System

Abstract

An object of the system according to the embodiment is to solve the shortage of educational resources and provide a learning plan for each student.SOLUTION: A system according to an embodiment includes an infrastructure improvement unit, a AI collecting unit, and a data tutor unit. The infrastructure improvement unit performs Internet connection and device improvement. The data collection unit collects learning data of a student. The data-tutor unit provides a learning plan based on the AI collected by the data-collecting 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 technologies have room for improvement due to a lack of educational resources and insufficient provision of individual learning plans for students.

[0005] The system according to the embodiment aims to resolve the shortage of educational resources and provide individual learning plans for students. [Means for solving the problem]

[0006] The system according to the embodiment includes an infrastructure development unit, a data collection unit, and an AI tutor unit. The infrastructure development unit prepares internet connections and devices. The data collection unit collects learning data from students. The AI ​​tutor unit provides learning plans based on the data collected by the data collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can alleviate the shortage of educational resources and provide individual learning plans for students. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The school education support system according to an embodiment of the present invention is based on GIGA School and utilizes generative AI to compensate for the lack of educational resources, provides individual learning plans and advice to students, and provides a new forum for communication by connecting multiple small schools online. As a result, the school education support system can improve the quality of education in remote areas and support children's learning.

[0029] The school education support system according to the embodiment includes an infrastructure development unit, a data collection unit, and an AI tutor unit. The infrastructure development unit prepares internet connections and devices. For example, it installs high-speed internet lines in remote schools and distributes tablets and laptops to each student. The infrastructure development unit also optimizes the network environment within the school to provide a stable online learning environment. The data collection unit collects student learning data. For example, it collects data on learning progress, grades, questions, and assignments, and a generation AI generates appropriate advice and study plans based on this data. The data collection unit also builds a system that collects learning data in real time and provides immediate feedback. The AI ​​tutor unit provides study plans based on the data collected by the data collection unit. For example, the generation AI answers students' questions and provides individual study plans. The AI ​​tutor unit also continuously updates individual study plans in real time based on the students' learning histories. As a result, the school education support system according to the embodiment can compensate for a lack of educational resources, provide individual study plans and advice to students, and provide a new forum for communication by connecting multiple small schools online.

[0030] The Infrastructure Development Department can use generative AI to automatically generate optimal internet line placement plans and device distribution plans. For example, the Infrastructure Development Department uses generative AI to automatically generate optimal internet line placement plans taking into account each school's geographical conditions and student population. For example, the department proposes routes for efficiently introducing high-speed internet, even in remote areas such as mountainous regions and remote islands. The Infrastructure Development Department's generative AI also analyzes each student's home environment and learning style and automatically generates optimal device distribution plans based on that analysis. For example, the department provides individualized support, such as providing mobile routers to students who do not have internet access at home. The Infrastructure Development Department's generative AI also monitors local infrastructure conditions in real time and proposes strengthening internet lines and distributing additional devices as needed. For example, the department can immediately take countermeasures if communication speeds decrease in a specific area. This enables efficient infrastructure development by automatically generating optimal internet line placement plans and device distribution plans.

[0031] The data collection unit can build a system that collects students' learning data in real time and provides instant feedback. For example, the data collection unit builds a system in which a generative AI collects students' learning data in real time and provides instant feedback. For example, it instantly analyzes online test results and suggests specific areas for improvement to students. The data collection unit also monitors students' learning progress in real time and provides feedback as needed. For example, it suggests additional learning resources for students who appear to be falling behind in their studies. The data collection unit also develops a system in which a generative AI analyzes students' learning data in real time and provides instant feedback. For example, it provides individual learning advice based on assignment submission status and grades. In this way, learning data is collected in real time and instant feedback is provided, improving students' learning efficiency.

[0032] The AI ​​Tutoring Department can continuously update individual learning plans in real time based on a student's learning history. For example, the AI ​​Tutoring Department will develop an AI tutor in which the generating AI analyzes a student's learning history in real time and continuously updates the individual learning plan. For example, it will automatically suggest the next content to study based on the student's learning progress. The AI ​​Tutoring Department will also develop an AI tutor in which the generating AI adjusts the individual learning plan in real time based on the student's learning data. For example, if a weak point in a particular subject is found, it will provide additional practice problems. The AI ​​Tutoring Department will also build a system in which the generating AI analyzes a student's learning history and develops an AI tutor that continuously updates the individual learning plan in real time. For example, it will suggest appropriate teaching materials and resources based on the student's learning progress. This will maximize the student's learning effectiveness by continuously updating the individual learning plan in real time.

