The study print generation unit can incorporate game elements and interactive content into study prints, allowing students to learn while having fun. For example, the study print generation unit can incorporate game elements into study prints, allowing the generation AI to allow students to learn while having fun. For example, a system can be introduced in which points are accumulated each time a problem is solved. The study print generation unit can also incorporate interactive content into study prints, allowing the generation AI to allow students to learn by operating the system themselves. For example, an animation can be displayed when solving a problem using a tablet. The study print generation unit can also incorporate quiz-style questions into study prints, allowing the generation AI to encourage students to learn with a competitive spirit. For example, the entire class can hold a quiz competition and compete for the highest scores. In this way, by incorporating game elements and interactive content into study prints, it is possible to make learning fun.

[0036] The study print generation unit generates cross-curriculum prints that integrate different subjects and courses, thereby promoting comprehensive learning. The study print generation unit, for example, generates cross-curriculum prints that integrate different subjects and courses, and the generation AI promotes comprehensive learning. For example, it creates problems that combine mathematics and science. The study print generation unit also generates study prints that integrate knowledge from different subjects, and the generation AI provides students with opportunities to acquire a wide range of knowledge. For example, it creates teaching materials that combine history and geography. The study print generation unit also generates cross-curriculum prints, and the generation AI allows students to understand the connections between different subjects. For example, it provides problems that combine English and social studies. This makes it possible to promote comprehensive learning by generating cross-curriculum prints that integrate different subjects and courses.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The education support system can further monitor the physical health status of students and adjust the education program based on that. For example, if the education program generation unit detects that a student is not getting enough exercise, it generates a program to increase physical education time. Also, if the education program generation unit detects that a student is not getting enough sleep, it can provide a program with content that helps students relax. Furthermore, the education program generation unit can monitor the student's diet and add health education programs if the nutritional balance is unbalanced. This makes it possible to adjust the education program based on the student's physical health status.

[0039] The educational support system can also customize learning programs based on students' learning styles. For example, the educational program generation unit can provide learning materials that make extensive use of diagrams and graphs to students who are good at visual learning. It can also provide audio guides to students who are good at auditory learning. It can also provide tactile learning materials to students who are good at tactile learning. This makes it possible to provide optimal learning programs based on students' learning styles.

[0040] The education support system can further personalize learning programs based on the student's home environment and lifestyle. For example, the education program generation unit can provide a program that strengthens learning at school to a student who has limited time to study at home. Also, for a student who has a night-owl lifestyle, it can change morning lessons to more relaxing content. Furthermore, it can add relaxing activities to a student who is experiencing high stress at home. This makes it possible to personalize learning programs based on the student's home environment and lifestyle.

[0041] The educational support system can also generate programs that promote cooperative learning between students with different disabilities and deepen mutual understanding. For example, a program can be provided in which a visually impaired student and a hearing impaired student are paired to solve a problem by leveraging each other's strengths. It can also provide a program in which students with different disabilities work together on a project. It can also provide a program in which a visually impaired student creates tactile learning materials and a hearing impaired student uses those materials to learn. This makes it possible to promote cooperative learning between students with different disabilities and deepen mutual understanding.

[0042] The educational support system can also incorporate game elements and interactive content into study sheets, making learning fun. For example, the study sheet generation unit can implement a system in which points are accumulated each time a problem is solved. It can also provide interactive content that displays animations when a problem is solved using a tablet. It can also provide a program that allows the entire class to hold a quiz and compete for the highest scores. In this way, by incorporating game elements and interactive content into study sheets, it is possible to make learning fun.

[0043] The educational support system can also generate cross-curriculum printouts that integrate different subjects and courses, promoting comprehensive learning. For example, the study print generation unit can create problems that combine mathematics and science. It can also provide teaching materials that combine history and geography. It can also provide problems that combine English and social studies. In this way, by generating cross-curriculum printouts that integrate different subjects and courses, it is possible to promote comprehensive learning.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The educational program generator automatically generates educational programs based on the student's level of disability. For example, it generates programs with Braille and audio guides for students with visual impairments, programs with plenty of visually easy-to-understand diagrams and illustrations for students with hearing impairments, and programs with simple content that are easy to understand for students with intellectual disabilities. Step 2: The study print generator automatically creates study prints based on the educational program generated by the educational program generator. For example, for students with visual impairments, it creates prints with Braille or audio guides, and for students with hearing impairments, it creates prints with many visually easy-to-understand diagrams and illustrations. It also creates prints with simple content that are easy to understand for students with intellectual disabilities.

