system

Abstract

The system according to this embodiment aims to collect the activities desired by children and provide an appropriate virtual learning environment based on those activities. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a support unit. The collection unit collects the activities desired by the children. The analysis unit analyzes the information collected by the collection unit and proposes a solution. The provision unit provides a virtual environment based on the solution proposed by the analysis unit. The support unit supports the children within the virtual environment provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the activities desired by children are not sufficiently collected effectively and an appropriate learning environment is not provided based on them.

[0005] The system according to the embodiment aims to collect the activities desired by children and provide an appropriate virtual learning environment based on them.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a support unit. The collection unit collects the activities desired by the children. The analysis unit analyzes the information collected by the collection unit and proposes solutions. The provision unit provides a virtual environment based on the solutions proposed by the analysis unit. The support unit provides support to the children within the virtual environment provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can collect the activities desired by children and provide an appropriate virtual learning environment based on them. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The programming environment construction system according to an embodiment of the present invention is a system that utilizes generative AI to build a programming environment for children and contribute to solving local issues. This system provides a learning environment through a virtual environment, along with suggestions for actual solutions to what children "want to do". Next, a lab is created in which children can create concrete use cases with the help of an agent teacher tailored to each child. In this lab, the applications that children actually create are deployed to address issues in local governments and other organizations, with the aim of practical application. Examples include use in projection mapping and digital signage in cities and towns, waste sorting and library book reservation systems, and information exchange apps for local playgrounds. This has the advantage that local governments can try various things without incurring costs, and children can feel closer to social issues. For example, when children install and run this app, thumbnails of multiple virtual teachers, modeled after school staff, are displayed. For instance, "Game Development Teacher," "Digital Art Teacher," and "Digital Music Teacher" are shown. When children select one of these teachers, they are prompted to enter a virtual environment, where they are provided with pre-installed tools such as programming tools for game development, digital art tools for digital art, and digital music tools for music. An AI teacher is always present in this environment, and screens are displayed where children can select basic courses and ask questions through free-form input. Children can use these tools in a workshop-like manner, asking the AI ​​teacher questions about various issues that arise during the process of creating their own works. Furthermore, as an output, the project is matched with the current problems faced by local governments, allowing children to create applications that can actually be used by users in a way that addresses these challenges. For example, by tackling various themes such as "I want to make a game themed around mascots," "I want to make a library book recommendation app," "I want a garbage sorting app that uses image AI," or "I want something that lets people enjoy disaster prevention maps using augmented reality," children can answer the challenges faced by local governments. By making the design and access rights of APIs that receive pre-prepared outputs publicly available, agents can autonomously adjust children's applications to match those outputs. For example, a library's book recommendation system can be displayed by providing an access API for the signage installed in the library. Also, mascot games and disaster prevention apps can be freely published and made available to citizens by leaving a specific space open on the municipality's website. This system allows local governments to work on creating new systems that would be difficult to implement using the traditional waterfall model, and it also provides children with an opportunity to think about how they can use DX (Digital Transformation) to solve local issues in the future. As a result, the programming environment building system can improve children's learning environment and contribute to solving local issues.

[0029] The programming environment construction system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a support unit. The collection unit collects the activities desired by children. The collection unit can collect the activities desired by children through, for example, questionnaires or interviews. The collection unit can also collect children's activities using sensor data. For example, the collection unit collects children's behavioral data and identifies their desired activities. The analysis unit analyzes the information collected by the collection unit and proposes solutions. The analysis unit can analyze the collected information using, for example, data mining techniques. The analysis unit can also analyze the information using statistical analysis and propose solutions. For example, the analysis unit proposes the optimal solution based on the collected data. The provision unit provides a virtual environment based on the solution proposed by the analysis unit. The provision unit can provide a virtual environment using, for example, VR or AR technology. The provision unit can also provide a simulation environment. For example, the provision unit provides an environment in which children can learn within the virtual environment. The support unit supports children within the virtual environment provided by the provision unit. The support unit can support children using, for example, an AI tutor. Furthermore, the support unit can also support children using a virtual assistant. For example, the support unit provides necessary support when children are learning in the virtual environment. This allows the programming environment construction system according to the embodiment to improve children's learning environment.

[0030] The data collection department collects information on the activities children desire. For example, the department can collect this information through surveys and interviews. Specifically, surveys are conducted using online forms or paper questionnaires and include questions about programming themes and project types that children are interested in, as well as the tools and technologies they would like to use. Interviews involve teachers or parents directly interacting with children to gather detailed information about their interests and desires. The data collection department can also collect information on children's activities using sensor data. For example, it can collect behavioral data to identify desired activities. Specifically, it can use sensors from wearable devices and smartphones to collect data on children's movements, heart rate, and activity time, and analyze what activities they are interested in. This allows the data collection department to more accurately understand children's potential interests and desires. Furthermore, the data collection department can also collect data from social media and online communities to understand what programming projects children are interested in. This allows the data collection department to gather comprehensive information from diverse data sources and accurately identify the activities children desire.

[0031] The analysis department analyzes the information collected by the data collection department and proposes solutions. For example, the analysis department can analyze the collected information using data mining techniques. Specifically, it uses data mining techniques to extract common patterns and trends from collected questionnaires and interview data to understand the tendencies of programming activities that interest children. The analysis department can also analyze information using statistical analysis and propose solutions. For example, it can propose the optimal solution based on the collected data. Specifically, it uses statistical analysis to design programming curricula and projects based on children's interests and wishes. Furthermore, the analysis department can use machine learning algorithms to analyze children's learning styles and progress from the collected data and propose individually optimized learning plans. For example, it can predict what pace of learning is most effective for children based on past learning data and adjust the learning plan accordingly. In addition, the analysis department can use natural language processing techniques to analyze children's free-form writing and feedback to understand their needs and problems. As a result, the analysis department can analyze the collected data from multiple perspectives and propose concrete solutions to optimize children's learning environment.