[0033] The Infrastructure Development Department can analyze the local power supply situation and propose the optimal energy management plan. For example, the generation AI in the Infrastructure Development Department analyzes the local power supply situation in real time and proposes the optimal energy management plan. For example, in areas with unstable power supply, it recommends the introduction of solar power generation and storage batteries. The generation AI in the Infrastructure Development Department also collects school power consumption data and proposes an efficient energy management plan. For example, it suggests adjusting schedules to reduce power consumption during peak hours and introducing energy-saving devices. The generation AI in the Infrastructure Development Department also integrates the local power supply situation with the school's power consumption data and automatically generates the optimal energy management plan. For example, it makes suggestions such as concentrating online classes during times when the power supply is stable. In this way, efficient energy use is possible by analyzing the local power supply situation and proposing the optimal energy management plan.

[0034] The AI ​​tutoring department can develop multiple AI tutors specializing in different subjects or topics, allowing students to choose from them. For example, the generating AI can develop multiple AI tutors specializing in different subjects or topics, allowing students to choose from them. For example, it can provide specialized tutors for mathematics, science, history, etc. The AI ​​tutoring department can also develop AI tutors specializing in each subject or topic, allowing students to choose from them according to their learning needs. For example, it can provide tutors that delve deeply into specific topics. The AI ​​tutoring department can also develop AI tutors specializing in different subjects or topics, creating a system that allows students to freely choose from them. For example, it can suggest appropriate tutors based on learning progress. This allows students to choose from AI tutors specializing in different subjects or topics, allowing them to choose from them according to their learning needs.

[0035] The infrastructure development department can plan optimal device distribution routes taking into account local traffic conditions. For example, the generation AI in the infrastructure development department analyzes local traffic conditions in real time and plans optimal device distribution routes. For example, it proposes routes to distribute devices during times of light traffic congestion. The generation AI in the infrastructure development department also integrates local geographic information and traffic data to automatically generate efficient device distribution routes. For example, it proposes the shortest route for schools in remote locations, shortening distribution time. The generation AI in the infrastructure development department also monitors local traffic conditions and adjusts device distribution routes in real time as needed. For example, it proposes alternative routes to avoid traffic accidents or congestion caused by construction. This enables efficient device distribution by planning optimal device distribution routes taking traffic conditions into account.

[0036] The data collection unit can collect home environment data and provide advice to optimize the learning environment at home. For example, the data collection unit allows the generating AI to collect home environment data and provide advice to optimize the learning environment at home. For example, it suggests ensuring a quiet study space and using appropriate lighting. The data collection unit also allows the generating AI to monitor home environment data in real time and suggest measures to improve the learning environment as needed. For example, it suggests soundproofing measures if the noise level in the home is high. The data collection unit also allows the generating AI to analyze home environment data and provide specific advice to optimize the learning environment at home. For example, it suggests the placement of furniture and devices appropriate for learning. In this way, collecting home environment data and providing advice to optimize the learning environment at home improves students' learning outcomes.

[0037] The AI ​​Tutoring Department can analyze students' interests and propose study plans based on them. For example, the AI ​​Tutoring Department will develop an AI tutor in which a generative AI analyzes a student's interests and proposes a study plan based on them. For example, it will provide learning materials related to topics of interest. The AI ​​Tutoring Department will also develop an AI tutor in which a generative AI analyzes a student's interests in real time and adjusts the study plan. For example, it will suggest tasks related to fields of interest. The AI ​​Tutoring Department will also build a system in which a generative AI develops an AI tutor in which a generative AI proposes an individual study plan based on a student's interests and proposes a project related to a topic of interest. In this way, by analyzing a student's interests and proposing a study plan based on them, it will increase the student's motivation to learn.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The Infrastructure Development Department can collect local weather data and propose weather-appropriate infrastructure development plans. For example, in areas where heavy rain or typhoons are expected, the department can recommend installing durable internet lines and devices. The Infrastructure Development Department can also use weather data to adjust infrastructure development schedules in response to weather fluctuations. For example, it can postpone work on days when bad weather is predicted and concentrate work on days when the weather is stable. The Infrastructure Development Department can also monitor weather data in real time and update infrastructure development plans as needed. For example, it can quickly decide to suspend or resume work in response to sudden changes in weather. This makes it possible to utilize local weather data to develop infrastructure efficiently and safely.

