System

Abstract

An object of the system according to the embodiment is to provide a learning plan and an online community suitable for each child.SOLUTION: A system includes a learning plan generation unit and a community generation unit. The learning plan generator generates a learning plan suitable for each child. The community generation unit generates an online community in which the children can participate based on the learning plan generated by the learning plan generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Previous technology had the problem of not doing enough to provide learning plans tailored to each child and foster social connections.

[0005] The system according to the embodiment aims to provide learning plans and online communities that are tailored to each individual child. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning plan generation unit and a community generation unit. The learning plan generation unit generates a learning plan tailored to each child. The community generation unit generates an online community in which the children can participate, based on the learning plan generated by the learning plan generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide learning plans and online communities that are tailored to each child. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses the double generative AI "Mana-viva" to provide "learning opportunities" and "online communities" to children who are not attending school. This allows children to regain a sense of self-affirmation and rebuild connections with society. As a result, a system using the double generative AI "Mana-viva" allows children to regain a sense of self-affirmation and rebuild connections with society.

[0029] A system according to an embodiment includes a learning plan generation unit and a community generation unit. The learning plan generation unit generates a learning plan tailored to each child. For example, the learning plan generation unit suggests appropriate learning content and learning materials based on the child's learning history and interests. Furthermore, when the generation AI receives a prompt such as "I want to learn the basics of mathematics," the learning plan generation unit can suggest appropriate learning materials and practice problems. The generation AI generates a learning plan based on the child's learning progress and interests using a text generation AI (e.g., LLM) or a multimodal generation AI. The community generation unit generates an online community in which children can participate. For example, the community generation unit suggests appropriate community content and activities based on the child's interests and communication style. Furthermore, when the generation AI receives a prompt such as "I'm interested in science," the community generation unit can suggest science-related discussions and projects. The generation AI generates and manages an online community in which children can participate using a text generation AI (e.g., LLM) or a multimodal generation AI. This allows children to regain their self-esteem and rebuild their connections with society. For example, providing study plans can increase motivation to study, participating in online communities can reduce feelings of loneliness, improving self-esteem can help students participate in society with confidence, and participating in local activities can strengthen social connections.

[0030] The study plan generation unit can collect not only a child's study history but also behavioral data from daily life, and based on that, suggest optimal study times and methods. For example, the study plan generation unit can analyze a child's sleep patterns and suggest study times when they are most able to concentrate. For example, it can recommend nighttime study for a night owl child and early morning study for a morning person child. The study plan generation unit can also analyze a child's diet and adjust study times based on nutritional balance. For example, it can set study times taking into account the time it takes to digest food after a meal. Furthermore, the study plan generation unit can analyze a child's exercise volume and suggest studying during refreshment time after exercise. For example, it can recommend studying in a relaxed state after exercise. This makes it possible to suggest optimal study times and methods based on a child's daily life.

[0031] The learning plan generation unit can incorporate educational curricula from different cultures and regions to provide learning from a global perspective. The learning plan generation unit can, for example, incorporate educational curricula from different countries to provide children with learning from diverse perspectives. For example, it can suggest studying American and European history along with Japanese history. The learning plan generation unit can also incorporate literary works from different cultures to encourage children to understand diverse cultures. For example, it can suggest studying American and European literary works along with Japanese literary works. The learning plan generation unit can also incorporate science and technology from different regions to encourage children to understand diverse technologies. For example, it can suggest studying American and European science and technology along with Japanese science and technology. This makes it possible to provide learning from a global perspective.

[0032] The learning plan generation unit can provide active learning opportunities, including actual fieldwork or experiments, as part of the learning plan. For example, the learning plan generation unit can incorporate fieldwork into the learning plan to provide learning in an actual setting. For example, the learning plan generation unit can suggest activities such as nature observation or a museum visit. The learning plan generation unit can also incorporate experiments into the learning plan to provide learning through actual experiments. For example, the learning plan generation unit can suggest activities such as science experiments or chemistry experiments. Furthermore, the learning plan generation unit can incorporate project-based learning into the learning plan to provide learning through actual projects. For example, the learning plan generation unit can suggest activities such as group discussions or project-based learning. This can provide active learning opportunities, including actual fieldwork or experiments.

[0033] The community generation unit can visualize children's growth based on activity history within the online community, allowing them to feel a sense of accomplishment. The community generation unit, for example, analyzes activity history within the community and visualizes children's growth in graphs and charts. For example, it displays the number of discussions participated in and the number of messages posted. The community generation unit can also evaluate children's growth based on activity history within the community, allowing them to feel a sense of accomplishment. For example, it displays goals achieved and learning progress. Furthermore, the community generation unit can provide opportunities for children to reflect on their growth based on activity history within the community. For example, it can hold regular review sessions so that children can realize their own growth. This makes children's growth visible, allowing them to feel a sense of accomplishment.

