System

Abstract

An object of the system according to the embodiment is to solve the problems of geographical restrictions and access and provide an opportunity to receive education.SOLUTION: A system according to an embodiment includes an Internet environment arrangement part and an education provision part. The Internet environment arrangement unit arranges an Internet environment. The education providing unit provides education using the robot.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] Previous technology had the problem of limited educational opportunities due to geographical constraints and access issues.

[0005] The system according to the embodiment aims to provide opportunities to receive education by eliminating geographical restrictions and access issues. [Means for solving the problem]

[0006] The system according to the embodiment includes an internet environment creation unit and an education providing unit. The internet environment creation unit creates an internet environment. The education providing unit provides education using a robot. [Effects of the Invention]

[0007] The system according to the embodiment can provide opportunities to receive education by eliminating geographical restrictions and access issues. [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) An education support system according to an embodiment of the present invention is a system that supports children and young people around the world to receive a high-quality education by overcoming geographical constraints and access issues. As a result, the education support system overcomes geographical constraints and access issues, allowing children and young people around the world to receive a high-quality education.

[0029] An education support system according to an embodiment includes an internet environment development unit and an education provision unit. The internet environment development unit develops an internet environment. For example, satellite internet can be introduced to provide internet connectivity even in areas without terrestrial infrastructure. It can also expand mobile networks to provide high-speed internet connectivity in more areas. Furthermore, public Wi-Fi hotspots can be installed to provide free internet connectivity to public facilities such as schools, libraries, and community centers. The education provision unit provides education using robots. For example, educational robots can be introduced into schools and homes to provide individualized instruction and supplementary lessons to children. Robots can also be used to connect teachers and students in remote locations, enabling real-time lessons. Furthermore, robots can be used to provide individual educational programs for children with special needs. This allows the education support system according to an embodiment to overcome geographical constraints and access issues, enabling children and young people around the world to receive high-quality education.

[0030] The Internet Environment Development Department can introduce satellite internet, making it possible for people to receive education via the internet even in mountainous or remote island areas with limited access. For example, when introducing satellite internet, the Internet Environment Development Department uses generative AI to analyze geographic conditions and population distribution and calculate the optimal satellite constellation in real time. This maximizes connection quality and improves access to educational resources. Generative AI can also be used to adjust satellite orbits and positions in real time to optimize connection quality. For example, satellite constellation can be dynamically changed in response to changes in weather and terrain. Furthermore, generative AI can be used to build a system that monitors and optimizes connection quality. For example, connection speed and latency can be analyzed in real time to propose the optimal satellite constellation. This makes it possible for people to receive education via the internet even in mountainous or remote island areas with limited access.

[0031] The Internet Environment Development Department can expand mobile networks and provide stable Internet connections not only in urban areas but also in rural and suburban areas. For example, the Internet Environment Development Department analyzes demographic data for each region to identify areas with the highest demand. For example, it determines network deployment priorities based on population density, age groups, and the distribution of educational institutions. It also collects demographic data in real time and dynamically adjusts network expansion plans in response to fluctuations in demand. For example, it can expand networks in response to the construction of new residential areas and schools. Furthermore, it calculates the optimal placement of base stations based on demographic data for each region and maximizes network coverage. For example, it prioritizes the installation of base stations in densely populated areas. This makes it possible to provide stable Internet connections not only in urban areas but also in rural and suburban areas.

[0032] The Internet Environment Improvement Department can install public Wi-Fi spots so that children who do not have internet access at home can access educational resources. For example, when installing public Wi-Fi spots, the Internet Environment Improvement Department uses an emotion estimation function to monitor user satisfaction in real time. For example, it analyzes facial expressions and voices when using Wi-Fi and calculates a satisfaction score. It also builds a system that dynamically adjusts the placement of Wi-Fi spots based on the emotion estimation data. For example, it can install additional Wi-Fi spots in areas with low satisfaction. Furthermore, when installing public Wi-Fi spots, it uses the emotion estimation function to collect user feedback and optimize the placement. For example, it can prioritize coverage of areas with high user satisfaction. This allows children who do not have internet access at home to access educational resources.

[0033] By introducing educational robots, the education delivery department can provide high-quality education even in areas where there is a shortage of teachers. For example, the education delivery department can equip the educational robot with generative AI to analyze each student's learning progress in real time. For example, it can automatically generate an optimal learning plan based on test results and learning history. It can also use generative AI to analyze students' learning data and provide customized learning plans tailored to their individual learning needs. For example, it can create plans that focus on weak subjects or areas. Furthermore, the educational robot can use generative AI to monitor students' learning progress in real time and dynamically adjust the learning plan as needed. For example, it can suggest supplementary lessons if progress is falling behind. This makes it possible to provide high-quality education even in areas where there is a shortage of teachers.