[0040] The data collection unit can monitor internet usage at home and suggest optimal study times. For example, it can identify times when internet usage at home is low and concentrate online classes during those times. The data collection unit can also adjust study times in real time based on internet usage at home. For example, it can suggest changing study times if there is a sudden increase in internet usage at home. The data collection unit can also analyze internet usage at home and provide advice to provide an optimal learning environment. For example, it can suggest schedule adjustments to avoid times when internet usage is concentrated. This improves students' learning efficiency by suggesting optimal study times that take into account internet usage at home.

[0041] The AI ​​tutoring department can provide customizable learning plans according to each student's learning style. For example, for students who prefer visual learning, it can provide learning materials that make extensive use of diagrams and graphs. The AI ​​tutoring department can also analyze students' learning styles in real time and adjust the learning plan accordingly. For example, for students who prefer auditory learning, it can provide learning materials with audio commentary. The AI ​​tutoring department can also build a system that proposes learning plans according to each student's learning style. For example, for students who prefer practical learning, it can provide experiments and project-based assignments. This maximizes learning effectiveness by providing customizable learning plans according to each student's learning style.

[0042] The Infrastructure Development Department can propose infrastructure development plans that take into account local culture and customs. For example, it can adjust infrastructure development schedules to accommodate periods when there are many local festivals and events. The Infrastructure Development Department can also customize infrastructure development methods based on local culture and customs. For example, it can propose device installation methods that match the traditional architectural style of the area. The Infrastructure Development Department can also adjust infrastructure development plans in real time while respecting local culture and customs. For example, it can emphasize communication with local residents and propose measures to gain their understanding and cooperation regarding infrastructure development. This allows for smooth infrastructure development by proposing infrastructure development plans that take into account local culture and customs.

[0043] The Infrastructure Development Department can propose eco-friendly infrastructure development plans to protect the local natural environment. For example, introducing an energy supply system that utilizes solar power generation or wind power generation. The Infrastructure Development Department can also propose device installation methods that take the local natural environment into consideration. For example, installing devices so as not to damage the natural landscape. The Infrastructure Development Department can also adjust eco-friendly infrastructure development plans to protect the local natural environment in real time. For example, proposing measures to minimize environmental impact. This makes it possible to develop eco-friendly infrastructure while protecting the local natural environment.

[0044] The Infrastructure Development Department can utilize regional disaster prevention data to propose infrastructure restoration plans in the event of a disaster. For example, they can create plans to quickly restore internet lines and devices in the event of a disaster such as an earthquake or typhoon. The Infrastructure Development Department can also use disaster prevention data to determine priorities for infrastructure maintenance in the event of a disaster. For example, they can prioritize restoring internet connections to evacuation centers and important facilities. The Infrastructure Development Department can also monitor disaster prevention data in real time and build systems to respond quickly in the event of a disaster. For example, they can introduce a system that automatically executes restoration plans when a disaster occurs. This will enable the rapid restoration of infrastructure in the event of a disaster by utilizing regional disaster prevention data.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The Infrastructure Development Department will prepare internet connections and devices. For example, they will install high-speed internet lines in remote schools and provide tablets and laptops to each student. They will also optimize the network environment within the schools to provide a stable online learning environment. Step 2: The data collection unit collects students' learning data. For example, data on learning progress, grades, questions, and assignments is collected, and the AI ​​generates appropriate advice and learning plans based on this data. We also build a system that collects learning data in real time and provides immediate feedback. Step 3: The AI ​​tutoring unit provides a learning plan based on the data collected by the data collection unit. For example, the generation AI answers the student's questions and provides an individual learning plan. The individual learning plan is also updated in real time based on the student's learning history.