[0032] The service provider will provide a virtual environment based on the solutions proposed by the analysis department. For example, the service provider can provide a virtual environment using VR or AR technology. Specifically, they can use VR technology to create an environment where children can learn programming in a virtual space. For example, they can create virtual classrooms or laboratories to allow children to learn interactively. They can also use AR technology to overlay virtual objects onto the real world, allowing children to visually confirm the results of their programming. For example, they can use an AR app to see how the programs they create work in the real world. Furthermore, the service provider can also provide a simulation environment. For example, they can provide an environment where children can learn within a virtual environment. Specifically, they can build a programming simulation environment where children can actually write and run code and check the results. This allows the service provider to provide an environment where children can learn programming practically, enhancing their learning effectiveness. The service provider can also provide an environment through an online platform where children can interact with other learners and teachers and work on projects collaboratively. This allows children to deepen their learning through collaboration and communication with others while learning within the virtual environment.

[0033] The support department provides support to children within the virtual environment provided by the service provider. For example, the support department can use an AI tutor to support children. Specifically, the AI ​​tutor monitors children's learning progress in real time and provides appropriate feedback and advice. For instance, when children are working on a programming assignment, the AI ​​tutor can identify the cause of errors and suggest solutions. The AI ​​tutor can also adjust the learning content according to the children's level of understanding, providing individually optimized learning plans. Furthermore, the support department can also support children using a virtual assistant. For example, the virtual assistant can provide necessary information and answer questions as children learn within the virtual environment. The virtual assistant uses natural language processing technology to provide appropriate responses to children's questions and requests. This allows the support department to provide the necessary support as children learn within the virtual environment. The support department can also analyze children's learning data to evaluate their learning progress and achievements. For example, based on children's learning data, they can identify their strengths and weaknesses and provide information to plan future learning. This allows the support department to provide concrete support to improve the children's learning environment and maximize their learning effectiveness.

[0034] The service provider can deploy applications created by children to address the challenges of local governments. For example, the service provider can utilize these applications in projection mapping and digital signage projects for local governments. It can also provide systems for waste sorting and library book reservations. For instance, the service provider can provide an app for exchanging information about local playgrounds. In this way, by deploying applications created by children to address the challenges of local governments, it can contribute to solving those challenges. Some or all of the above-described processes in the service provider may be performed using, for example, a generative AI, or without one. For example, the service provider can input applications created by children into a generative AI and have the AI ​​execute the optimal deployment method for addressing the challenges of the local government.

[0035] The support unit allows agent teachers to support children. The support unit can support children using, for example, an AI tutor. The support unit can also support children using a virtual assistant. For example, the support unit provides necessary support when children are learning in a virtual environment. This allows agent teachers to support children and effectively assist their learning. Some or all of the above processes in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input children's learning data into a generative AI and have the generative AI execute the optimal support method.

[0036] The service provider can offer services such as projection mapping and digital signage applications for local governments, waste sorting and library book reservation systems, and an information exchange app for local playgrounds. For example, the service provider can perform projection mapping for local governments. The service provider can also utilize digital signage. For example, the service provider can provide a waste sorting system. The service provider can also provide a library book reservation system. For example, the service provider can provide an information exchange app for local playgrounds. By providing services such as projection mapping and digital signage applications for local governments, waste sorting and library book reservation systems, and an information exchange app for local playgrounds, the service provider can contribute to solving local issues. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or not using generative AI. For example, the service provider can have generative AI execute the optimal solution to the local government's problems.

[0037] The data collection unit can analyze children's past activity history and select the optimal collection method. For example, the unit can prioritize collecting related "things they want to do" based on themes children have shown interest in in the past. It can also collect "things they want to do" related to similar themes by referring to the history of workshops and events children have participated in in the past. For example, the unit can efficiently acquire information by collecting data at specific times of day or on specific days of the week based on children's past activity history. This allows the unit to select the optimal collection method and efficiently collect information by analyzing past activity history. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input children's past activity data into an AI and have the AI ​​select the optimal collection method.

[0038] The data collection unit can filter data based on the children's current interests and areas of interest during the collection process. For example, the unit prioritizes collecting things that children "want to do" that are related to themes they are currently interested in. The unit can also filter relevant information and eliminate unnecessary information based on the children's areas of interest. For example, the unit can adjust the categories of information to be collected according to the children's current interests to obtain the most relevant information. This allows the unit to collect highly relevant information by filtering based on current interests and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the children's interest data into an AI and have the AI ​​perform the filtering.

[0039] The data collection unit can prioritize collecting highly relevant information by considering the children's geographical location during the collection process. For example, the data collection unit can prioritize collecting things that children "want to do" that are related to their current location. The data collection unit can also collect information about nearby events and activities based on the children's geographical location. For example, the data collection unit can collect things that children "want to do" that are appropriate for the characteristics of the area, taking into account the children's location. This allows for the priority collection of highly relevant information by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the children's location data into an AI and have the AI ​​perform the collection of highly relevant information.

[0040] The data collection unit can analyze children's social media activities and collect relevant information during the collection process. For example, the collection unit can collect relevant "things they want to do" based on the content children share on social media. The collection unit can also analyze the activities of children's social media followers and friends and collect relevant information. For example, the collection unit can collect relevant "things they want to do" based on topics children show interest in on social media. This allows for the efficient collection of relevant information by analyzing social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input children's social media data into an AI and have the AI ​​collect relevant information.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on highly important information and propose specific solutions. Conversely, the analysis unit can perform a concise analysis on less important information and propose basic solutions. For example, the analysis unit can determine the priority of the analysis according to the importance of the collected information and perform the analysis efficiently. This allows for efficient analysis by adjusting the level of detail based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a code analysis algorithm to information related to programming and propose the optimal solution. Similarly, it can apply an image analysis algorithm to information related to digital art and propose the optimal solution. For example, it can apply an audio analysis algorithm to information related to music and propose the optimal solution. This allows the analysis unit to propose the optimal solution by applying different analysis algorithms depending on the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input information category data into the AI ​​and have the AI ​​apply different analysis algorithms.