[0034] The community generation department can introduce cross-age programs in online communities that allow children of different age groups and backgrounds to interact. For example, the community generation department can suggest discussions and projects that children of different age groups can participate in together. For example, elementary school students and middle school students can work on a science project together. The community generation department can also suggest events that allow children of different backgrounds to interact. For example, it can suggest cultural exchange events that children of different cultural backgrounds can participate in together. Furthermore, the community generation department can allow children of different age groups and backgrounds to participate in mentoring programs. For example, it can suggest a mentoring program in which high school students teach elementary school students. This allows for the introduction of cross-age programs that allow children of different age groups and backgrounds to interact.

[0035] The Community Generation Department can link activities within the community with the actual local community, promoting hybrid online and offline exchanges. For example, the Community Generation Department can link online discussions and projects with activities in the actual local community. For example, an event planned online could be carried out in the local community. The Community Generation Department can also combine online learning and exchanges with offline practical activities. For example, it could propose local fieldwork based on what was learned online. Furthermore, the Community Generation Department can propose hybrid online and offline events. For example, it could propose an event that combines an online workshop with offline practical activities. This can promote hybrid online and offline exchanges.

[0036] The system can provide opportunities for children to record small daily successes and reflect on them in order to improve their self-esteem. For example, the system can record children's daily successes and provide opportunities for them to reflect on them regularly. For example, it can record daily learning outcomes and achieved goals and hold weekend reflection sessions. The system can also visualize children's successes in graphs and charts to help them feel a sense of accomplishment. For example, it can display achieved goals and learning progress. Furthermore, the system can create self-evaluation sheets based on children's successes and provide feedback to improve their self-esteem. For example, it can provide positive feedback based on the self-evaluation sheets. This allows children to record small daily successes and provide opportunities for them to reflect on them.

[0037] The system can provide online art therapy or music therapy to improve self-esteem. For example, the system can provide online art therapy sessions to provide opportunities for children to improve their self-esteem through self-expression. For example, sessions for drawing or making crafts can be held. The system can also provide online music therapy sessions to provide opportunities for children to improve their self-esteem through music. For example, sessions for playing musical instruments or singing can be held. Furthermore, the system can provide online art therapy or music therapy workshops to provide opportunities for children to improve their self-esteem through self-expression. For example, workshops themed around art or music can be held. In this way, art therapy or music therapy can be provided online.

[0038] The system can provide regular self-assessment and feedback sessions so that children can realize their own growth. For example, the system can provide regular self-assessment sessions so that children can realize their own growth. For example, a monthly self-assessment meeting can be held to review achieved goals and learning progress. The system can also create self-assessment sheets so that children can visualize their own growth. For example, the system can record achieved goals and learning progress based on the self-assessment sheet. Furthermore, the system can provide regular feedback sessions so that children can realize their own growth. For example, a monthly feedback meeting can be held to provide feedback on achieved goals and learning progress. As a result, by providing regular self-assessment and feedback sessions, children can realize their own growth.

[0039] The system can provide mentoring programs with local experts and leaders based on the interests of children. For example, the system provides mentoring programs with local experts and leaders based on the interests of children. For example, for a child who is interested in science, the system can suggest online mentoring with a scientist. The system can also provide workshops with local experts and leaders based on the interests of children. For example, for a child who is interested in art, the system can suggest an online workshop with an artist. Furthermore, the system can also provide discussions with local experts and leaders based on the interests of children. For example, for a child who is interested in business, the system can suggest an online discussion with an entrepreneur. In this way, it is possible to provide mentoring programs with local experts and leaders.

[0040] To rebuild connections with society, generative AI can automatically suggest local events and activities and encourage participation. For example, generative AI can collect information about local events and automatically suggest events and activities that are suitable for children. For example, it can suggest local cleanup activities or volunteer activities. Generative AI can also send messages to children encouraging their participation based on local events and activities. For example, it can send a message such as, "Why not take part in a local cleanup activity?" Furthermore, generative AI can send reminders to children encouraging their participation based on local events and activities. For example, it can send a reminder such as, "Don't forget to volunteer tomorrow!" This makes it possible to automatically suggest local events and activities and encourage participation.

[0041] The system can work with local companies and organizations to provide opportunities for children to gain actual work experience or internships. The system can, for example, work with local companies and organizations to provide opportunities for children to gain actual work experience. For example, it can suggest a one-day work experience at a local company. The system can also work with local companies and organizations to provide opportunities for children to gain internships. For example, it can suggest a short-term internship at a local company. The system can also work with local companies and organizations to provide opportunities for children to deepen their learning through work experience or internships. For example, it can suggest practical experience at a local company. This can provide opportunities for children to gain actual work experience or internships.