[0034] The education delivery department supports distance learning, allowing students to receive direct instruction from excellent educators regardless of geographical constraints. For example, the education delivery department uses generative AI to automatically translate teachers' instructional content in real time, accommodating students who speak different languages. For example, English lessons can be translated into Japanese or Spanish. Furthermore, in support of distance learning, generative AI can be used to translate teachers' instructional content into multiple languages, creating an international classroom. For example, educational robots equipped with simultaneous interpretation functions can be introduced. Furthermore, generative AI can be used to automatically translate teachers' instructional content, providing consistent education to students who speak different languages. For example, a system can be built to translate lesson materials and texts into multiple languages. This allows students to receive direct instruction from excellent educators regardless of geographical constraints.

[0035] The education provision unit provides special needs education, allowing children with disabilities and learning difficulties to receive an appropriate education. For example, in special needs education, the education provision unit uses an emotion estimation function to monitor students' emotional states in real time. For example, it analyzes facial expressions and voice to detect stress and anxiety. Furthermore, based on the emotion estimation data, it builds a system that provides appropriate support to students who require special assistance. For example, it can suggest counseling or relaxation programs depending on the emotional state. Furthermore, in special needs education, the emotion estimation function is used to monitor students' emotional states in real time and dynamically adjust support plans as needed. For example, it provides additional support if the emotional state worsens. This allows children with disabilities and learning difficulties to receive an appropriate education.

[0036] The Internet Environment Development Department can use generative AI to calculate the optimal satellite constellation in real time, maximizing connection quality. For example, when introducing satellite internet, the Internet Environment Development Department uses generative AI to analyze geographical conditions and population distribution and calculate the optimal satellite constellation in real time. This maximizes connection quality and improves access to educational resources. Generative AI can also be used to adjust satellite orbits and positions in real time to optimize connection quality. For example, satellite constellation can be dynamically changed in response to changes in weather and terrain. Furthermore, generative AI can be used to build a system that monitors and optimizes connection quality. For example, connection speed and latency can be analyzed in real time and the optimal satellite constellation proposed. This allows the optimal satellite constellation to be calculated in real time, maximizing connection quality.

[0037] The Internet environment development unit can analyze demographic data and prioritize network deployment in areas with the highest demand. For example, the Internet environment development unit analyzes demographic data for each region to identify areas with the highest demand. For example, it determines the priority of network deployment based on population density, age groups, and the distribution of educational institutions. It also collects demographic data in real time and dynamically adjusts network expansion plans in response to fluctuations in demand. For example, it can expand the network in response to the construction of new residential areas or schools. Furthermore, it calculates the optimal placement of base stations based on the demographic data for each region and maximizes network coverage. For example, it prioritizes the installation of base stations in densely populated areas. This allows the network to be deployed preferentially in areas with the highest demand.

[0038] The education provision unit can use generative AI to analyze each student's learning progress in real time and provide an optimal learning plan. For example, the education provision unit can install generative AI in an educational robot and analyze each student's learning progress in real time. For example, it can automatically generate an optimal learning plan based on test results and learning history. It can also use generative AI to analyze students' learning data and provide customized learning plans tailored to their individual learning needs. For example, it can create plans that focus on weak subjects or areas. Furthermore, the educational robot can use generative AI to monitor each student's learning progress in real time and dynamically adjust the learning plan as needed. For example, it can suggest a supplementary lesson plan if progress is falling behind. This makes it possible to analyze each student's learning progress in real time and provide an optimal learning plan.

[0039] The education provision unit can use generative AI to automatically translate teacher instruction content to accommodate students who speak different languages. For example, the education provision unit can use generative AI to automatically translate teacher instruction content in real time to accommodate students who speak different languages. For example, translating English lessons into Japanese or Spanish. In addition, to support distance education, generative AI can be used to translate teacher instruction content into multiple languages ​​to realize an international classroom. For example, educational robots equipped with simultaneous interpretation functions can be introduced. Furthermore, generative AI can be used to automatically translate teacher instruction content to provide consistent education to students who speak different languages. For example, a system can be built to translate lesson materials and texts into multiple languages. This allows teacher instruction content to be automatically translated to accommodate students who speak different languages.