[0043] The analysis unit can determine the priority of analysis based on the information submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected information and propose the latest solutions. Furthermore, the analysis unit can perform basic analysis on older information and update it as needed. For example, the analysis unit can determine the priority of analysis based on the submission date and perform the analysis efficiently. This allows for efficient analysis by determining the priority of analysis based on the information submission date. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input information submission date data into the AI ​​and have the AI ​​determine the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information and propose specific solutions. Furthermore, for less relevant information, the analysis unit can perform a concise analysis and propose basic solutions. For example, the analysis unit can adjust the order of analysis based on the relevance of the information to perform the analysis efficiently. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input information relevance data into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0045] The service provider can adjust the level of detail of the virtual environment based on the importance of the proposed solution at the time of delivery. For example, the service provider can provide a detailed virtual environment for high-importance solutions, and a simplified virtual environment for low-importance solutions. For example, the service provider adjusts the level of detail of the virtual environment according to the importance of the proposed solution. This allows for efficient provision of virtual environments by adjusting the level of detail based on the importance of the solution. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input solution importance data into AI and have the AI ​​perform the adjustment of the level of detail of the virtual environment.

[0046] The service provider can provide different virtual environments depending on the category of the solution at the time of provision. For example, for a solution related to programming, the service provider can provide a virtual environment with programming tools installed. For a solution related to digital art, the service provider can provide a virtual environment with digital art tools installed. For example, for a solution related to music, the service provider can provide a virtual environment with digital music tools installed. By providing the optimal virtual environment according to the category of the solution, the learning effect of children can be enhanced. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input solution category data into AI and have the AI ​​perform the provision of different virtual environments.

[0047] The service provider can determine the priority of virtual environments based on the submission date of the solutions at the time of provision. For example, the service provider can provide a readily available virtual environment for recently proposed solutions. Alternatively, it can provide a basic virtual environment for older solutions. For example, the service provider can determine the priority of virtual environments based on the submission date and provide them efficiently. This allows for efficient provision of virtual environments by prioritizing based on the submission date of the solutions. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input solution submission date data into AI and have the AI ​​determine the priority of virtual environments.

[0048] The service provider can adjust the order of virtual environments based on the relevance of the solutions at the time of delivery. For example, the service provider can prioritize providing a detailed virtual environment for highly relevant solutions. Conversely, the service provider can provide a simplified virtual environment for less relevant solutions. For example, the service provider can adjust the order of virtual environments based on the relevance of the solutions and deliver them efficiently. This allows for efficient delivery of virtual environments by adjusting the order based on the relevance of the solutions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the relevance of solutions into AI and have AI perform the adjustment of the order of virtual environments.

[0049] The support department can select appropriate support methods by referring to the children's past activity history when providing support. For example, the support department can provide relevant support by referring to the children's past participation history in workshops and events. The support department can also prioritize support on specific themes based on the children's past activity history. For example, the support department can analyze the children's past activity history and select the most effective support method. This allows the support department to select the optimal support method and provide support effectively by referring to past activity history. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input the children's past activity data into AI and have the AI ​​select the optimal support method.

[0050] The support unit can customize the means of support based on the children's current interests and areas of interest during support sessions. For example, the support unit can provide support related to themes that the children are currently interested in. The support unit can also provide relevant information and resources based on the children's areas of interest. For example, the support unit can customize the means of support according to the children's current interests to provide optimal support. This allows for the provision of optimal support by customizing the means of support based on current interests and areas of interest. Some or all of the above-described processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input data on the children's interests and have the AI ​​perform the customization of the means of support.

[0051] The support unit can select the optimal support method by considering the children's geographical location information during support. For example, the support unit can provide support related to the children's current location. Furthermore, based on the children's geographical location information, the support unit can provide support related to nearby resources and events. For example, the support unit can provide support tailored to the characteristics of the region by considering the children's location information. This allows for the selection of the optimal support method and effective support delivery by considering geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the children's location data into AI and have the AI ​​select the optimal support method.

[0052] The support unit can analyze children's social media activity and suggest support measures when providing assistance. For example, the support unit can provide relevant support based on the content children share on social media. The support unit can also analyze the activities of children's social media followers and friends and provide relevant support. For example, the support unit can provide relevant support based on topics children show interest in on social media. This allows for the efficient provision of relevant support by analyzing social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or not. For example, the support unit can input children's social media data into AI and have the AI ​​suggest support measures.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The service provider can adjust the level of detail of the virtual environment based on the importance of the proposed solution at the time of delivery. For example, the service provider can provide a detailed virtual environment for high-importance solutions, and a simplified virtual environment for low-importance solutions. For example, the service provider adjusts the level of detail of the virtual environment according to the importance of the proposed solution. This allows for efficient provision of virtual environments by adjusting the level of detail based on the importance of the solution. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input solution importance data into AI and have the AI ​​perform the adjustment of the level of detail of the virtual environment.

[0055] The support department can select appropriate support methods by referring to the children's past activity history when providing support. For example, the support department can provide relevant support by referring to the children's past participation history in workshops and events. The support department can also prioritize support on specific themes based on the children's past activity history. For example, the support department can analyze the children's past activity history and select the most effective support method. This allows the support department to select the optimal support method and provide support effectively by referring to past activity history. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input the children's past activity data into AI and have the AI ​​select the optimal support method.

[0056] The service provider can provide different virtual environments depending on the category of the solution at the time of provision. For example, for a solution related to programming, the service provider can provide a virtual environment with programming tools installed. For a solution related to digital art, the service provider can provide a virtual environment with digital art tools installed. For example, for a solution related to music, the service provider can provide a virtual environment with digital music tools installed. By providing the optimal virtual environment according to the category of the solution, the learning effect of children can be enhanced. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input solution category data into AI and have the AI ​​perform the provision of different virtual environments.

[0057] The data collection unit can filter data based on the children's current interests and areas of interest during the collection process. For example, the unit prioritizes collecting things that children "want to do" that are related to themes they are currently interested in. The unit can also filter relevant information and eliminate unnecessary information based on the children's areas of interest. For example, the unit can adjust the categories of information to be collected according to the children's current interests to obtain the most relevant information. This allows the unit to collect highly relevant information by filtering based on current interests and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the children's interest data into an AI and have the AI ​​perform the filtering.

[0058] The support unit can analyze children's social media activity and suggest support measures when providing assistance. For example, the support unit can provide relevant support based on the content children share on social media. The support unit can also analyze the activities of children's social media followers and friends and provide relevant support. For example, the support unit can provide relevant support based on topics children show interest in on social media. This allows for the efficient provision of relevant support by analyzing social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or not. For example, the support unit can input children's social media data into AI and have the AI ​​suggest support measures.