[0042] The system can suggest hybrid online and offline social activities, increasing opportunities for children to connect with society in diverse ways. The system can, for example, suggest hybrid online and offline social activities, providing children with opportunities to connect with society in diverse ways. For example, it can combine online discussions with offline hands-on activities. The system can also combine online learning and interaction with offline hands-on activities. For example, it can suggest local fieldwork based on what children have learned online. Furthermore, the system can suggest hybrid online and offline events. For example, it can suggest events that combine online workshops with offline hands-on activities. This can increase opportunities for children to connect with society in diverse ways.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The learning plan generator can suggest the optimal learning environment based on each child's learning style. For example, for a child who prefers visual learning, it can suggest learning materials that make extensive use of visual aids and infographics. For a child who prefers auditory learning, it can also suggest learning methods that utilize audiobooks and podcasts. Furthermore, for a child who prefers tactile learning, it can suggest project-based learning that allows for hands-on learning. This makes it possible to provide the optimal learning environment according to each child's learning style.

[0045] The learning plan generation unit can provide reminders to help children progress at an appropriate pace according to their learning progress. For example, if children are behind in their learning progress, it can send a reminder to encourage them to make progress. Also, if children are progressing smoothly, it can provide a reminder to move on to the next step. Furthermore, the learning plan generation unit can suggest appropriate break times based on children's learning progress. For example, if children's concentration is declining, it can suggest a short break. This allows children to progress at an appropriate pace.

[0046] The learning plan generator can suggest future career paths based on children's learning history. For example, a child interested in science can be suggested a career path as a scientist or engineer. A child interested in art can be suggested a career path as an artist or designer. Furthermore, the learning plan generator can suggest appropriate further education or vocational training programs based on children's learning history. For example, it can suggest universities or vocational schools that specialize in specific fields. This can support children's future career paths.

[0047] The learning plan generator can set personalized learning goals based on children's learning history and interests. For example, a child who is interested in mathematics can be set a goal of mastering specific mathematical skills. Also, a child who is interested in literature can be set a goal of reading specific literary works. Furthermore, the learning plan generator can set short-term and long-term learning goals based on children's learning history and interests. For example, a short-term goal can be set to understand a specific subject, and a long-term goal can be set to obtain a specific qualification. In this way, learning goals can be personalized and set for children.

[0048] The community generation department can provide a safe environment where children can freely express their opinions. For example, it can provide a function that allows them to post opinions anonymously. The community generation department can also provide guidelines for children to express their opinions. For example, it can teach them how to give constructive feedback and how to respect the opinions of others. Furthermore, the community generation department can provide training for children to express their opinions. For example, it can hold workshops to improve debating and presentation skills. This can provide a safe environment where children can freely express their opinions.

[0049] The community generation department can provide opportunities for children to learn new skills based on their interests. For example, if a child is interested in programming, it can suggest an online programming course. Or, if a child is interested in art, it can suggest an online art class. Furthermore, the community generation department can suggest projects for children to learn new skills based on their interests. For example, if a child is interested in science, it can suggest a science experiment project. This provides children with opportunities to learn new skills based on their interests.

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

[0051] Step 1: The learning plan generator generates a learning plan tailored to each child. For example, the learning plan generator suggests appropriate learning content and learning materials based on the child's learning history and interests. If the generator receives a prompt such as "I want to learn the basics of mathematics," it can suggest appropriate learning materials and practice problems. The generator uses text generation AI (e.g., LLM) and multimodal generation AI to generate a learning plan based on the child's learning progress and interests. Step 2: The community generation unit generates an online community in which children can participate. For example, the community generation unit suggests appropriate community content and activities based on the child's interests and communication style. If the generation AI receives the prompt "I'm interested in science," it can suggest science-related discussions and projects. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to generate and manage an online community in which children can participate.

[0052] (Example 2) A system according to an embodiment of the present invention uses the double generative AI "Mana-viva" to provide "learning opportunities" and "online communities" to children who are not attending school. This allows children to regain a sense of self-affirmation and rebuild connections with society. As a result, a system using the double generative AI "Mana-viva" allows children to regain a sense of self-affirmation and rebuild connections with society.