[0040] The Internet Environment Development Department can design satellite Internet infrastructure as a multi-use system that can be used not only in education but also in other fields such as agriculture and medicine. For example, it can be used to collect agricultural data or support remote medical care. Furthermore, when introducing satellite Internet, the infrastructure is built taking into account the needs of the agricultural and medical fields. For example, agricultural weather data and medical patient data can be collected and distributed in real time. Furthermore, the satellite Internet infrastructure is designed for multi-purpose use and can be used in fields other than education. For example, a system supporting remote agricultural monitoring and remote medical diagnosis can be built. This allows the satellite Internet infrastructure to be designed as a multi-use system that can be used not only in education but also in other fields such as agriculture and medicine.

[0041] When expanding a mobile network, the Internet environment development unit can design it so that it can also be used as an emergency communication means in the event of a disaster. For example, when expanding a mobile network, the Internet environment development unit can design it so that it can also be used as an emergency communication means in the event of a disaster. For example, a protocol can be introduced to ensure priority communication when a disaster occurs. In addition, the redundancy of the mobile network can be increased so that it can be used as an emergency communication means in the event of a disaster. For example, multiple communication routes can be secured so that communication will not be interrupted even if part of the network is damaged. Furthermore, when expanding a mobile network, its function as an emergency communication means in the event of a disaster can be strengthened. For example, a system that automatically sends emergency messages in the event of a disaster can be introduced. In this way, when expanding a mobile network, it can be designed so that it can also be used as an emergency communication means in the event of a disaster.

[0042] The education provision unit can design the educational robot so that it can be used not only for learning support at home but also for rehabilitation and nursing care support. For example, the education provision unit designs the educational robot so that it can be used not only for learning support at home but also for rehabilitation and nursing care support. For example, it can be equipped with a program that supports the recovery of motor functions. Furthermore, rehabilitation and nursing care support functions can be added to the educational robot so that it can be used for multiple purposes. For example, it can provide support for elderly people's daily lives and rehabilitation. Furthermore, the educational robot can be made multifunctional so that it can support rehabilitation and nursing care support in addition to learning support at home. For example, it can be equipped with programs that perform health management and exercise instruction. In this way, the educational robot can be designed so that it can be used not only for learning support at home but also for rehabilitation and nursing care support.

[0043] The Educational Provision Department will be able to support distance learning and apply it to corporate training and professional training, meeting a wide range of educational needs. For example, the Educational Provision Department will build a system that can be applied to corporate training and professional training in support of distance learning. For example, it will provide online training for employees working remotely. It will also use generative AI to automatically generate corporate training and professional training programs and apply them to support distance learning. For example, it will be able to provide technical training and management training. Furthermore, it will develop a system that can also support corporate training and professional training in support of distance learning. For example, it will provide online specialized training for medical professionals. This will allow it to be applied to corporate training and professional training in support of distance learning, meeting a wide range of educational needs.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The education provision department can use generative AI to provide customized learning plans tailored to each student's learning style. For example, for students who prefer visual learning, a plan that makes heavy use of visual content can be provided. Generative AI can also be used to analyze students' learning data and suggest optimal learning methods. For example, audio materials can be provided for students who prefer auditory learning. Furthermore, generative AI can be used to build a system that provides customized learning plans tailored to each student's learning style. For example, experiments and project-based learning can be suggested for students who prefer practical learning. This makes it possible to provide optimal learning plans tailored to each student's learning style.

[0046] The education delivery department can use generative AI to automatically translate teacher instruction to accommodate students who speak different languages. For example, translating English lessons into Japanese or Spanish. To support distance learning, generative AI can also be used to translate teacher instruction into multiple languages, creating an international classroom. For example, educational robots equipped with simultaneous interpretation functions can be introduced. Generative AI can also be used to automatically translate teacher instruction to provide consistent education to students who speak different languages. For example, a system can be built to translate lesson materials and texts into multiple languages. This allows teacher instruction to be automatically translated to accommodate students who speak different languages.

[0047] The education delivery department can use generative AI to automatically translate teacher instruction to accommodate students who speak different languages. For example, translating English lessons into Japanese or Spanish. To support distance learning, generative AI can also be used to translate teacher instruction into multiple languages, creating an international classroom. For example, educational robots equipped with simultaneous interpretation functions can be introduced. Generative AI can also be used to automatically translate teacher instruction to provide consistent education to students who speak different languages. For example, a system can be built to translate lesson materials and texts into multiple languages. This allows teacher instruction to be automatically translated to accommodate students who speak different languages.

[0048] The education delivery department can use generative AI to automatically translate teacher instruction to accommodate students who speak different languages. For example, translating English lessons into Japanese or Spanish. To support distance learning, generative AI can also be used to translate teacher instruction into multiple languages, creating an international classroom. For example, educational robots equipped with simultaneous interpretation functions can be introduced. Generative AI can also be used to automatically translate teacher instruction to provide consistent education to students who speak different languages. For example, a system can be built to translate lesson materials and texts into multiple languages. This allows teacher instruction to be automatically translated to accommodate students who speak different languages.