[0046] (Example 2) The education support system according to the embodiment of the present invention is a system that automates the creation of daily education programs and actual study handouts tailored to the degree of disability of students with disabilities, thereby reducing the burden on teachers and enabling more effective education.

[0047] An educational support system according to an embodiment includes an educational program generation unit and a study print generation unit. The educational program generation unit automatically generates an educational program based on the degree of disability of a student. For example, the educational program generation unit generates a program with braille and audio guides for a visually impaired student. The educational program generation unit also generates a program that makes extensive use of visually easy-to-understand diagrams and illustrations for a hearing impaired student. The educational program generation unit also generates a program with simple content that is easy to understand for a student with an intellectual disability. For example, teaching materials using braille and audio guides are provided for a visually impaired student. Teaching materials that make extensive use of visually easy-to-understand diagrams and illustrations are provided for a hearing impaired student. Teaching materials with simple content that is easy to understand are provided for a student with an intellectual disability. The study print generation unit automatically creates study prints based on the educational program generated by the educational program generation unit. For example, the study print generation unit creates study prints with braille and audio guides for a visually impaired student. The study print generation unit also creates study prints that make extensive use of visually easy-to-understand diagrams and illustrations for a hearing impaired student. In addition, the study print generation unit creates prints with simple content that are easy to understand for students with intellectual disabilities. For example, prints using Braille or audio guides are provided for students with visual impairments. Prints that make extensive use of visually easy-to-understand diagrams and illustrations are provided for students with hearing impairments. Prints with simple content that are easy to understand are provided for students with intellectual disabilities. This enables the education support system according to the embodiment to automatically create educational programs and study prints based on the degree of disability. For example, the education support system can reduce the burden on teachers and provide more effective education.

[0048] The educational program generation unit can monitor students' emotional states in real time and dynamically adjust the educational program according to those emotions. For example, to monitor students' emotional states in real time, the generation AI uses facial recognition technology to analyze facial expressions and detect changes in emotions. For example, it determines whether students are concentrating during class and adjusts the program if their concentration drops. To monitor students' emotional states, the generation AI uses voice analysis technology to analyze their tone of voice and speaking style to detect changes in emotions. For example, if a student is feeling stressed, the program is changed to content that helps them relax. To monitor students' emotional states, the generation AI uses sensors to measure their heart rate and galvanic skin response and detect changes in emotions. For example, if a student is feeling tense, a relaxing activity is added. This enables dynamic adjustment of the educational program according to the student's emotional state.

[0049] The educational program generation unit analyzes students' past learning history and performance data to suggest the optimal learning pace and content for each individual student. For example, the educational program generation unit analyzes a student's past test results and homework submission status, and the generation AI suggests the optimal learning pace for each individual student. For example, if a student has weaknesses in a particular subject, the generation AI generates a program that focuses on that area. Furthermore, the educational program generation unit identifies a student's strengths and weaknesses based on the student's past learning history and suggests learning content accordingly. For example, it provides more advanced content for strong subjects and content that starts from the basics for weak subjects. Furthermore, the educational program generation unit analyzes students' performance data, and the generation AI suggests a program tailored to the student's learning style. For example, it provides materials that make extensive use of diagrams and graphs for students who are good at visual learning, and audio guides for students who are good at auditory learning. This makes it possible to suggest the optimal learning pace and content based on a student's past learning history and performance data.