[0059] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a code analysis algorithm to information related to programming and propose the optimal solution. Similarly, it can apply an image analysis algorithm to information related to digital art and propose the optimal solution. For example, it can apply an audio analysis algorithm to information related to music and propose the optimal solution. This allows the analysis unit to propose the optimal solution by applying different analysis algorithms depending on the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input information category data into the AI ​​and have the AI ​​apply different analysis algorithms.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects the activities that children want to do. The data collection unit can collect the activities that children want to do, for example, through questionnaires or interviews. Alternatively, the data collection unit can collect children's activities using sensor data. For example, the data collection unit can collect children's behavioral data and identify the activities they want to do. Step 2: The analysis unit analyzes the information collected by the collection unit and proposes a solution. The analysis unit can analyze the collected information using, for example, data mining techniques. Alternatively, the analysis unit can analyze the information using statistical analysis and propose a solution. For example, the analysis unit proposes the optimal solution based on the collected data. Step 3: The service provider provides a virtual environment based on the solution proposed by the analysis unit. The service provider can provide the virtual environment using, for example, VR or AR technology. The service provider can also provide a simulation environment. For example, the service provider can provide an environment in which children can learn within the virtual environment. Step 4: The support team provides support to the children within the virtual environment provided by the provider team. The support team can, for example, support the children using an AI tutor. The support team can also support the children using a virtual assistant. For example, the support team provides the necessary support when the children are learning within the virtual environment.

[0062] (Example of form 2) The programming environment construction system according to an embodiment of the present invention is a system that utilizes generative AI to build a programming environment for children and contribute to solving local issues. This system provides a learning environment through a virtual environment, along with suggestions for actual solutions to what children "want to do". Next, a lab is created in which children can create concrete use cases with the help of an agent teacher tailored to each child. In this lab, the applications that children actually create are deployed to address issues in local governments and other organizations, with the aim of practical application. Examples include use in projection mapping and digital signage in cities and towns, waste sorting and library book reservation systems, and information exchange apps for local playgrounds. This has the advantage that local governments can try various things without incurring costs, and children can feel closer to social issues. For example, when children install and run this app, thumbnails of multiple virtual teachers, modeled after school staff, are displayed. For instance, "Game Development Teacher," "Digital Art Teacher," and "Digital Music Teacher" are shown. When children select one of these teachers, they are prompted to enter a virtual environment, where they are provided with pre-installed tools such as programming tools for game development, digital art tools for digital art, and digital music tools for music. An AI teacher is always present in this environment, and screens are displayed where children can select basic courses and ask questions through free-form input. Children can use these tools in a workshop-like manner, asking the AI ​​teacher questions about various issues that arise during the process of creating their own works. Furthermore, as an output, the project is matched with the current problems faced by local governments, allowing children to create applications that can actually be used by users in a way that addresses these challenges. For example, by tackling various themes such as "I want to make a game themed around mascots," "I want to make a library book recommendation app," "I want a garbage sorting app that uses image AI," or "I want something that lets people enjoy disaster prevention maps using augmented reality," children can answer the challenges faced by local governments. By making the design and access rights of APIs that receive pre-prepared outputs publicly available, agents can autonomously adjust children's applications to match those outputs. For example, a library's book recommendation system can be displayed by providing an access API for the signage installed in the library. Also, mascot games and disaster prevention apps can be freely published and made available to citizens by leaving a specific space open on the municipality's website. This system allows local governments to work on creating new systems that would be difficult to implement using the traditional waterfall model, and it also provides children with an opportunity to think about how they can use DX (Digital Transformation) to solve local issues in the future. As a result, the programming environment building system can improve children's learning environment and contribute to solving local issues.

[0063] The programming environment construction system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a support unit. The collection unit collects the activities desired by children. The collection unit can collect the activities desired by children through, for example, questionnaires or interviews. The collection unit can also collect children's activities using sensor data. For example, the collection unit collects children's behavioral data and identifies their desired activities. The analysis unit analyzes the information collected by the collection unit and proposes solutions. The analysis unit can analyze the collected information using, for example, data mining techniques. The analysis unit can also analyze the information using statistical analysis and propose solutions. For example, the analysis unit proposes the optimal solution based on the collected data. The provision unit provides a virtual environment based on the solution proposed by the analysis unit. The provision unit can provide a virtual environment using, for example, VR or AR technology. The provision unit can also provide a simulation environment. For example, the provision unit provides an environment in which children can learn within the virtual environment. The support unit supports children within the virtual environment provided by the provision unit. The support unit can support children using, for example, an AI tutor. Furthermore, the support unit can also support children using a virtual assistant. For example, the support unit provides necessary support when children are learning in the virtual environment. This allows the programming environment construction system according to the embodiment to improve children's learning environment.

[0064] The data collection department collects information on the activities children desire. For example, the department can collect this information through surveys and interviews. Specifically, surveys are conducted using online forms or paper questionnaires and include questions about programming themes and project types that children are interested in, as well as the tools and technologies they would like to use. Interviews involve teachers or parents directly interacting with children to gather detailed information about their interests and desires. The data collection department can also collect information on children's activities using sensor data. For example, it can collect behavioral data to identify desired activities. Specifically, it can use sensors from wearable devices and smartphones to collect data on children's movements, heart rate, and activity time, and analyze what activities they are interested in. This allows the data collection department to more accurately understand children's potential interests and desires. Furthermore, the data collection department can also collect data from social media and online communities to understand what programming projects children are interested in. This allows the data collection department to gather comprehensive information from diverse data sources and accurately identify the activities children desire.