[0053] A system according to an embodiment includes a learning plan generation unit and a community generation unit. The learning plan generation unit generates a learning plan tailored to each child. For example, the learning plan generation unit suggests appropriate learning content and learning materials based on the child's learning history and interests. Furthermore, when the generation AI receives a prompt such as "I want to learn the basics of mathematics," the learning plan generation unit can suggest appropriate learning materials and practice problems. The generation AI generates a learning plan based on the child's learning progress and interests using a text generation AI (e.g., LLM) or a multimodal generation AI. The community generation unit generates an online community in which children can participate. For example, the community generation unit suggests appropriate community content and activities based on the child's interests and communication style. Furthermore, when the generation AI receives a prompt such as "I'm interested in science," the community generation unit can suggest science-related discussions and projects. The generation AI generates and manages an online community in which children can participate using a text generation AI (e.g., LLM) or a multimodal generation AI. This allows children to regain their self-esteem and rebuild their connections with society. For example, providing study plans can increase motivation to study, participating in online communities can reduce feelings of loneliness, improving self-esteem can help students participate in society with confidence, and participating in local activities can strengthen social connections.

[0054] The study plan generation unit can collect not only a child's study history but also behavioral data from daily life, and based on that, suggest optimal study times and methods. For example, the study plan generation unit can analyze a child's sleep patterns and suggest study times when they are most able to concentrate. For example, it can recommend nighttime study for a night owl child and early morning study for a morning person child. The study plan generation unit can also analyze a child's diet and adjust study times based on nutritional balance. For example, it can set study times taking into account the time it takes to digest food after a meal. Furthermore, the study plan generation unit can analyze a child's exercise volume and suggest studying during refreshment time after exercise. For example, it can recommend studying in a relaxed state after exercise. This makes it possible to suggest optimal study times and methods based on a child's daily life.

[0055] The learning plan generation unit can monitor a child's emotional state in real time and suggest low-stress learning methods. For example, the learning plan generation unit analyzes a child's facial expressions and voice and suggests a learning method that will help them relax if they are feeling stressed. For example, it can recommend studying while listening to relaxing music or a learning method that includes short breaks. The learning plan generation unit can also collect a child's heart rate and electrodermal activity using a sensor to analyze their stress level. For example, if their heart rate is elevated, it can suggest a learning method that incorporates deep breathing or meditation. Furthermore, the learning plan generation unit can evaluate a child's stress level based on their self-report and suggest a low-stress learning method. For example, if a child reports feeling "tired," it can recommend a learning method that incorporates light exercise and relaxation. In this way, it is possible to suggest low-stress learning methods based on a child's emotional state.

[0056] The learning plan generation unit can use the emotion estimation function to analyze the frustration or joy a child feels while learning and adjust the learning content based on that. For example, the learning plan generation unit analyzes facial expressions and voice during learning, and if the child feels frustrated, suggests questions with a lower level of difficulty. For example, if the child is having difficulty, the learning plan generation unit advises the child to return to basic questions. The learning plan generation unit can also collect heart rate and electrodermal activity using sensors during learning to analyze the child's frustration level. For example, if the child's heart rate is elevated, the learning plan generation unit suggests a relaxing learning method. Furthermore, the learning plan generation unit can evaluate the child's frustration level based on self-reporting and adjust the learning content. For example, if the child reports that the learning is "difficult," the learning plan generation unit advises the child to return to easier questions. The learning plan generation unit can also analyze facial expressions and voice during learning, and if the child feels joy, suggests questions with a higher level of difficulty. For example, if the child is enjoying themselves, the learning plan generation unit suggests challenging questions. This makes it possible to adjust the learning content based on the child's emotions.

[0057] The learning plan generation unit can incorporate educational curricula from different cultures and regions to provide learning from a global perspective. The learning plan generation unit can, for example, incorporate educational curricula from different countries to provide children with learning from diverse perspectives. For example, it can suggest studying American and European history along with Japanese history. The learning plan generation unit can also incorporate literary works from different cultures to encourage children to understand diverse cultures. For example, it can suggest studying American and European literary works along with Japanese literary works. The learning plan generation unit can also incorporate science and technology from different regions to encourage children to understand diverse technologies. For example, it can suggest studying American and European science and technology along with Japanese science and technology. This makes it possible to provide learning from a global perspective.

[0058] The learning plan generation unit can provide active learning opportunities, including actual fieldwork or experiments, as part of the learning plan. For example, the learning plan generation unit can incorporate fieldwork into the learning plan to provide learning in an actual setting. For example, the learning plan generation unit can suggest activities such as nature observation or a museum visit. The learning plan generation unit can also incorporate experiments into the learning plan to provide learning through actual experiments. For example, the learning plan generation unit can suggest activities such as science experiments or chemistry experiments. Furthermore, the learning plan generation unit can incorporate project-based learning into the learning plan to provide learning through actual projects. For example, the learning plan generation unit can suggest activities such as group discussions or project-based learning. This can provide active learning opportunities, including actual fieldwork or experiments.