[0047] (Example 2) The school education support system according to an embodiment of the present invention is based on GIGA School and utilizes generative AI to compensate for the lack of educational resources, provides individual learning plans and advice to students, and provides a new forum for communication by connecting multiple small schools online. As a result, the school education support system can improve the quality of education in remote areas and support children's learning.

[0048] The school education support system according to the embodiment includes an infrastructure development unit, a data collection unit, and an AI tutor unit. The infrastructure development unit prepares internet connections and devices. For example, it installs high-speed internet lines in remote schools and distributes tablets and laptops to each student. The infrastructure development unit also optimizes the network environment within the school to provide a stable online learning environment. The data collection unit collects student learning data. For example, it collects data on learning progress, grades, questions, and assignments, and a generation AI generates appropriate advice and study plans based on this data. The data collection unit also builds a system that collects learning data in real time and provides immediate feedback. The AI ​​tutor unit provides study plans based on the data collected by the data collection unit. For example, the generation AI answers students' questions and provides individual study plans. The AI ​​tutor unit also continuously updates individual study plans in real time based on the students' learning histories. As a result, the school education support system according to the embodiment can compensate for a lack of educational resources, provide individual study plans and advice to students, and provide a new forum for communication by connecting multiple small schools online.

[0049] The Infrastructure Development Department can use generative AI to automatically generate optimal internet line placement plans and device distribution plans. For example, the Infrastructure Development Department uses generative AI to automatically generate optimal internet line placement plans taking into account each school's geographical conditions and student population. For example, the department proposes routes for efficiently introducing high-speed internet, even in remote areas such as mountainous regions and remote islands. The Infrastructure Development Department's generative AI also analyzes each student's home environment and learning style and automatically generates optimal device distribution plans based on that analysis. For example, the department provides individualized support, such as providing mobile routers to students who do not have internet access at home. The Infrastructure Development Department's generative AI also monitors local infrastructure conditions in real time and proposes strengthening internet lines and distributing additional devices as needed. For example, the department can immediately take countermeasures if communication speeds decrease in a specific area. This enables efficient infrastructure development by automatically generating optimal internet line placement plans and device distribution plans.

[0050] The data collection unit can build a system that collects students' learning data in real time and provides instant feedback. For example, the data collection unit builds a system in which a generative AI collects students' learning data in real time and provides instant feedback. For example, it instantly analyzes online test results and suggests specific areas for improvement to students. The data collection unit also monitors students' learning progress in real time and provides feedback as needed. For example, it suggests additional learning resources for students who appear to be falling behind in their studies. The data collection unit also develops a system in which a generative AI analyzes students' learning data in real time and provides instant feedback. For example, it provides individual learning advice based on assignment submission status and grades. In this way, learning data is collected in real time and instant feedback is provided, improving students' learning efficiency.

[0051] The AI ​​Tutoring Department can continuously update individual learning plans in real time based on a student's learning history. For example, the AI ​​Tutoring Department will develop an AI tutor in which the generating AI analyzes a student's learning history in real time and continuously updates the individual learning plan. For example, it will automatically suggest the next content to study based on the student's learning progress. The AI ​​Tutoring Department will also develop an AI tutor in which the generating AI adjusts the individual learning plan in real time based on the student's learning data. For example, if a weak point in a particular subject is found, it will provide additional practice problems. The AI ​​Tutoring Department will also build a system in which the generating AI analyzes a student's learning history and develops an AI tutor that continuously updates the individual learning plan in real time. For example, it will suggest appropriate teaching materials and resources based on the student's learning progress. This will maximize the student's learning effectiveness by continuously updating the individual learning plan in real time.