[0065] The analysis department analyzes the information collected by the data collection department and proposes solutions. For example, the analysis department can analyze the collected information using data mining techniques. Specifically, it uses data mining techniques to extract common patterns and trends from collected questionnaires and interview data to understand the tendencies of programming activities that interest children. The analysis department can also analyze information using statistical analysis and propose solutions. For example, it can propose the optimal solution based on the collected data. Specifically, it uses statistical analysis to design programming curricula and projects based on children's interests and wishes. Furthermore, the analysis department can use machine learning algorithms to analyze children's learning styles and progress from the collected data and propose individually optimized learning plans. For example, it can predict what pace of learning is most effective for children based on past learning data and adjust the learning plan accordingly. In addition, the analysis department can use natural language processing techniques to analyze children's free-form writing and feedback to understand their needs and problems. As a result, the analysis department can analyze the collected data from multiple perspectives and propose concrete solutions to optimize children's learning environment.

[0066] The service provider will provide a virtual environment based on the solutions proposed by the analysis department. For example, the service provider can provide a virtual environment using VR or AR technology. Specifically, they can use VR technology to create an environment where children can learn programming in a virtual space. For example, they can create virtual classrooms or laboratories to allow children to learn interactively. They can also use AR technology to overlay virtual objects onto the real world, allowing children to visually confirm the results of their programming. For example, they can use an AR app to see how the programs they create work in the real world. Furthermore, the service provider can also provide a simulation environment. For example, they can provide an environment where children can learn within a virtual environment. Specifically, they can build a programming simulation environment where children can actually write and run code and check the results. This allows the service provider to provide an environment where children can learn programming practically, enhancing their learning effectiveness. The service provider can also provide an environment through an online platform where children can interact with other learners and teachers and work on projects collaboratively. This allows children to deepen their learning through collaboration and communication with others while learning within the virtual environment.

[0067] The support department provides support to children within the virtual environment provided by the service provider. For example, the support department can use an AI tutor to support children. Specifically, the AI ​​tutor monitors children's learning progress in real time and provides appropriate feedback and advice. For instance, when children are working on a programming assignment, the AI ​​tutor can identify the cause of errors and suggest solutions. The AI ​​tutor can also adjust the learning content according to the children's level of understanding, providing individually optimized learning plans. Furthermore, the support department can also support children using a virtual assistant. For example, the virtual assistant can provide necessary information and answer questions as children learn within the virtual environment. The virtual assistant uses natural language processing technology to provide appropriate responses to children's questions and requests. This allows the support department to provide the necessary support as children learn within the virtual environment. The support department can also analyze children's learning data to evaluate their learning progress and achievements. For example, based on children's learning data, they can identify their strengths and weaknesses and provide information to plan future learning. This allows the support department to provide concrete support to improve the children's learning environment and maximize their learning effectiveness.

[0068] The service provider can deploy applications created by children to address the challenges of local governments. For example, the service provider can utilize these applications in projection mapping and digital signage projects for local governments. It can also provide systems for waste sorting and library book reservations. For instance, the service provider can provide an app for exchanging information about local playgrounds. In this way, by deploying applications created by children to address the challenges of local governments, it can contribute to solving those challenges. Some or all of the above-described processes in the service provider may be performed using, for example, a generative AI, or without one. For example, the service provider can input applications created by children into a generative AI and have the AI ​​execute the optimal deployment method for addressing the challenges of the local government.

[0069] The support unit allows agent teachers to support children. The support unit can support children using, for example, an AI tutor. The support unit can also support children using a virtual assistant. For example, the support unit provides necessary support when children are learning in a virtual environment. This allows agent teachers to support children and effectively assist their learning. Some or all of the above processes in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input children's learning data into a generative AI and have the generative AI execute the optimal support method.

[0070] The service provider can offer services such as projection mapping and digital signage applications for local governments, waste sorting and library book reservation systems, and an information exchange app for local playgrounds. For example, the service provider can perform projection mapping for local governments. The service provider can also utilize digital signage. For example, the service provider can provide a waste sorting system. The service provider can also provide a library book reservation system. For example, the service provider can provide an information exchange app for local playgrounds. By providing services such as projection mapping and digital signage applications for local governments, waste sorting and library book reservation systems, and an information exchange app for local playgrounds, the service provider can contribute to solving local issues. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or not using generative AI. For example, the service provider can have generative AI execute the optimal solution to the local government's problems.

[0071] The data collection unit can estimate children's emotions and adjust the timing of collecting "what they want to do" based on the estimated emotions. For example, if children are excited, the data collection unit can immediately collect what they want to do and obtain information before their interest wanks. Conversely, if children are relaxed, the data collection unit can collect what they want to do at a slower pace and obtain more detailed information. For example, if children are tired, the data collection unit can collect what they want to do after a break and obtain information when their concentration has recovered. This allows for more effective collection of what children want to do by adjusting the collection timing based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can input children's emotion data into a generative AI and have the generative AI adjust the collection timing.

[0072] The data collection unit can analyze children's past activity history and select the optimal collection method. For example, the unit can prioritize collecting related "things they want to do" based on themes children have shown interest in in the past. It can also collect "things they want to do" related to similar themes by referring to the history of workshops and events children have participated in in the past. For example, the unit can efficiently acquire information by collecting data at specific times of day or on specific days of the week based on children's past activity history. This allows the unit to select the optimal collection method and efficiently collect information by analyzing past activity history. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input children's past activity data into an AI and have the AI ​​select the optimal collection method.

[0073] The data collection unit can filter data based on the children's current interests and areas of interest during the collection process. For example, the unit prioritizes collecting things that children "want to do" that are related to themes they are currently interested in. The unit can also filter relevant information and eliminate unnecessary information based on the children's areas of interest. For example, the unit can adjust the categories of information to be collected according to the children's current interests to obtain the most relevant information. This allows the unit to collect highly relevant information by filtering based on current interests and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the children's interest data into an AI and have the AI ​​perform the filtering.

[0074] The data collection unit can estimate children's emotions and, based on those estimated emotions, determine the priority of the "things they want to do" to collect. For example, if children are excited, the data collection unit will prioritize collecting "things they want to do" that can be done immediately. If children are relaxed, the data collection unit can prioritize collecting "things they want to do" that relate to long-term projects. For example, if children are tired, the data collection unit will prioritize collecting "things they want to do" that can be completed in a short time. This allows for more effective collection of "things they want to do" by determining priorities based on children's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input children's emotion data into a generative AI and have the generative AI perform the priority determination.