[0050] The educational program generation unit can collect data on a student's home environment and lifestyle habits and personalize the educational program based on that data. For example, the educational program generation unit collects data on a student's home environment, and the generation AI personalizes the educational program based on that data. For example, a student with limited study time at home can be provided with a program that strengthens their learning at school. The educational program generation unit also collects data on a student's lifestyle habits, and the generation AI adjusts the educational program based on that data. For example, for a student with a night-owl lifestyle, the generation AI can change morning lessons to more relaxing content. The educational program generation unit also suggests a program to reduce a student's stress level based on data on the student's home environment and lifestyle habits. For example, for a student with high stress at home, relaxing activities can be added. This makes it possible to personalize the educational program based on a student's home environment and lifestyle habits.

[0051] The educational program generation unit generates a program that promotes cooperative learning between students with different disabilities, thereby deepening mutual understanding. The educational program generation unit generates, for example, a program that allows students with different disabilities to learn together cooperatively. For example, a student with a visual impairment and a student with a hearing impairment are paired together to solve a problem by leveraging each other's strengths. The educational program generation unit also generates a program in which students with different disabilities work together on a project. For example, a student with a visual impairment writes a document, and a student with a hearing impairment puts that document together into a presentation. The educational program generation unit also generates a program in which students with different disabilities learn together cooperatively to deepen mutual understanding. For example, a student with a visual impairment creates tactile learning materials, and a student with a hearing impairment uses those materials to learn. This makes it possible to promote cooperative learning between students with different disabilities and deepen mutual understanding.

[0052] The educational program generation unit can incorporate elements of intercultural understanding and diversity education into educational programs to foster a global perspective. For example, the educational program generation unit incorporates elements of intercultural understanding into educational programs, and the generation AI provides students with opportunities to learn about different cultures and customs. For example, it creates teaching materials about festivals and traditions of different cultures. The educational program generation unit also generates educational programs that incorporate elements of diversity education to provide students with opportunities to understand diverse values ​​and backgrounds. For example, it creates teaching materials to learn about the lives and experiences of people with different disabilities. The educational program generation unit also incorporates international topics into educational programs to foster a global perspective. For example, it generates teaching materials to learn about global environmental issues and human rights issues. In this way, it is possible to foster a global perspective by incorporating elements of intercultural understanding and diversity education.

[0053] The educational program generation unit uses the emotion estimation function to generate a motivation improvement program based on the student's emotions, thereby increasing their motivation to learn. The educational program generation unit, for example, uses the emotion estimation function to analyze the student's emotional state and generate a motivation improvement program. For example, if the student is tired, it adds a refreshing activity. The educational program generation unit also generates a program to increase motivation based on the student's emotions. For example, it sets small goals that give the student a sense of accomplishment and provides a program to celebrate the achievement. The educational program generation unit also uses the emotion estimation function to generate a program that provides feedback according to the student's emotions. For example, if the student is depressed, it displays an encouraging message. This makes it possible to generate a motivation improvement program based on the student's emotions and increase their motivation to learn.

[0054] The study print generation unit can monitor a student's emotional state in real time and dynamically adjust the content and format of the study print according to that emotion. For example, the study print generation unit monitors a student's emotional state in real time, and the generation AI adjusts the content of the study print. For example, if a student is concentrating, the difficulty level is increased, and if their concentration is declining, easier questions are provided. The study print generation unit also analyzes a student's emotional state, and the generation AI dynamically adjusts the format of the study print. For example, if a student is tired, the generation AI provides prints that make extensive use of visually easy-to-understand diagrams and illustrations. The study print generation unit also monitors a student's emotional state, and the generation AI personalizes the content of the study print. For example, the generation AI provides questions related to topics that the student is interested in. This makes it possible to dynamically adjust study prints according to the student's emotional state.

[0055] The study print generation unit can personalize the content of study prints based on students' interests and concerns, thereby improving learning effectiveness. For example, the study print generation unit analyzes students' interests and concerns, and the generation AI personalizes the content of study prints based on that. For example, it provides questions related to themes that students like. The study print generation unit also generates study prints that incorporate topics that interest students. For example, it creates questions related to sports or hobbies that students are interested in. The study print generation unit also adjusts the content of study prints based on students' interests and concerns. For example, it provides learning materials related to science experiments or historical events that students are interested in. This makes it possible to personalize study prints based on students' interests and concerns.