[0049] The education delivery department can use generative AI to automatically translate teacher instruction to accommodate students who speak different languages. For example, translating English lessons into Japanese or Spanish. To support distance learning, generative AI can also be used to translate teacher instruction into multiple languages, creating an international classroom. For example, educational robots equipped with simultaneous interpretation functions can be introduced. Generative AI can also be used to automatically translate teacher instruction to provide consistent education to students who speak different languages. For example, a system can be built to translate lesson materials and texts into multiple languages. This allows teacher instruction to be automatically translated to accommodate students who speak different languages.

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

[0051] Step 1: The Internet Environment Development Department will develop the Internet environment. For example, they can introduce satellite Internet to provide Internet access in areas without terrestrial infrastructure. They can also expand mobile networks to provide high-speed Internet access to more areas. They can also set up public Wi-Fi hotspots to provide free Internet access to public facilities such as schools, libraries, and community centers. Step 2: The Educational Services Department uses robots to provide education. For example, educational robots can be introduced into schools and homes to provide individualized instruction and supplementary lessons to children. Robots can also be used to connect teachers and students in remote locations, enabling real-time lessons. Furthermore, robots can be used to provide individual educational programs for children with special needs.

[0052] (Example 2) An education support system according to an embodiment of the present invention is a system that supports children and young people around the world to receive a high-quality education by overcoming geographical constraints and access issues. As a result, the education support system overcomes geographical constraints and access issues, allowing children and young people around the world to receive a high-quality education.

[0053] An education support system according to an embodiment includes an internet environment development unit and an education provision unit. The internet environment development unit develops an internet environment. For example, satellite internet can be introduced to provide internet connectivity even in areas without terrestrial infrastructure. It can also expand mobile networks to provide high-speed internet connectivity in more areas. Furthermore, public Wi-Fi hotspots can be installed to provide free internet connectivity to public facilities such as schools, libraries, and community centers. The education provision unit provides education using robots. For example, educational robots can be introduced into schools and homes to provide individualized instruction and supplementary lessons to children. Robots can also be used to connect teachers and students in remote locations, enabling real-time lessons. Furthermore, robots can be used to provide individual educational programs for children with special needs. This allows the education support system according to an embodiment to overcome geographical constraints and access issues, enabling children and young people around the world to receive high-quality education.

[0054] The Internet Environment Development Department can introduce satellite internet, making it possible for people to receive education via the internet even in mountainous or remote island areas with limited access. For example, when introducing satellite internet, the Internet Environment Development Department uses generative AI to analyze geographic conditions and population distribution and calculate the optimal satellite constellation in real time. This maximizes connection quality and improves access to educational resources. Generative AI can also be used to adjust satellite orbits and positions in real time to optimize connection quality. For example, satellite constellation can be dynamically changed in response to changes in weather and terrain. Furthermore, generative AI can be used to build a system that monitors and optimizes connection quality. For example, connection speed and latency can be analyzed in real time to propose the optimal satellite constellation. This makes it possible for people to receive education via the internet even in mountainous or remote island areas with limited access.

[0055] The Internet Environment Development Department can expand mobile networks and provide stable Internet connections not only in urban areas but also in rural and suburban areas. For example, the Internet Environment Development Department analyzes demographic data for each region to identify areas with the highest demand. For example, it determines network deployment priorities based on population density, age groups, and the distribution of educational institutions. It also collects demographic data in real time and dynamically adjusts network expansion plans in response to fluctuations in demand. For example, it can expand networks in response to the construction of new residential areas and schools. Furthermore, it calculates the optimal placement of base stations based on demographic data for each region and maximizes network coverage. For example, it prioritizes the installation of base stations in densely populated areas. This makes it possible to provide stable Internet connections not only in urban areas but also in rural and suburban areas.

[0056] The Internet Environment Improvement Department can install public Wi-Fi spots so that children who do not have internet access at home can access educational resources. For example, when installing public Wi-Fi spots, the Internet Environment Improvement Department uses an emotion estimation function to monitor user satisfaction in real time. For example, it analyzes facial expressions and voices when using Wi-Fi and calculates a satisfaction score. It also builds a system that dynamically adjusts the placement of Wi-Fi spots based on the emotion estimation data. For example, it can install additional Wi-Fi spots in areas with low satisfaction. Furthermore, when installing public Wi-Fi spots, it uses the emotion estimation function to collect user feedback and optimize the placement. For example, it can prioritize coverage of areas with high user satisfaction. This allows children who do not have internet access at home to access educational resources.