[0075] The data collection unit can prioritize collecting highly relevant information by considering the children's geographical location during the collection process. For example, the data collection unit can prioritize collecting things that children "want to do" that are related to their current location. The data collection unit can also collect information about nearby events and activities based on the children's geographical location. For example, the data collection unit can collect things that children "want to do" that are appropriate for the characteristics of the area, taking into account the children's location. This allows for the priority collection of highly relevant information by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the children's location data into an AI and have the AI ​​perform the collection of highly relevant information.

[0076] The data collection unit can analyze children's social media activities and collect relevant information during the collection process. For example, the collection unit can collect relevant "things they want to do" based on the content children share on social media. The collection unit can also analyze the activities of children's social media followers and friends and collect relevant information. For example, the collection unit can collect relevant "things they want to do" based on topics children show interest in on social media. This allows for the efficient collection of relevant information by analyzing social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input children's social media data into an AI and have the AI ​​collect relevant information.

[0077] The analysis unit can estimate the children's emotions and adjust the suggested solutions based on those emotions. For example, if the children are excited, the analysis unit can suggest an intuitive and visually appealing solution. If the children are relaxed, the analysis unit can suggest a solution that includes detailed explanations. If the children are tired, for example, the analysis unit can suggest a concise and easy-to-understand solution. By adjusting the suggested solutions based on the children's emotions, more effective solutions can be proposed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input the children's emotion data into a generative AI and have the generative AI adjust the suggested solutions.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on highly important information and propose specific solutions. Conversely, the analysis unit can perform a concise analysis on less important information and propose basic solutions. For example, the analysis unit can determine the priority of the analysis according to the importance of the collected information and perform the analysis efficiently. This allows for efficient analysis by adjusting the level of detail based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0079] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a code analysis algorithm to information related to programming and propose the optimal solution. Similarly, it can apply an image analysis algorithm to information related to digital art and propose the optimal solution. For example, it can apply an audio analysis algorithm to information related to music and propose the optimal solution. This allows the analysis unit to propose the optimal solution by applying different analysis algorithms depending on the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input information category data into the AI ​​and have the AI ​​apply different analysis algorithms.

[0080] The analysis unit can estimate the children's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the children are excited, the analysis unit will prioritize suggesting solutions that can be implemented immediately. If the children are relaxed, the analysis unit can prioritize suggesting solutions related to long-term projects. For example, if the children are tired, the analysis unit will prioritize suggesting solutions that can be completed in a short time. By prioritizing suggestions based on the children's emotions, more effective solutions can be suggested. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input the children's emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0081] The analysis unit can determine the priority of analysis based on the information submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected information and propose the latest solutions. Furthermore, the analysis unit can perform basic analysis on older information and update it as needed. For example, the analysis unit can determine the priority of analysis based on the submission date and perform the analysis efficiently. This allows for efficient analysis by determining the priority of analysis based on the information submission date. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input information submission date data into the AI ​​and have the AI ​​determine the analysis priority.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information and propose specific solutions. Furthermore, for less relevant information, the analysis unit can perform a concise analysis and propose basic solutions. For example, the analysis unit can adjust the order of analysis based on the relevance of the information to perform the analysis efficiently. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input information relevance data into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0083] The service provider can estimate children's emotions and adjust how the virtual environment is presented based on the estimated emotions. For example, if children are excited, the service provider can provide a visually appealing virtual environment. If children are relaxed, the service provider can provide a calming virtual environment. For example, if children are tired, the service provider can provide a simple and easy-to-use virtual environment. This allows for a more effective virtual environment to be provided by adjusting the presentation method based on children's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input children's emotion data into a generative AI and have the generative AI adjust the presentation method.

[0084] The service provider can adjust the level of detail of the virtual environment based on the importance of the proposed solution at the time of delivery. For example, the service provider can provide a detailed virtual environment for high-importance solutions, and a simplified virtual environment for low-importance solutions. For example, the service provider adjusts the level of detail of the virtual environment according to the importance of the proposed solution. This allows for efficient provision of virtual environments by adjusting the level of detail based on the importance of the solution. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input solution importance data into AI and have the AI ​​perform the adjustment of the level of detail of the virtual environment.

[0085] The service provider can provide different virtual environments depending on the category of the solution at the time of provision. For example, for a solution related to programming, the service provider can provide a virtual environment with programming tools installed. For a solution related to digital art, the service provider can provide a virtual environment with digital art tools installed. For example, for a solution related to music, the service provider can provide a virtual environment with digital music tools installed. By providing the optimal virtual environment according to the category of the solution, the learning effect of children can be enhanced. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input solution category data into AI and have the AI ​​perform the provision of different virtual environments.

[0086] The service provider can estimate children's emotions and prioritize virtual environments based on the estimated emotions. For example, if children are excited, the service provider will prioritize providing immediately available virtual environments. If children are relaxed, the service provider can prioritize providing virtual environments suitable for long-term projects. For example, if children are tired, the service provider will prioritize providing virtual environments that can be completed in a short time. This allows for the provision of more effective virtual environments by prioritizing based on children's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input children's emotion data into a generative AI and have the generative AI perform the priority determination.

[0087] The service provider can determine the priority of virtual environments based on the submission date of the solutions at the time of provision. For example, the service provider can provide a readily available virtual environment for recently proposed solutions. Alternatively, it can provide a basic virtual environment for older solutions. For example, the service provider can determine the priority of virtual environments based on the submission date and provide them efficiently. This allows for efficient provision of virtual environments by prioritizing based on the submission date of the solutions. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input solution submission date data into AI and have the AI ​​determine the priority of virtual environments.

[0088] The service provider can adjust the order of virtual environments based on the relevance of the solutions at the time of delivery. For example, the service provider can prioritize providing a detailed virtual environment for highly relevant solutions. Conversely, the service provider can provide a simplified virtual environment for less relevant solutions. For example, the service provider can adjust the order of virtual environments based on the relevance of the solutions and deliver them efficiently. This allows for efficient delivery of virtual environments by adjusting the order based on the relevance of the solutions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the relevance of solutions into AI and have AI perform the adjustment of the order of virtual environments.