[0052] The infrastructure maintenance department can use the emotion estimation function to detect students' and faculty's anxieties and concerns about infrastructure maintenance in real time and provide appropriate support. For example, the infrastructure maintenance department uses the emotion estimation function to detect students' and faculty's anxieties and concerns about infrastructure maintenance in real time. For example, the infrastructure maintenance department can detect stress felt by students when their internet connection is unstable and immediately take countermeasures. The infrastructure maintenance department also uses the emotion estimation function to detect faculty's concerns about infrastructure maintenance and provide appropriate support. For example, if a faculty member is anxious about operating a new device, the infrastructure maintenance department can provide online support explaining how to operate it. The infrastructure maintenance department also uses the emotion estimation function to monitor fluctuations in students' and faculty's emotions about infrastructure maintenance and strengthen support as necessary. For example, the infrastructure maintenance department can provide counseling to reduce the anxiety felt by students when their internet connection is unstable. This enables smooth infrastructure maintenance by detecting anxieties and concerns about infrastructure maintenance in real time and providing appropriate support.

[0053] The data collection unit can use the emotion estimation function to detect emotional fluctuations from students' learning data and provide advice to increase their motivation to learn. For example, the data collection unit uses the emotion estimation function to detect emotional fluctuations from students' learning data and provide advice to increase their motivation to learn. For example, the data collection unit suggests relaxation methods to students who feel stressed while studying. The data collection unit also uses the emotion estimation function to monitor emotional fluctuations from students' learning data in real time and provide advice as needed. For example, the data collection unit sends a motivating message to students whose motivation to study is declining. The data collection unit also uses the emotion estimation function to analyze emotional fluctuations from students' learning data and provide specific advice to increase their motivation to study. For example, the data collection unit sends messages of praise or encouragement according to their learning progress. In this way, the data collection unit can detect emotional fluctuations from students' learning data and provide advice to increase their motivation to study, thereby improving students' learning effectiveness.

[0054] The AI ​​tutor unit can use the emotion estimation function to suggest a learning approach that suits the student's emotional state. For example, the AI ​​tutor unit will use the emotion estimation function to develop an AI tutor that suggests a learning approach that suits the student's emotional state. For example, it will suggest relaxation methods for students who are feeling stressed. The AI ​​tutor unit will also use the emotion estimation function to develop an AI tutor that analyzes students' emotional data in real time and adjusts their learning approach. For example, it will suggest more difficult tasks when positive emotions are strong. The AI ​​tutor unit will also build a system that uses the emotion estimation function to develop an AI tutor that suggests a learning approach that suits the student's emotional state. For example, it will adjust the learning content according to emotional fluctuations. In this way, by suggesting a learning approach that suits the student's emotional state, it will increase students' motivation to learn and improve their learning effectiveness.

[0055] The Infrastructure Development Department can analyze the local power supply situation and propose the optimal energy management plan. For example, the generation AI in the Infrastructure Development Department analyzes the local power supply situation in real time and proposes the optimal energy management plan. For example, in areas with unstable power supply, it recommends the introduction of solar power generation and storage batteries. The generation AI in the Infrastructure Development Department also collects school power consumption data and proposes an efficient energy management plan. For example, it suggests adjusting schedules to reduce power consumption during peak hours and introducing energy-saving devices. The generation AI in the Infrastructure Development Department also integrates the local power supply situation with the school's power consumption data and automatically generates the optimal energy management plan. For example, it makes suggestions such as concentrating online classes during times when the power supply is stable. In this way, efficient energy use is possible by analyzing the local power supply situation and proposing the optimal energy management plan.

[0056] The AI ​​tutoring department can develop multiple AI tutors specializing in different subjects or topics, allowing students to choose from them. For example, the generating AI can develop multiple AI tutors specializing in different subjects or topics, allowing students to choose from them. For example, it can provide specialized tutors for mathematics, science, history, etc. The AI ​​tutoring department can also develop AI tutors specializing in each subject or topic, allowing students to choose from them according to their learning needs. For example, it can provide tutors that delve deeply into specific topics. The AI ​​tutoring department can also develop AI tutors specializing in different subjects or topics, creating a system that allows students to freely choose from them. For example, it can suggest appropriate tutors based on learning progress. This allows students to choose from AI tutors specializing in different subjects or topics, allowing them to choose from them according to their learning needs.