[0057] By introducing educational robots, the education delivery department can provide high-quality education even in areas where there is a shortage of teachers. For example, the education delivery department can equip the educational robot with generative AI to analyze each student's learning progress in real time. For example, it can automatically generate an optimal learning plan based on test results and learning history. It can also use generative AI to analyze students' learning data and provide customized learning plans tailored to their individual learning needs. For example, it can create plans that focus on weak subjects or areas. Furthermore, the educational robot can use generative AI to monitor students' learning progress in real time and dynamically adjust the learning plan as needed. For example, it can suggest supplementary lessons if progress is falling behind. This makes it possible to provide high-quality education even in areas where there is a shortage of teachers.

[0058] The education delivery department supports distance learning, allowing students to receive direct instruction from excellent educators regardless of geographical constraints. For example, the education delivery department uses generative AI to automatically translate teachers' instructional content in real time, accommodating students who speak different languages. For example, English lessons can be translated into Japanese or Spanish. Furthermore, in support of distance learning, generative AI can be used to translate teachers' instructional content into multiple languages, creating an international classroom. For example, educational robots equipped with simultaneous interpretation functions can be introduced. Furthermore, generative AI can be used to automatically translate teachers' instructional content, providing consistent education to students who speak different languages. For example, a system can be built to translate lesson materials and texts into multiple languages. This allows students to receive direct instruction from excellent educators regardless of geographical constraints.

[0059] The education provision unit provides special needs education, allowing children with disabilities and learning difficulties to receive an appropriate education. For example, in special needs education, the education provision unit uses an emotion estimation function to monitor students' emotional states in real time. For example, it analyzes facial expressions and voice to detect stress and anxiety. Furthermore, based on the emotion estimation data, it builds a system that provides appropriate support to students who require special assistance. For example, it can suggest counseling or relaxation programs depending on the emotional state. Furthermore, in special needs education, the emotion estimation function is used to monitor students' emotional states in real time and dynamically adjust support plans as needed. For example, it provides additional support if the emotional state worsens. This allows children with disabilities and learning difficulties to receive an appropriate education.

[0060] The Internet Environment Development Department can use generative AI to calculate the optimal satellite constellation in real time, maximizing connection quality. For example, when introducing satellite internet, the Internet Environment Development Department uses generative AI to analyze geographical conditions and population distribution and calculate the optimal satellite constellation in real time. This maximizes connection quality and improves access to educational resources. Generative AI can also be used to adjust satellite orbits and positions in real time to optimize connection quality. For example, satellite constellation can be dynamically changed in response to changes in weather and terrain. Furthermore, generative AI can be used to build a system that monitors and optimizes connection quality. For example, connection speed and latency can be analyzed in real time and the optimal satellite constellation proposed. This allows the optimal satellite constellation to be calculated in real time, maximizing connection quality.

[0061] The Internet environment development unit can analyze demographic data and prioritize network deployment in areas with the highest demand. For example, the Internet environment development unit analyzes demographic data for each region to identify areas with the highest demand. For example, it determines the priority of network deployment based on population density, age groups, and the distribution of educational institutions. It also collects demographic data in real time and dynamically adjusts network expansion plans in response to fluctuations in demand. For example, it can expand the network in response to the construction of new residential areas or schools. Furthermore, it calculates the optimal placement of base stations based on the demographic data for each region and maximizes network coverage. For example, it prioritizes the installation of base stations in densely populated areas. This allows the network to be deployed preferentially in areas with the highest demand.

[0062] The internet environment maintenance unit can use the emotion estimation function to monitor user satisfaction in real time and optimize the placement of Wi-Fi spots as needed. For example, when installing public Wi-Fi spots, the internet environment maintenance unit uses the emotion estimation function to monitor user satisfaction in real time. For example, it analyzes facial expressions and voices when using Wi-Fi and calculates a satisfaction score. Furthermore, a system is constructed that dynamically adjusts the placement of Wi-Fi spots based on the emotion estimation data. For example, additional Wi-Fi spots can be installed in areas with low satisfaction. Furthermore, when installing public Wi-Fi spots, the emotion estimation function is used to collect user feedback and optimize the placement. For example, areas with high user satisfaction can be given priority. This makes it possible to monitor user satisfaction in real time and optimize the placement of Wi-Fi spots as needed.