[0089] The support unit can estimate the children's emotions and adjust its support methods based on the estimated emotions. For example, if the children are excited, the support unit can provide proactive support and quick feedback. If the children are relaxed, the support unit can provide support that includes detailed explanations. For example, if the children are tired, the support unit can provide concise and easy-to-understand support. This allows for more effective support by adjusting the support methods based on the children's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using a generative AI, or not. For example, the support unit can input the children's emotion data into a generative AI and have the generative AI adjust the support methods.

[0090] The support department can select appropriate support methods by referring to the children's past activity history when providing support. For example, the support department can provide relevant support by referring to the children's past participation history in workshops and events. The support department can also prioritize support on specific themes based on the children's past activity history. For example, the support department can analyze the children's past activity history and select the most effective support method. This allows the support department to select the optimal support method and provide support effectively by referring to past activity history. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input the children's past activity data into AI and have the AI ​​select the optimal support method.

[0091] The support unit can customize the means of support based on the children's current interests and areas of interest during support sessions. For example, the support unit can provide support related to themes that the children are currently interested in. The support unit can also provide relevant information and resources based on the children's areas of interest. For example, the support unit can customize the means of support according to the children's current interests to provide optimal support. This allows for the provision of optimal support by customizing the means of support based on current interests and areas of interest. Some or all of the above-described processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input data on the children's interests and have the AI ​​perform the customization of the means of support.

[0092] The support unit can estimate children's emotions and determine support priorities based on those estimates. For example, if children are excited, the support unit can provide immediate support and rapid feedback. If children are relaxed, the support unit can prioritize providing support that includes detailed explanations. For example, if children are tired, the support unit can prioritize providing concise and easy-to-understand support. This allows for more effective support by prioritizing based on children's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using or without a generative AI. For example, the support unit can input children's emotion data into a generative AI and have the generative AI determine the support priorities.

[0093] The support unit can select the optimal support method by considering the children's geographical location information during support. For example, the support unit can provide support related to the children's current location. Furthermore, based on the children's geographical location information, the support unit can provide support related to nearby resources and events. For example, the support unit can provide support tailored to the characteristics of the region by considering the children's location information. This allows for the selection of the optimal support method and effective support delivery by considering geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the children's location data into AI and have the AI ​​select the optimal support method.

[0094] The support unit can analyze children's social media activity and suggest support measures when providing assistance. For example, the support unit can provide relevant support based on the content children share on social media. The support unit can also analyze the activities of children's social media followers and friends and provide relevant support. For example, the support unit can provide relevant support based on topics children show interest in on social media. This allows for the efficient provision of relevant support by analyzing social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or not. For example, the support unit can input children's social media data into AI and have the AI ​​suggest support measures.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The analysis unit can estimate the children's emotions and adjust the suggested solutions based on those emotions. For example, if the children are excited, the analysis unit can suggest an intuitive and visually appealing solution. If the children are relaxed, the analysis unit can suggest a solution that includes detailed explanations. If the children are tired, for example, the analysis unit can suggest a concise and easy-to-understand solution. By adjusting the suggested solutions based on the children's emotions, more effective solutions can be proposed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input the children's emotion data into a generative AI and have the generative AI adjust the suggested solutions.

[0097] The service provider can adjust the level of detail of the virtual environment based on the importance of the proposed solution at the time of delivery. For example, the service provider can provide a detailed virtual environment for high-importance solutions, and a simplified virtual environment for low-importance solutions. For example, the service provider adjusts the level of detail of the virtual environment according to the importance of the proposed solution. This allows for efficient provision of virtual environments by adjusting the level of detail based on the importance of the solution. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input solution importance data into AI and have the AI ​​perform the adjustment of the level of detail of the virtual environment.

[0098] The data collection unit can estimate children's emotions and adjust the timing of collecting "what they want to do" based on the estimated emotions. For example, if children are excited, the data collection unit can immediately collect what they want to do and obtain information before their interest wanks. Conversely, if children are relaxed, the data collection unit can collect what they want to do at a slower pace and obtain more detailed information. For example, if children are tired, the data collection unit can collect what they want to do after a break and obtain information when their concentration has recovered. This allows for more effective collection of what children want to do by adjusting the collection timing based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can input children's emotion data into a generative AI and have the generative AI adjust the collection timing.

[0099] The support department can select appropriate support methods by referring to the children's past activity history when providing support. For example, the support department can provide relevant support by referring to the children's past participation history in workshops and events. The support department can also prioritize support on specific themes based on the children's past activity history. For example, the support department can analyze the children's past activity history and select the most effective support method. This allows the support department to select the optimal support method and provide support effectively by referring to past activity history. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input the children's past activity data into AI and have the AI ​​select the optimal support method.

[0100] The service provider can provide different virtual environments depending on the category of the solution at the time of provision. For example, for a solution related to programming, the service provider can provide a virtual environment with programming tools installed. For a solution related to digital art, the service provider can provide a virtual environment with digital art tools installed. For example, for a solution related to music, the service provider can provide a virtual environment with digital music tools installed. By providing the optimal virtual environment according to the category of the solution, the learning effect of children can be enhanced. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input solution category data into AI and have the AI ​​perform the provision of different virtual environments.

[0101] The data collection unit can filter data based on the children's current interests and areas of interest during the collection process. For example, the unit prioritizes collecting things that children "want to do" that are related to themes they are currently interested in. The unit can also filter relevant information and eliminate unnecessary information based on the children's areas of interest. For example, the unit can adjust the categories of information to be collected according to the children's current interests to obtain the most relevant information. This allows the unit to collect highly relevant information by filtering based on current interests and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the children's interest data into an AI and have the AI ​​perform the filtering.

[0102] The service provider can estimate children's emotions and adjust how the virtual environment is presented based on the estimated emotions. For example, if children are excited, the service provider can provide a visually appealing virtual environment. If children are relaxed, the service provider can provide a calming virtual environment. For example, if children are tired, the service provider can provide a simple and easy-to-use virtual environment. This allows for a more effective virtual environment to be provided by adjusting the presentation method based on children's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input children's emotion data into a generative AI and have the generative AI adjust the presentation method.