[0056] The study print generation unit can incorporate game elements and interactive content into study prints, allowing students to learn while having fun. For example, the study print generation unit can incorporate game elements into study prints, allowing the generation AI to allow students to learn while having fun. For example, a system can be introduced in which points are accumulated each time a problem is solved. The study print generation unit can also incorporate interactive content into study prints, allowing the generation AI to allow students to learn by operating the system themselves. For example, an animation can be displayed when solving a problem using a tablet. The study print generation unit can also incorporate quiz-style questions into study prints, allowing the generation AI to encourage students to learn with a competitive spirit. For example, the entire class can hold a quiz competition and compete for the highest scores. In this way, by incorporating game elements and interactive content into study prints, it is possible to make learning fun.

[0057] The study print generation unit generates cross-curriculum prints that integrate different subjects and courses, thereby promoting comprehensive learning. The study print generation unit, for example, generates cross-curriculum prints that integrate different subjects and courses, and the generation AI promotes comprehensive learning. For example, it creates problems that combine mathematics and science. The study print generation unit also generates study prints that integrate knowledge from different subjects, and the generation AI provides students with opportunities to acquire a wide range of knowledge. For example, it creates teaching materials that combine history and geography. The study print generation unit also generates cross-curriculum prints, and the generation AI allows students to understand the connections between different subjects. For example, it provides problems that combine English and social studies. This makes it possible to promote comprehensive learning by generating cross-curriculum prints that integrate different subjects and courses.

[0058] The study print generation unit uses the emotion estimation function to incorporate feedback based on the student's emotions into the study print, thereby maintaining motivation to learn. For example, the study print generation unit uses the emotion estimation function to analyze the student's emotional state, and the generation AI incorporates feedback into the study print. For example, an encouraging message is displayed after the student solves a problem. The study print generation unit also incorporates feedback based on the student's emotions into the study print, and the generation AI maintains the student's motivation. For example, a compliment is displayed if the student answers correctly, and a message is displayed encouraging the student to try again if the student answers incorrectly. The study print generation unit also uses the emotion estimation function to generate study prints that provide feedback according to the student's emotions. For example, if the student is tired, a message encouraging the student to take a break is displayed. In this way, by using the emotion estimation function to incorporate feedback based on the student's emotions into the study print, it is possible to maintain motivation to learn.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The education support system can further monitor the physical health status of students and adjust the education program based on that. For example, if the education program generation unit detects that a student is not getting enough exercise, it generates a program to increase physical education time. Also, if the education program generation unit detects that a student is not getting enough sleep, it can provide a program with content that helps students relax. Furthermore, the education program generation unit can monitor the student's diet and add health education programs if the nutritional balance is unbalanced. This makes it possible to adjust the education program based on the student's physical health status.

[0061] The education support system can further monitor the emotional state of the student and dynamically adjust the learning environment based on the emotional state. For example, if the student is feeling stressed, the education program generation unit can play relaxing music. If the student is lacking concentration, the education program generation unit can provide environmental sounds to help the student concentrate. If the student is tired, the education program generation unit can provide a program to increase break times. This makes it possible to dynamically adjust the learning environment based on the student's emotional state.

[0062] The educational support system can also customize learning programs based on students' learning styles. For example, the educational program generation unit can provide learning materials that make extensive use of diagrams and graphs to students who are good at visual learning. It can also provide audio guides to students who are good at auditory learning. It can also provide tactile learning materials to students who are good at tactile learning. This makes it possible to provide optimal learning programs based on students' learning styles.

[0063] The education support system can further personalize learning programs based on the student's home environment and lifestyle. For example, the education program generation unit can provide a program that strengthens learning at school to a student who has limited time to study at home. Also, for a student who has a night-owl lifestyle, it can change morning lessons to more relaxing content. Furthermore, it can add relaxing activities to a student who is experiencing high stress at home. This makes it possible to personalize learning programs based on the student's home environment and lifestyle.