[0063] The education provision unit can use generative AI to analyze each student's learning progress in real time and provide an optimal learning plan. For example, the education provision unit can install generative AI in an educational robot and analyze each student's learning progress in real time. For example, it can automatically generate an optimal learning plan based on test results and learning history. It can also use generative AI to analyze students' learning data and provide customized learning plans tailored to their individual learning needs. For example, it can create plans that focus on weak subjects or areas. Furthermore, the educational robot can use generative AI to monitor each student's learning progress in real time and dynamically adjust the learning plan as needed. For example, it can suggest a supplementary lesson plan if progress is falling behind. This makes it possible to analyze each student's learning progress in real time and provide an optimal learning plan.

[0064] The education provision unit can use generative AI to automatically translate teacher instruction content to accommodate students who speak different languages. For example, the education provision unit can use generative AI to automatically translate teacher instruction content in real time to accommodate students who speak different languages. For example, translating English lessons into Japanese or Spanish. In addition, to support distance education, generative AI can be used to translate teacher instruction content into multiple languages ​​to realize an international classroom. For example, educational robots equipped with simultaneous interpretation functions can be introduced. Furthermore, generative AI can be used to automatically translate teacher instruction content to provide consistent education to students who speak different languages. For example, a system can be built to translate lesson materials and texts into multiple languages. This allows teacher instruction content to be automatically translated to accommodate students who speak different languages.

[0065] The education provision unit can use the emotion estimation function to monitor the emotional state of students requiring special support in real time and provide appropriate support. For example, in special needs education, the education provision unit uses the emotion estimation function to monitor the emotional state of students in real time. For example, it analyzes facial expressions and voice to detect stress and anxiety. Furthermore, based on the emotion estimation data, a system is built to provide appropriate support to students requiring special support. For example, counseling or relaxation programs can be suggested depending on the emotional state. Furthermore, in special needs education, the emotion estimation function is used to monitor the emotional state of students in real time and dynamically adjust the support plan as needed. For example, additional support can be provided if the emotional state worsens. This makes it possible to monitor the emotional state of students requiring special support in real time and provide appropriate support.

[0066] The Internet Environment Development Department can design satellite Internet infrastructure as a multi-use system that can be used not only in education but also in other fields such as agriculture and medicine. For example, it can be used to collect agricultural data or support remote medical care. Furthermore, when introducing satellite Internet, the infrastructure is built taking into account the needs of the agricultural and medical fields. For example, agricultural weather data and medical patient data can be collected and distributed in real time. Furthermore, the satellite Internet infrastructure is designed for multi-purpose use and can be used in fields other than education. For example, a system supporting remote agricultural monitoring and remote medical diagnosis can be built. This allows the satellite Internet infrastructure to be designed as a multi-use system that can be used not only in education but also in other fields such as agriculture and medicine.

[0067] When expanding a mobile network, the Internet environment development unit can design it so that it can also be used as an emergency communication means in the event of a disaster. For example, when expanding a mobile network, the Internet environment development unit can design it so that it can also be used as an emergency communication means in the event of a disaster. For example, a protocol can be introduced to ensure priority communication when a disaster occurs. In addition, the redundancy of the mobile network can be increased so that it can be used as an emergency communication means in the event of a disaster. For example, multiple communication routes can be secured so that communication will not be interrupted even if part of the network is damaged. Furthermore, when expanding a mobile network, its function as an emergency communication means in the event of a disaster can be strengthened. For example, a system that automatically sends emergency messages in the event of a disaster can be introduced. In this way, when expanding a mobile network, it can be designed so that it can also be used as an emergency communication means in the event of a disaster.

[0068] The internet environment maintenance unit can use the emotion estimation function to install Wi-Fi spots in locations where users feel most relaxed, thereby optimizing the learning environment. For example, when installing public Wi-Fi spots, the internet environment maintenance unit uses the emotion estimation function to identify locations where users feel most relaxed. For example, the unit analyzes the user's facial expressions and voice and installs Wi-Fi spots in locations where users feel most relaxed. Furthermore, based on the emotion estimation data, the unit optimizes the placement of Wi-Fi spots to provide a user with a relaxing environment. For example, the unit can install Wi-Fi spots in quiet locations or natural environments. Furthermore, when installing public Wi-Fi spots, the unit uses the emotion estimation function to monitor the user's relaxation level in real time and provide an optimal learning environment. For example, the unit installs additional Wi-Fi spots in locations where users feel most relaxed. This allows the unit to install Wi-Fi spots in locations where users feel most relaxed, thereby optimizing the learning environment.