[0059] The lesson plan generation unit can use the emotion estimation function to identify a learning theme that a child is most interested in and suggest project-based learning based on that theme. For example, the lesson plan generation unit can use the emotion estimation function to identify a theme that a child is most interested in and suggest a project based on that theme. For example, for a child who is interested in animals, it can suggest a project related to animal protection. The lesson plan generation unit can also use the emotion estimation function to identify a theme that a child is most interested in and suggest a discussion based on that theme. For example, for a child who is interested in science, it can suggest a science discussion. Furthermore, the lesson plan generation unit can use the emotion estimation function to identify a theme that a child is most interested in and suggest an experiment based on that theme. For example, for a child who is interested in chemistry, it can suggest a chemistry experiment. In this way, project-based learning can be suggested based on the learning theme that a child is most interested in.

[0060] The community generation unit can analyze the comments and actions of children within the community and provide feedback to foster leadership and cooperation. For example, the community generation unit can analyze the content of comments within the community and provide positive feedback to children who demonstrate leadership. For example, it can send a message such as, "You're demonstrating great leadership!" The community generation unit can also analyze actions within the community and provide positive feedback to children who demonstrate cooperation. For example, it can send a message such as, "Your teamwork is great!" Furthermore, the community generation unit can analyze the comments and actions within the community and suggest training to foster leadership and cooperation. For example, it can suggest activities such as leadership training or cooperative games. This makes it possible to provide feedback to foster leadership and cooperation.

[0061] The community generation unit can visualize children's growth based on activity history within the online community, allowing them to feel a sense of accomplishment. The community generation unit, for example, analyzes activity history within the community and visualizes children's growth in graphs and charts. For example, it displays the number of discussions participated in and the number of messages posted. The community generation unit can also evaluate children's growth based on activity history within the community, allowing them to feel a sense of accomplishment. For example, it displays goals achieved and learning progress. Furthermore, the community generation unit can provide opportunities for children to reflect on their growth based on activity history within the community. For example, it can hold regular review sessions so that children can realize their own growth. This makes children's growth visible, allowing them to feel a sense of accomplishment.

[0062] The community generation unit can use the emotion estimation function to monitor changes in children's emotions within the community and make suggestions to promote positive interactions. The community generation unit, for example, analyzes comments and actions within the community to monitor changes in emotions. For example, if negative emotions are increasing, the community generation unit can suggest activities to promote positive interactions. The community generation unit can also use the emotion estimation function to monitor changes in children's emotions in real time and provide feedback to promote positive interactions. For example, if the emotion score is low, the community generation unit can send an encouraging message. Furthermore, the community generation unit can use the emotion estimation function to analyze changes in children's emotions and suggest events to promote positive interactions. For example, if the emotion score is high, the community generation unit can suggest fun events. In this way, suggestions can be made to promote positive interactions.

[0063] The community generation department can introduce cross-age programs in online communities that allow children of different age groups and backgrounds to interact. For example, the community generation department can suggest discussions and projects that children of different age groups can participate in together. For example, elementary school students and middle school students can work on a science project together. The community generation department can also suggest events that allow children of different backgrounds to interact. For example, it can suggest cultural exchange events that children of different cultural backgrounds can participate in together. Furthermore, the community generation department can allow children of different age groups and backgrounds to participate in mentoring programs. For example, it can suggest a mentoring program in which high school students teach elementary school students. This allows for the introduction of cross-age programs that allow children of different age groups and backgrounds to interact.

[0064] The Community Generation Department can link activities within the community with the actual local community, promoting hybrid online and offline exchanges. For example, the Community Generation Department can link online discussions and projects with activities in the actual local community. For example, an event planned online could be carried out in the local community. The Community Generation Department can also combine online learning and exchanges with offline practical activities. For example, it could propose local fieldwork based on what was learned online. Furthermore, the Community Generation Department can propose hybrid online and offline events. For example, it could propose an event that combines an online workshop with offline practical activities. This can promote hybrid online and offline exchanges.

[0065] The community generation unit can use the emotion estimation function to identify the activities that children enjoy most and periodically hold events centered around those activities. The community generation unit, for example, uses the emotion estimation function to identify the activities that children enjoy most and periodically hold online events centered around those activities. For example, it can propose a game tournament or an art contest. The community generation unit can also use the emotion estimation function to identify the activities that children enjoy most and periodically hold offline events centered around those activities. For example, it can propose a sports tournament or a music festival. The community generation unit can also use the emotion estimation function to identify the activities that children enjoy most and periodically hold hybrid events centered around those activities. For example, it can propose an event that combines online preparation and offline implementation. This makes it possible to periodically hold events centered around the activities that children enjoy most.