[0064] The educational support system can also generate programs that promote cooperative learning between students with different disabilities and deepen mutual understanding. For example, a program can be provided in which a visually impaired student and a hearing impaired student are paired to solve a problem by leveraging each other's strengths. It can also provide a program in which students with different disabilities work together on a project. It can also provide a program in which a visually impaired student creates tactile learning materials and a hearing impaired student uses those materials to learn. This makes it possible to promote cooperative learning between students with different disabilities and deepen mutual understanding.

[0065] The education support system can further use the emotion estimation function to generate a motivation improvement program based on the student's emotions and increase their motivation to learn. For example, if the student is tired, the education program generation unit can add a refreshing activity. It can also set small goals that will give the student a sense of accomplishment and provide a program to celebrate their achievement. Furthermore, if the student is feeling down, it can provide a program that displays encouraging messages. In this way, it is possible to generate a motivation improvement program based on the student's emotions and increase their motivation to learn.

[0066] The education support system can further use the emotion estimation function to incorporate feedback based on the student's emotions into the study print to maintain motivation for learning. For example, the study print generation unit can display an encouraging message after the student solves a problem. It can also display words of praise if the student answers correctly, and provide feedback encouraging the student to try again if the student answers incorrectly. It can also display a message encouraging the student to take a break if the student is tired. In this way, by incorporating feedback based on the student's emotions into the study print using the emotion estimation function, it is possible to maintain motivation for learning.

[0067] The educational support system can also incorporate game elements and interactive content into study sheets, making learning fun. For example, the study sheet generation unit can implement a system in which points are accumulated each time a problem is solved. It can also provide interactive content that displays animations when a problem is solved using a tablet. It can also provide a program that allows the entire class to hold a quiz and compete for the highest scores. In this way, by incorporating game elements and interactive content into study sheets, it is possible to make learning fun.

[0068] The educational support system can also generate cross-curriculum printouts that integrate different subjects and courses, promoting comprehensive learning. For example, the study print generation unit can create problems that combine mathematics and science. It can also provide teaching materials that combine history and geography. It can also provide problems that combine English and social studies. In this way, by generating cross-curriculum printouts that integrate different subjects and courses, it is possible to promote comprehensive learning.

[0069] The education support system can further use the emotion estimation function to adjust the progress of the learning program based on the student's emotions. For example, the education program generation unit can smoothly advance the learning progress when the student is concentrating. Also, if the student is tired, it can provide a program that includes breaks. Furthermore, if the student is feeling stressed, it can add activities that allow them to relax. This makes it possible to adjust the progress of the learning program based on the student's emotions.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The educational program generator automatically generates educational programs based on the student's level of disability. For example, it generates programs with Braille and audio guides for students with visual impairments, programs with plenty of visually easy-to-understand diagrams and illustrations for students with hearing impairments, and programs with simple content that are easy to understand for students with intellectual disabilities. Step 2: The study print generator automatically creates study prints based on the educational program generated by the educational program generator. For example, for students with visual impairments, it creates prints with Braille or audio guides, and for students with hearing impairments, it creates prints with many visually easy-to-understand diagrams and illustrations. It also creates prints with simple content that are easy to understand for students with intellectual disabilities.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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]

[0139] 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. an educational program generation unit that automatically generates an educational program based on the degree of disability of a student; a learning print generating unit that automatically generates learning prints based on the educational program generated by the educational program generating unit. A system characterized by:

2. The educational program generation unit Monitoring the emotional state of the student in real time and dynamically adjusting the educational program according to the emotional state.

2. The system of claim 1.

3. The educational program generation unit Create a program to promote cooperative learning among students with different disabilities and deepen mutual understanding.

2. The system of claim 1.

4. The learning print generation unit The emotional state of the student is monitored in real time, and the content and format of the learning handouts are dynamically adjusted according to the emotional state.

2. The system of claim 1.

5. The learning print generation unit Incorporate game elements and interactive content into the learning materials to make learning fun.

2. The system of claim 1.

6. The educational program generation unit A motivational program based on the student's emotions is generated to enhance their motivation to learn.

2. The system of claim 1.

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