[0069] The education provision unit can design the educational robot so that it can be used not only for learning support at home but also for rehabilitation and nursing care support. For example, the education provision unit designs the educational robot so that it can be used not only for learning support at home but also for rehabilitation and nursing care support. For example, it can be equipped with a program that supports the recovery of motor functions. Furthermore, rehabilitation and nursing care support functions can be added to the educational robot so that it can be used for multiple purposes. For example, it can provide support for elderly people's daily lives and rehabilitation. Furthermore, the educational robot can be made multifunctional so that it can support rehabilitation and nursing care support in addition to learning support at home. For example, it can be equipped with programs that perform health management and exercise instruction. In this way, the educational robot can be designed so that it can be used not only for learning support at home but also for rehabilitation and nursing care support.

[0070] The Educational Provision Department will be able to support distance learning and apply it to corporate training and professional training, meeting a wide range of educational needs. For example, the Educational Provision Department will build a system that can be applied to corporate training and professional training in support of distance learning. For example, it will provide online training for employees working remotely. It will also use generative AI to automatically generate corporate training and professional training programs and apply them to support distance learning. For example, it will be able to provide technical training and management training. Furthermore, it will develop a system that can also support corporate training and professional training in support of distance learning. For example, it will provide online specialized training for medical professionals. This will allow it to be applied to corporate training and professional training in support of distance learning, meeting a wide range of educational needs.

[0071] The education provision unit can use the emotion estimation function to provide relaxation programs to reduce the stress levels of students with learning difficulties. For example, in special needs education, the education provision unit uses the emotion estimation function to monitor students' stress levels in real time and provide relaxation programs. For example, it can provide guidance on meditation and deep breathing. Furthermore, based on the emotion estimation data, a system can be built that provides relaxation programs to reduce stress for students with learning difficulties. For example, music therapy or art therapy can be suggested. Furthermore, in special needs education, the emotion estimation function can be used to provide relaxation programs to reduce students' stress levels. For example, it can be equipped with a function to play relaxation music or natural sounds. This makes it possible to provide relaxation programs to reduce the stress levels of students with learning difficulties.

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

[0073] The education department can use the emotion estimation function to monitor students' motivation to learn in real time and provide appropriate motivation-boosting programs. For example, by analyzing facial expressions and voice, it can send encouraging messages to students who are losing motivation to learn. It can also suggest learning programs incorporating game elements to students who are losing motivation based on emotion estimation data. Furthermore, it can use the emotion estimation function to monitor students' motivation to learn in real time and dynamically adjust learning plans as needed. For example, it can suggest taking a break if motivation to learn is decreasing. This helps maintain students' motivation to learn and support effective learning.

[0074] The education department can use the emotion estimation function to monitor students' concentration in real time and take appropriate measures if their concentration wanes. For example, it can analyze facial expressions and voice to suggest short breaks to students whose concentration is waning. It can also adjust the environment based on emotion estimation data to maintain students' concentration. For example, it can adjust lighting or music. Furthermore, it can use the emotion estimation function to monitor students' concentration in real time and dynamically adjust the learning plan as needed. For example, it can switch to easier tasks if students' concentration wanes. This helps maintain students' concentration and support effective learning.

[0075] The education department can use the emotion estimation function to monitor students' stress levels in real time and provide appropriate relaxation programs when stress levels rise. For example, by analyzing facial expressions and voice, the department can provide guidance on meditation and deep breathing to students experiencing high stress levels. Furthermore, based on the emotion estimation data, a system can be built to provide relaxation programs to reduce students' stress levels. For example, music therapy or art therapy can be suggested. Furthermore, the emotion estimation function can be used to monitor students' stress levels in real time and dynamically adjust relaxation programs as needed. For example, when stress levels rise, relaxation music or natural sounds can be played. This can reduce students' stress levels and support effective learning.

[0076] The education department can use the emotion estimation function to monitor students' emotional states in real time and provide learning plans that correspond to their emotional states. For example, it can analyze facial expressions and voices and suggest relaxation programs to students whose emotional states are unstable. It can also build a system that provides learning plans that correspond to students' emotional states based on the emotion estimation data. For example, it can switch to easier tasks if the student's emotional state is unstable. Furthermore, it can use the emotion estimation function to monitor students' emotional states in real time and dynamically adjust learning plans as needed. For example, it can suggest taking a break if the student's emotional state is unstable. This can stabilize students' emotional states and support effective learning.

[0077] The education department can use the emotion estimation function to monitor students' motivation to learn in real time and provide appropriate motivation-boosting programs if their motivation drops. For example, it can analyze facial expressions and voice to send encouraging messages to students who are losing motivation. It can also suggest learning programs incorporating game elements to students who are losing motivation based on emotion estimation data. Furthermore, it can use the emotion estimation function to monitor students' motivation to learn in real time and dynamically adjust learning plans as needed. For example, it can suggest taking a break if motivation drops. This helps maintain students' motivation and support effective learning.