[0057] The infrastructure development department can plan optimal device distribution routes taking into account local traffic conditions. For example, the generation AI in the infrastructure development department analyzes local traffic conditions in real time and plans optimal device distribution routes. For example, it proposes routes to distribute devices during times of light traffic congestion. The generation AI in the infrastructure development department also integrates local geographic information and traffic data to automatically generate efficient device distribution routes. For example, it proposes the shortest route for schools in remote locations, shortening distribution time. The generation AI in the infrastructure development department also monitors local traffic conditions and adjusts device distribution routes in real time as needed. For example, it proposes alternative routes to avoid traffic accidents or congestion caused by construction. This enables efficient device distribution by planning optimal device distribution routes taking traffic conditions into account.

[0058] The data collection unit can collect home environment data and provide advice to optimize the learning environment at home. For example, the data collection unit allows the generating AI to collect home environment data and provide advice to optimize the learning environment at home. For example, it suggests ensuring a quiet study space and using appropriate lighting. The data collection unit also allows the generating AI to monitor home environment data in real time and suggest measures to improve the learning environment as needed. For example, it suggests soundproofing measures if the noise level in the home is high. The data collection unit also allows the generating AI to analyze home environment data and provide specific advice to optimize the learning environment at home. For example, it suggests the placement of furniture and devices appropriate for learning. In this way, collecting home environment data and providing advice to optimize the learning environment at home improves students' learning outcomes.

[0059] The AI ​​Tutoring Department can analyze students' interests and propose study plans based on them. For example, the AI ​​Tutoring Department will develop an AI tutor in which a generative AI analyzes a student's interests and proposes a study plan based on them. For example, it will provide learning materials related to topics of interest. The AI ​​Tutoring Department will also develop an AI tutor in which a generative AI analyzes a student's interests in real time and adjusts the study plan. For example, it will suggest tasks related to fields of interest. The AI ​​Tutoring Department will also build a system in which a generative AI develops an AI tutor in which a generative AI proposes an individual study plan based on a student's interests and proposes a project related to a topic of interest. In this way, by analyzing a student's interests and proposing a study plan based on them, it will increase the student's motivation to learn.

[0060] The infrastructure development department can use the emotion estimation function to analyze local residents' emotions regarding infrastructure development and propose measures to promote cooperation throughout the community. For example, the infrastructure development department uses the emotion estimation function to analyze local residents' emotions regarding infrastructure development in real time. For example, it detects residents' anxieties and expectations regarding the introduction of internet lines and provides appropriate information. The infrastructure development department also uses the emotion estimation function to propose measures to promote cooperation throughout the community based on the emotion data of local residents. For example, it holds information sessions and workshops to alleviate residents' anxieties. The infrastructure development department also uses the emotion estimation function to monitor fluctuations in local residents' emotions regarding infrastructure development and adjust measures as necessary. For example, it provides additional support if residents' anxieties increase. This enables smooth infrastructure development by analyzing local residents' emotions regarding infrastructure development and proposing measures to promote cooperation throughout the community.

[0061] The data collection unit can use the emotion estimation function to analyze emotional fluctuations in the student's home environment and propose study support measures at home. The data collection unit, for example, uses the emotion estimation function to analyze emotional fluctuations in the student's home environment in real time and propose study support measures at home. For example, it suggests relaxation methods when stress is increasing at home. The data collection unit also uses the emotion estimation function to collect emotional data in the student's home environment and provide study support measures. For example, it suggests adjustments to study time or rest in accordance with emotional fluctuations at home. The data collection unit also uses the emotion estimation function to analyze emotional fluctuations in the student's home environment and propose specific study support measures at home. For example, it suggests communication methods to elicit positive emotions at home. In this way, by analyzing emotional fluctuations in the home environment and proposing study support measures at home, the student's learning effectiveness is improved.

[0062] The AI ​​tutoring department can use the emotion estimation function to suggest learning approaches that match the student's interests and concerns. For example, the AI ​​tutoring department uses the emotion estimation function to develop an AI tutor that suggests learning approaches that match the student's interests and concerns. For example, it provides learning materials related to topics of interest. The AI ​​tutoring department also uses the emotion estimation function to analyze students' emotional data in real time and develop an AI tutor that adjusts learning approaches that match the student's interests and concerns. For example, it suggests tasks related to fields of interest. The AI ​​tutoring department also builds a system that uses the emotion estimation function to develop an AI tutor that suggests learning approaches that match the student's interests and concerns. For example, it suggests projects related to topics of interest. In this way, by suggesting learning approaches that match the student's interests and concerns, it increases the student's motivation to learn and improves their learning effectiveness.