[0103] The support unit can analyze children's social media activity and suggest support measures when providing assistance. For example, the support unit can provide relevant support based on the content children share on social media. The support unit can also analyze the activities of children's social media followers and friends and provide relevant support. For example, the support unit can provide relevant support based on topics children show interest in on social media. This allows for the efficient provision of relevant support by analyzing social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or not. For example, the support unit can input children's social media data into AI and have the AI ​​suggest support measures.

[0104] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a code analysis algorithm to information related to programming and propose the optimal solution. Similarly, it can apply an image analysis algorithm to information related to digital art and propose the optimal solution. For example, it can apply an audio analysis algorithm to information related to music and propose the optimal solution. This allows the analysis unit to propose the optimal solution by applying different analysis algorithms depending on the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input information category data into the AI ​​and have the AI ​​apply different analysis algorithms.

[0105] The data collection unit can estimate children's emotions and, based on those estimated emotions, determine the priority of the "things they want to do" to collect. For example, if children are excited, the data collection unit will prioritize collecting "things they want to do" that can be done immediately. If children are relaxed, the data collection unit can prioritize collecting "things they want to do" that relate to long-term projects. For example, if children are tired, the data collection unit will prioritize collecting "things they want to do" that can be completed in a short time. This allows for more effective collection of "things they want to do" by determining priorities based on children's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input children's emotion data into a generative AI and have the generative AI perform the priority determination.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The data collection unit collects the activities that children want to do. The data collection unit can collect the activities that children want to do, for example, through questionnaires or interviews. Alternatively, the data collection unit can collect children's activities using sensor data. For example, the data collection unit can collect children's behavioral data and identify the activities they want to do. Step 2: The analysis unit analyzes the information collected by the collection unit and proposes a solution. The analysis unit can analyze the collected information using, for example, data mining techniques. Alternatively, the analysis unit can analyze the information using statistical analysis and propose a solution. For example, the analysis unit proposes the optimal solution based on the collected data. Step 3: The service provider provides a virtual environment based on the solution proposed by the analysis unit. The service provider can provide the virtual environment using, for example, VR or AR technology. The service provider can also provide a simulation environment. For example, the service provider can provide an environment in which children can learn within the virtual environment. Step 4: The support team provides support to the children within the virtual environment provided by the provider team. The support team can, for example, support the children using an AI tutor. The support team can also support the children using a virtual assistant. For example, the support team provides the necessary support when the children are learning within the virtual environment.

[0108] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0109] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0110] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0111] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and support unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect children's desired activities using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information and proposes solutions. The provision unit can provide a virtual environment, for example, by the control unit 46A of the smart device 14. The support unit can support children within the virtual environment, for example, by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0118] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0119] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0120] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0121] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0122] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0124] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect children's desired activities using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which analyzes the collected information and proposes a solution. The provision unit can provide a virtual environment, for example, in the control unit 46A of the smart glasses 214. The support unit can support children within the virtual environment, for example, in the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and support unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect children's desired activities using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information and proposes solutions. The provision unit can provide a virtual environment, for example, by the control unit 46A of the headset terminal 314. The support unit can support children within the virtual environment, for example, by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] As shown in Figure 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.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0152] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and support unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect the children's desired activities using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information and proposes a solution. The provision unit can provide a virtual environment, for example, by the control unit 46A of the robot 414. The support unit can support the children within the virtual environment, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0161] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0163] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0164] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0165] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0169] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0170] 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.

[0171] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0172] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0173] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0174] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0175] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0176] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0177] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0178] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0179] (Note 1) The collection department collects the activities that children want to do, An analysis unit analyzes the information collected by the aforementioned collection unit and proposes a solution, A provisioning unit that provides a virtual environment based on the solution proposed by the aforementioned analysis unit, The system includes a support unit that supports children within the virtual environment provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Applications created by children to address local government challenges will be deployed. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support unit is Agent teachers support the children The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We provide projection mapping and digital signage applications for local governments, waste sorting and library book reservation systems, and an app for exchanging information about local playgrounds. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We estimate the children's emotions and adjust the timing of collecting their "what they want to do" based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the children's past activity history and select the appropriate collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting data, filtering is performed based on the children's current interests and areas of concern. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the children's emotions and, based on those estimated emotions, determine the priority of the things they "want to do." The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, the collection process prioritizes the collection of highly relevant information, taking into account the children's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During collection, we analyze children's social media activity and gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, We estimate the children's emotions and adjust the proposed solutions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We estimate the children's emotions and prioritize proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the children's emotions and adjusts how the virtual environment is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the solution, adjust the level of detail in the virtual environment based on the importance of the proposed solution. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the solution, different virtual environments will be offered depending on the category of the solution. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the children's emotions and prioritizes the virtual environment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the solution, we will prioritize the virtual environment based on when the solution was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the solution, the order of the virtual environments will be adjusted based on the relevance of the solutions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned support unit is We estimate the children's emotions and adjust our support methods based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit is When providing support, refer to the children's past activity history to select the appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is When providing support, customize the methods of support based on the children's current interests and areas of focus. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is We estimate the children's emotions and determine the priority of support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is When providing support, the most suitable support method will be selected considering the children's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is During support sessions, we analyze children's social media activity and propose support strategies. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects the activities that children want to do, An analysis unit analyzes the information collected by the aforementioned collection unit and proposes a solution, A provisioning unit that provides a virtual environment based on the solution proposed by the aforementioned analysis unit, The system includes a support unit that supports children within the virtual environment provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned supply unit is, Applications created by children to address local government challenges will be deployed. The system according to feature 1.

3. The aforementioned support unit is Agent teachers support the children The system according to feature 1.

4. The aforementioned supply unit is, We provide projection mapping and digital signage applications for local governments, waste sorting and library book reservation systems, and an app for exchanging information about local playgrounds. The system according to feature 1.

5. The aforementioned collection unit is Analyze the children's past activity history and select the appropriate collection method. The system according to feature 1.

6. The aforementioned collection unit is When collecting data, filtering is performed based on the children's current interests and areas of concern. The system according to feature 1.

7. The aforementioned collection unit is When collecting data, the collection process prioritizes the collection of highly relevant information, taking into account the children's geographical location. The system according to feature 1.

8. The aforementioned collection unit is During collection, we analyze children's social media activity and gather relevant information. The system according to feature 1.

Images