[0078] The education provision department can use generative AI to provide customized learning plans tailored to each student's learning style. For example, for students who prefer visual learning, a plan that makes heavy use of visual content can be provided. Generative AI can also be used to analyze students' learning data and suggest optimal learning methods. For example, audio materials can be provided for students who prefer auditory learning. Furthermore, generative AI can be used to build a system that provides customized learning plans tailored to each student's learning style. For example, experiments and project-based learning can be suggested for students who prefer practical learning. This makes it possible to provide optimal learning plans tailored to each student's learning style.

[0079] The education delivery department can use generative AI to automatically translate teacher instruction to accommodate students who speak different languages. For example, translating English lessons into Japanese or Spanish. To support distance learning, generative AI can also be used to translate teacher instruction into multiple languages, creating an international classroom. For example, educational robots equipped with simultaneous interpretation functions can be introduced. Generative AI can also be used to automatically translate teacher instruction to provide consistent education to students who speak different languages. For example, a system can be built to translate lesson materials and texts into multiple languages. This allows teacher instruction to be automatically translated to accommodate students who speak different languages.

[0080] The education delivery department can use generative AI to automatically translate teacher instruction to accommodate students who speak different languages. For example, translating English lessons into Japanese or Spanish. To support distance learning, generative AI can also be used to translate teacher instruction into multiple languages, creating an international classroom. For example, educational robots equipped with simultaneous interpretation functions can be introduced. Generative AI can also be used to automatically translate teacher instruction to provide consistent education to students who speak different languages. For example, a system can be built to translate lesson materials and texts into multiple languages. This allows teacher instruction to be automatically translated to accommodate students who speak different languages.

[0081] The education delivery department can use generative AI to automatically translate teacher instruction to accommodate students who speak different languages. For example, translating English lessons into Japanese or Spanish. To support distance learning, generative AI can also be used to translate teacher instruction into multiple languages, creating an international classroom. For example, educational robots equipped with simultaneous interpretation functions can be introduced. Generative AI can also be used to automatically translate teacher instruction to provide consistent education to students who speak different languages. For example, a system can be built to translate lesson materials and texts into multiple languages. This allows teacher instruction to be automatically translated to accommodate students who speak different languages.

[0082] The education delivery department can use generative AI to automatically translate teacher instruction to accommodate students who speak different languages. For example, translating English lessons into Japanese or Spanish. To support distance learning, generative AI can also be used to translate teacher instruction into multiple languages, creating an international classroom. For example, educational robots equipped with simultaneous interpretation functions can be introduced. Generative AI can also be used to automatically translate teacher instruction to provide consistent education to students who speak different languages. For example, a system can be built to translate lesson materials and texts into multiple languages. This allows teacher instruction to be automatically translated to accommodate students who speak different languages.

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

[0084] Step 1: The Internet Environment Development Department will develop the Internet environment. For example, they can introduce satellite Internet to provide Internet access in areas without terrestrial infrastructure. They can also expand mobile networks to provide high-speed Internet access to more areas. They can also set up public Wi-Fi hotspots to provide free Internet access to public facilities such as schools, libraries, and community centers. Step 2: The Educational Services Department uses robots to provide education. For example, educational robots can be introduced into schools and homes to provide individualized instruction and supplementary lessons to children. Robots can also be used to connect teachers and students in remote locations, enabling real-time lessons. Furthermore, robots can be used to provide individual educational programs for children with special needs.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0119] 7, 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the 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.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The Internet Environment Development Department, which develops the Internet environment, an education providing unit that provides education using a robot; A system characterized by:

2. The Internet environment improvement department: Satellite internet will be introduced, making it possible to receive education via the internet even in mountainous or remote island areas that are difficult to access.

2. The system of claim 1.

3. The Internet environment improvement department: Expanding mobile networks to provide stable internet connections not only in urban areas but also in rural and suburban areas 2. The system of claim 1.

4. The Internet environment improvement department: Installing public Wi-Fi hotspots to allow children without internet access at home to access educational resources 2. The system of claim 1.

5. The education providing department Introducing educational robots to provide high-quality education in areas with teacher shortages 2. The system of claim 1.

6. The education providing department Supports distance learning, allowing students to receive direct instruction from top educators across geographical boundaries 2. The system of claim 1.

7. The education providing department Special education is provided to ensure that children with disabilities and learning difficulties receive an appropriate education.

2. The system of claim 1.

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