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

[0064] The Infrastructure Development Department can collect local weather data and propose weather-appropriate infrastructure development plans. For example, in areas where heavy rain or typhoons are expected, the department can recommend installing durable internet lines and devices. The Infrastructure Development Department can also use weather data to adjust infrastructure development schedules in response to weather fluctuations. For example, it can postpone work on days when bad weather is predicted and concentrate work on days when the weather is stable. The Infrastructure Development Department can also monitor weather data in real time and update infrastructure development plans as needed. For example, it can quickly decide to suspend or resume work in response to sudden changes in weather. This makes it possible to utilize local weather data to develop infrastructure efficiently and safely.

[0065] The data collection unit can monitor internet usage at home and suggest optimal study times. For example, it can identify times when internet usage at home is low and concentrate online classes during those times. The data collection unit can also adjust study times in real time based on internet usage at home. For example, it can suggest changing study times if there is a sudden increase in internet usage at home. The data collection unit can also analyze internet usage at home and provide advice to provide an optimal learning environment. For example, it can suggest schedule adjustments to avoid times when internet usage is concentrated. This improves students' learning efficiency by suggesting optimal study times that take into account internet usage at home.

[0066] The AI ​​tutoring department can provide customizable learning plans according to each student's learning style. For example, for students who prefer visual learning, it can provide learning materials that make extensive use of diagrams and graphs. The AI ​​tutoring department can also analyze students' learning styles in real time and adjust the learning plan accordingly. For example, for students who prefer auditory learning, it can provide learning materials with audio commentary. The AI ​​tutoring department can also build a system that proposes learning plans according to each student's learning style. For example, for students who prefer practical learning, it can provide experiments and project-based assignments. This maximizes learning effectiveness by providing customizable learning plans according to each student's learning style.

[0067] The Infrastructure Development Department can propose infrastructure development plans that take into account local culture and customs. For example, it can adjust infrastructure development schedules to accommodate periods when there are many local festivals and events. The Infrastructure Development Department can also customize infrastructure development methods based on local culture and customs. For example, it can propose device installation methods that match the traditional architectural style of the area. The Infrastructure Development Department can also adjust infrastructure development plans in real time while respecting local culture and customs. For example, it can emphasize communication with local residents and propose measures to gain their understanding and cooperation regarding infrastructure development. This allows for smooth infrastructure development by proposing infrastructure development plans that take into account local culture and customs.

[0068] The data collection unit can use the emotion estimation function to detect emotional fluctuations in students while they are studying and suggest appropriate break times. For example, it can suggest a short break to a student whose concentration is declining. The data collection unit can also use the emotion estimation function to monitor students' emotional data in real time and adjust break timing as needed. For example, it can suggest relaxation methods to a student who is experiencing increasing stress. The data collection unit can also use the emotion estimation function to build a system that analyzes students' emotional fluctuations and suggests optimal break timing. For example, it can advise students to take breaks at appropriate times depending on their learning progress. In this way, learning efficiency is improved by using the emotion estimation function to detect emotional fluctuations in students while they are studying and suggest appropriate break times.

[0069] The AI ​​tutor unit can use the emotion estimation function to provide feedback according to the student's emotional state. For example, when positive emotions are strong, it can send praise or encouraging messages. The AI ​​tutor unit can also use the emotion estimation function to analyze the student's emotional data in real time and adjust the feedback accordingly. For example, when negative emotions are strong, it can provide advice on relaxation methods and stress relief. The AI ​​tutor unit can also use the emotion estimation function to build a system that provides feedback according to the student's emotional state. For example, it can provide feedback at the appropriate time depending on emotional fluctuations. In this way, using the emotion estimation function to provide feedback according to the student's emotional state can increase the student's motivation to learn and improve their learning effectiveness.