[0066] The system can provide opportunities for children to record small daily successes and reflect on them in order to improve their self-esteem. For example, the system can record children's daily successes and provide opportunities for them to reflect on them regularly. For example, it can record daily learning outcomes and achieved goals and hold weekend reflection sessions. The system can also visualize children's successes in graphs and charts to help them feel a sense of accomplishment. For example, it can display achieved goals and learning progress. Furthermore, the system can create self-evaluation sheets based on children's successes and provide feedback to improve their self-esteem. For example, it can provide positive feedback based on the self-evaluation sheets. This allows children to record small daily successes and provide opportunities for them to reflect on them.

[0067] Generative AI can provide individualized mental health support to increase self-esteem. For example, generative AI can provide individualized mental health support and advice to increase self-esteem. For example, it can suggest methods for stress management and relaxation. Generative AI can also provide individualized mental health support and send encouraging messages to increase self-esteem. For example, it can send messages such as, "You're doing great!" Generative AI can also provide individualized mental health support and counseling to increase self-esteem. For example, it can use a chatbot to listen to children's concerns and offer advice. This makes it possible to provide individualized mental health support to increase self-esteem.

[0068] The emotion estimation function can track changes in children's emotions and provide appropriate support if negative emotions persist. For example, the emotion estimation function can track changes in children's emotions in real time and provide counseling or support if negative emotions persist. For example, if the emotion score is low, it can suggest relaxation methods. The emotion estimation function can also analyze changes in children's emotions and send encouraging messages if negative emotions persist. For example, it can send a message such as, "It's okay, you're doing your best!" Furthermore, based on changes in children's emotions, the emotion estimation function can suggest professional counseling if negative emotions persist. For example, if the emotion score is low, it can suggest a session with a psychological counselor. This makes it possible to provide appropriate support if negative emotions persist.

[0069] The system can provide online art therapy or music therapy to improve self-esteem. For example, the system can provide online art therapy sessions to provide opportunities for children to improve their self-esteem through self-expression. For example, sessions for drawing or making crafts can be held. The system can also provide online music therapy sessions to provide opportunities for children to improve their self-esteem through music. For example, sessions for playing musical instruments or singing can be held. Furthermore, the system can provide online art therapy or music therapy workshops to provide opportunities for children to improve their self-esteem through self-expression. For example, workshops themed around art or music can be held. In this way, art therapy or music therapy can be provided online.

[0070] The system can provide regular self-assessment and feedback sessions so that children can realize their own growth. For example, the system can provide regular self-assessment sessions so that children can realize their own growth. For example, a monthly self-assessment meeting can be held to review achieved goals and learning progress. The system can also create self-assessment sheets so that children can visualize their own growth. For example, the system can record achieved goals and learning progress based on the self-assessment sheet. Furthermore, the system can provide regular feedback sessions so that children can realize their own growth. For example, a monthly feedback meeting can be held to provide feedback on achieved goals and learning progress. As a result, by providing regular self-assessment and feedback sessions, children can realize their own growth.

[0071] The emotion estimation function can identify activities in which children feel most confident and provide programs centered around those activities. The emotion estimation function can, for example, identify activities in which children feel most confident and provide programs centered around those activities. For example, for a child who is confident in sports, online sports training can be suggested. The emotion estimation function can also identify activities in which children feel most confident and provide workshops centered around those activities. For example, for a child who is confident in art, online art workshops can be suggested. The emotion estimation function can also identify activities in which children feel most confident and provide events centered around those activities. For example, for a child who is confident in music, online music events can be suggested. This makes it possible to provide programs centered around activities in which children feel most confident.

[0072] The system can provide mentoring programs with local experts and leaders based on the interests of children. For example, the system provides mentoring programs with local experts and leaders based on the interests of children. For example, for a child who is interested in science, the system can suggest online mentoring with a scientist. The system can also provide workshops with local experts and leaders based on the interests of children. For example, for a child who is interested in art, the system can suggest an online workshop with an artist. Furthermore, the system can also provide discussions with local experts and leaders based on the interests of children. For example, for a child who is interested in business, the system can suggest an online discussion with an entrepreneur. In this way, it is possible to provide mentoring programs with local experts and leaders.

[0073] To rebuild connections with society, generative AI can automatically suggest local events and activities and encourage participation. For example, generative AI can collect information about local events and automatically suggest events and activities that are suitable for children. For example, it can suggest local cleanup activities or volunteer activities. Generative AI can also send messages to children encouraging their participation based on local events and activities. For example, it can send a message such as, "Why not take part in a local cleanup activity?" Furthermore, generative AI can send reminders to children encouraging their participation based on local events and activities. For example, it can send a reminder such as, "Don't forget to volunteer tomorrow!" This makes it possible to automatically suggest local events and activities and encourage participation.