[0070] The Infrastructure Development Department can propose eco-friendly infrastructure development plans to protect the local natural environment. For example, introducing an energy supply system that utilizes solar power generation or wind power generation. The Infrastructure Development Department can also propose device installation methods that take the local natural environment into consideration. For example, installing devices so as not to damage the natural landscape. The Infrastructure Development Department can also adjust eco-friendly infrastructure development plans to protect the local natural environment in real time. For example, proposing measures to minimize environmental impact. This makes it possible to develop eco-friendly infrastructure while protecting the local natural environment.

[0071] The data collection unit can use the emotion estimation function to detect emotional fluctuations of students while they are studying and adjust the learning content accordingly. For example, it can provide relaxing content to students who are experiencing high levels of stress. The data collection unit can also use the emotion estimation function to monitor students' emotional data in real time and adjust the learning content as needed. For example, it can provide more difficult tasks when students have strong positive emotions. The data collection unit can also use the emotion estimation function to build a system that analyzes students' emotional fluctuations and adjusts the learning content accordingly. For example, it can suggest appropriate learning content depending on the emotional fluctuations. In this way, the learning effect can be maximized by using the emotion estimation function to detect emotional fluctuations of students while they are studying and adjust the learning content accordingly.

[0072] The Infrastructure Development Department can utilize regional disaster prevention data to propose infrastructure restoration plans in the event of a disaster. For example, they can create plans to quickly restore internet lines and devices in the event of a disaster such as an earthquake or typhoon. The Infrastructure Development Department can also use disaster prevention data to determine priorities for infrastructure maintenance in the event of a disaster. For example, they can prioritize restoring internet connections to evacuation centers and important facilities. The Infrastructure Development Department can also monitor disaster prevention data in real time and build systems to respond quickly in the event of a disaster. For example, they can introduce a system that automatically executes restoration plans when a disaster occurs. This will enable the rapid restoration of infrastructure in the event of a disaster by utilizing regional disaster prevention data.

[0073] The AI ​​tutor unit can use the emotion estimation function to provide learning resources according to a student's emotional state. For example, it can provide relaxing videos or music to a student who is experiencing high stress. The AI ​​tutor unit can also use the emotion estimation function to analyze a student's emotional data in real time and adjust learning resources. For example, it can provide challenging tasks when a student is feeling strongly positive. The AI ​​tutor unit can also use the emotion estimation function to build a system that provides learning resources according to a student's emotional state. For example, it can suggest appropriate learning resources according to emotional fluctuations. In this way, using the emotion estimation function to provide learning resources according to a student's emotional state increases students' motivation to learn and improves their learning effectiveness.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The Infrastructure Development Department will prepare internet connections and devices. For example, they will install high-speed internet lines in remote schools and provide tablets and laptops to each student. They will also optimize the network environment within the schools to provide a stable online learning environment. Step 2: The data collection unit collects students' learning data. For example, data on learning progress, grades, questions, and assignments is collected, and the AI ​​generates appropriate advice and learning plans based on this data. We also build a system that collects learning data in real time and provides immediate feedback. Step 3: The AI ​​tutoring unit provides a learning plan based on the data collected by the data collection unit. For example, the generation AI answers the student's questions and provides an individual learning plan. The individual learning plan is also updated in real time based on the student's learning history.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0089] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0104] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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]

[0143] 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. The Infrastructure Development Department, which handles internet connections and device maintenance, and a data collection unit that collects learning data of students; an AI tutor unit that provides a learning plan based on the data collected by the data collection unit; A system characterized by:

2. The Infrastructure Development Department: Using generative AI, optimal internet line layout plans and device distribution plans are automatically generated.

2. The system of claim 1.

3. The data collection unit Build a system to collect the learning data of the students in real time and provide immediate feedback.

2. The system of claim 1.

4. The AI ​​tutor unit: The individual learning plan is updated in real time based on the student's learning history.

2. The system of claim 1.

5. The Infrastructure Development Department: Detecting student and faculty concerns about infrastructure in real time and providing appropriate support 2. The system of claim 1.

6. The data collection unit Detecting emotional fluctuations from the learning data of the student and providing advice to increase motivation to study 2. The system of claim 1.

7. The AI ​​tutor unit: Suggest learning approaches that correspond to the student's emotional state 2. The system of claim 1.

8. The Infrastructure Development Department: Analyzing the local power supply situation and proposing the optimal energy management plan 2. The system of claim 1.

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