[0074] The emotion estimation function can provide support to reduce anxiety and tension when children participate in social activities. For example, it can suggest breathing techniques or mental exercises to help them relax. The emotion estimation function can also analyze children's anxiety and tension when participating in social activities and send them encouraging messages. For example, it can send a message like, "Don't worry, you're doing your best!" Furthermore, the emotion estimation function can suggest professional counseling based on children's anxiety and tension when participating in social activities. For example, if the emotion score is low, it can suggest a session with a psychological counselor. This can provide support to reduce anxiety and tension when children participate in social activities.

[0075] The system can work with local companies and organizations to provide opportunities for children to gain actual work experience or internships. The system can, for example, work with local companies and organizations to provide opportunities for children to gain actual work experience. For example, it can suggest a one-day work experience at a local company. The system can also work with local companies and organizations to provide opportunities for children to gain internships. For example, it can suggest a short-term internship at a local company. The system can also work with local companies and organizations to provide opportunities for children to deepen their learning through work experience or internships. For example, it can suggest practical experience at a local company. This can provide opportunities for children to gain actual work experience or internships.

[0076] The system can suggest hybrid online and offline social activities, increasing opportunities for children to connect with society in diverse ways. The system can, for example, suggest hybrid online and offline social activities, providing children with opportunities to connect with society in diverse ways. For example, it can combine online discussions with offline hands-on activities. The system can also combine online learning and interaction with offline hands-on activities. For example, it can suggest local fieldwork based on what children have learned online. Furthermore, the system can suggest hybrid online and offline events. For example, it can suggest events that combine online workshops with offline hands-on activities. This can increase opportunities for children to connect with society in diverse ways.

[0077] The emotion estimation function can identify the social activities that children are most interested in and provide programs centered around those activities. The emotion estimation function can, for example, identify the social activities that children are most interested in and provide programs centered around those activities. For example, for a child who is interested in environmental protection, the function can suggest local cleanup activities or tree planting activities. The emotion estimation function can also identify the social activities that children are most interested in and provide workshops centered around those activities. For example, for a child who is interested in animal protection, the function can suggest a workshop on animal protection. The emotion estimation function can also identify the social activities that children are most interested in and provide events centered around those activities. For example, for a child who is interested in local volunteer activities, the function can suggest events related to volunteer activities. This makes it possible to provide programs centered around the social activities that children are most interested in.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The learning plan generator can suggest the optimal learning environment based on each child's learning style. For example, for a child who prefers visual learning, it can suggest learning materials that make extensive use of visual aids and infographics. For a child who prefers auditory learning, it can also suggest learning methods that utilize audiobooks and podcasts. Furthermore, for a child who prefers tactile learning, it can suggest project-based learning that allows for hands-on learning. This makes it possible to provide the optimal learning environment according to each child's learning style.

[0080] The learning plan generation unit can provide reminders to help children progress at an appropriate pace according to their learning progress. For example, if children are behind in their learning progress, it can send a reminder to encourage them to make progress. Also, if children are progressing smoothly, it can provide a reminder to move on to the next step. Furthermore, the learning plan generation unit can suggest appropriate break times based on children's learning progress. For example, if children's concentration is declining, it can suggest a short break. This allows children to progress at an appropriate pace.

[0081] The learning plan generator uses the emotion estimation function to analyze changes in a child's motivation while they are learning and can adjust the learning content accordingly. For example, if their motivation is declining, it can suggest new topics that pique their interest. If their motivation is rising, it can provide them with challenging tasks. Furthermore, the learning plan generator can evaluate their motivation level based on their self-reports and adjust the learning content accordingly. For example, if a child reports that they "want to learn more," it can provide them with additional learning resources. This allows the learning content to be adjusted based on the child's motivation.

[0082] The learning plan generation unit can use the emotion estimation function to analyze the sense of accomplishment children feel while learning, and provide feedback on their learning progress based on that. For example, if they feel a sense of accomplishment, it can provide feedback to encourage them to move on to the next step. If their sense of accomplishment is low, it can also send them an encouraging message. Furthermore, the learning plan generation unit can evaluate their sense of accomplishment based on children's self-reports and provide feedback on their learning progress. For example, if a child reports that they "finished it," it can provide positive feedback. This makes it possible to provide feedback on their learning progress based on their sense of accomplishment.

[0083] The learning plan generator can suggest future career paths based on children's learning history. For example, a child interested in science can be suggested a career path as a scientist or engineer. A child interested in art can be suggested a career path as an artist or designer. Furthermore, the learning plan generator can suggest appropriate further education or vocational training programs based on children's learning history. For example, it can suggest universities or vocational schools that specialize in specific fields. This can support children's future career paths.

[0084] The learning plan generator can set personalized learning goals based on children's learning history and interests. For example, a child who is interested in mathematics can be set a goal of mastering specific mathematical skills. Also, a child who is interested in literature can be set a goal of reading specific literary works. Furthermore, the learning plan generator can set short-term and long-term learning goals based on children's learning history and interests. For example, a short-term goal can be set to understand a specific subject, and a long-term goal can be set to obtain a specific qualification. In this way, learning goals can be personalized and set for children.

[0085] The learning plan generation unit can use the emotion estimation function to analyze the anxiety and tension children feel while studying, and based on that, suggest a relaxing learning environment. For example, if a child is feeling anxious or nervous, it can suggest playing relaxing music. It can also suggest appropriate lighting and temperature to provide a relaxing learning environment. Furthermore, the learning plan generation unit can evaluate anxiety and tension based on children's self-reports and suggest a relaxing learning environment. For example, if a child reports that they are "nervous," it can suggest learning methods that incorporate deep breathing and meditation. In this way, a relaxing learning environment can be provided based on children's anxiety and tension.

[0086] The community generation department can provide a safe environment where children can freely express their opinions. For example, it can provide a function that allows them to post opinions anonymously. The community generation department can also provide guidelines for children to express their opinions. For example, it can teach them how to give constructive feedback and how to respect the opinions of others. Furthermore, the community generation department can provide training for children to express their opinions. For example, it can hold workshops to improve debating and presentation skills. This can provide a safe environment where children can freely express their opinions.

[0087] The community generation unit can use the emotion estimation function to analyze the sense of loneliness children feel within the community and make suggestions to promote interaction based on that. For example, if a child feels lonely, it can suggest group activities or pair work. The emotion estimation function can also be used to identify the activities children enjoy most and suggest events centered around those activities. For example, for children who are interested in games or art, it can suggest gaming tournaments or art workshops. Furthermore, the community generation unit can evaluate children's feelings of loneliness based on their self-reports and make suggestions to promote interaction. For example, if a child reports that they "feel lonely," it can suggest online chats or video calls. This makes it possible to make suggestions to promote interaction based on children's feelings of loneliness.

[0088] The community generation department can provide opportunities for children to learn new skills based on their interests. For example, if a child is interested in programming, it can suggest an online programming course. Or, if a child is interested in art, it can suggest an online art class. Furthermore, the community generation department can suggest projects for children to learn new skills based on their interests. For example, if a child is interested in science, it can suggest a science experiment project. This provides children with opportunities to learn new skills based on their interests.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The learning plan generator generates a learning plan tailored to each child. For example, the learning plan generator suggests appropriate learning content and learning materials based on the child's learning history and interests. If the generator receives a prompt such as "I want to learn the basics of mathematics," it can suggest appropriate learning materials and practice problems. The generator uses text generation AI (e.g., LLM) and multimodal generation AI to generate a learning plan based on the child's learning progress and interests. Step 2: The community generation unit generates an online community in which children can participate. For example, the community generation unit suggests appropriate community content and activities based on the child's interests and communication style. If the generation AI receives the prompt "I'm interested in science," it can suggest science-related discussions and projects. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to generate and manage an online community in which children can participate.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0097] The 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.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0101] Fig. 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.

[0102] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] The data processing system 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The 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.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] 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.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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]

[0158] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A learning plan generation unit that generates a learning plan tailored to each child; a community generation unit that generates an online community in which children can participate based on the learning plan generated by the learning plan generation unit. A system characterized by:

2. The learning plan generation unit Collects not only a child's learning history but also data on their daily activities, and suggests optimal study times and methods based on that data.

2. The system of claim 1.

3. The learning plan generation unit Incorporating educational curricula from different cultures and regions to provide learning from a global perspective 2. The system of claim 1.

4. The community generation unit Analyze what children say or do in the community and provide feedback to foster leadership and collaboration.

2. The system of claim 1.

5. The system comprises: To improve children's self-esteem, provide opportunities for them to record and reflect on small successes they have had every day.

2. The system of claim 1.

6. The community generation unit Monitor the emotional changes of children in the community and make suggestions to promote positive interactions.

2. The system of claim 1.

7. The emotion estimation function is Identify the activities in which children feel most confident and provide programs centered around those activities 2. The system of claim 1.

8. The emotion estimation function is Providing support to reduce anxiety and tension when children participate in social activities 2. The system of claim 1.

Images