system

Abstract

The system according to this embodiment aims to enable senior citizens to effectively utilize digital devices and reduce the workload of local governments. [Solution] The system according to the embodiment comprises a learning support unit, a family collaboration unit, a gamification unit, and a data analysis unit. The learning support unit provides customized guides and tutorials based on the user's learning pace and level of understanding. The family collaboration unit allows family members to check and support the user's learning progress. The gamification unit provides gamification to make learning fun and sustainable. The data analysis unit analyzes the user's learning data and provides an individually optimized learning plan.

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, it is difficult for the senior layer to effectively utilize digital devices, and there is a problem that the workload of local governments increases.

[0005] The system according to the embodiment aims to enable the senior layer to effectively utilize digital devices and reduce the workload of local governments.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning support unit, a family collaboration unit, a gamification unit, and a data analysis unit. The learning support unit provides customized guides and tutorials based on the user's learning pace and level of understanding. The family collaboration unit allows family members to monitor and support the user's learning progress. The gamification unit provides gamification to make learning enjoyable and sustainable. The data analysis unit analyzes the user's learning data and provides individually optimized learning plans. [Effects of the Invention]

[0007] The system according to this embodiment can enable senior citizens to effectively utilize digital devices and reduce the workload of local governments. [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 labeled 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 reception 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 reception 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 system according to an embodiment of the present invention is an AI agent system that provides customized learning content for seniors. This AI agent system supports learning in stages, from simple operations to complex operations, and even how to use local government services, according to the user's learning pace and level of understanding. For example, the AI ​​agent system can teach users how to start with basic smartphone operations, send and receive emails, use the internet, online shopping, and how to use local government online services. The AI ​​agent system also incorporates family collaboration support functions, gamification, and individual optimization through data analysis to maximize learning effectiveness. The family collaboration support function allows family members to check and support the user's learning progress. Gamification elements make learning fun and sustainable. Furthermore, data analysis analyzes the user's learning data and provides an individually optimized learning plan. This mechanism allows seniors to freely use digital devices and make the most of local government services. This improves the quality of daily life and bridges the digital divide. It also creates an environment where seniors can participate and contribute more to society. In addition, it reduces the workload of local governments and helps to control personnel. This allows the AI ​​agent system to enable seniors to freely use digital devices and make the most of locally tailored services.

[0029] The AI ​​agent system according to this embodiment comprises a learning support unit, a family collaboration unit, a gamification unit, and a data analysis unit. The learning support unit provides customized guides and tutorials based on the user's learning pace and level of understanding. For example, the learning support unit measures the user's learning pace and adjusts the content of the guides and tutorials according to the rate of progress. The learning support unit can also evaluate the user's level of understanding and customize the learning content based on test results and quiz accuracy rates. For example, the learning support unit provides content tailored to the user's individual learning goals and learning style to make it easy for the user to understand. The family collaboration unit provides a function that allows family members to check and support the user's learning progress. For example, the family collaboration unit notifies family members of the user's learning progress and sends alerts to family members when support is needed. The family collaboration unit can also provide a dashboard that allows family members to check the user's learning progress in real time. For example, the family collaboration unit allows family members to monitor the user's learning progress and provide support as needed. The gamification unit provides elements to make learning fun and sustainable. The Gamification Department enhances user motivation to learn by incorporating elements such as point systems, badges, and level-ups. They can also provide learning content that incorporates game elements to make learning enjoyable. For example, users can earn points as they progress through their studies, achieving badges and level-ups to gain a sense of accomplishment. The Data Analysis Department analyzes user learning data and provides individually optimized learning plans. This involves collecting and analyzing data such as user learning history, test results, and study time. Based on user learning data, they can also provide optimal learning plans tailored to individual learning goals and styles. For example, they can analyze user learning data and suggest what to study next and effective learning methods.As a result, the AI ​​agent system according to this embodiment can provide customized guides and tutorials based on the user's learning pace and level of understanding, and maximize learning effectiveness through family collaboration, gamification, and data analysis.

[0030] The Learning Support Department provides customized guides and tutorials based on the user's learning pace and understanding. Specifically, the Learning Support Department monitors learning time and progress speed in real time to measure the user's learning pace. For example, if a user is spending too much time on a particular task, the Learning Support Department adjusts the difficulty level of that task or provides additional explanations and hints. It also regularly conducts quizzes and tests to assess the user's understanding and analyzes the results. This allows the department to identify where the user is struggling and provide necessary supplementary materials and review content. Furthermore, the Learning Support Department customizes materials according to the user's learning style. For example, users who prefer visual learning are provided with materials that heavily utilize diagrams and graphs, while users who prefer auditory learning are provided with audio guides and podcast-style materials. This allows users to learn in a way that is best suited to them, thereby enhancing learning effectiveness. The Learning Support Department uses AI to analyze this data and automatically generate the optimal learning plan for each user. For example, the AI ​​predicts, based on past learning data, what pace of learning is most effective for the user and proposes a learning schedule based on that prediction. This allows users to learn efficiently and without difficulty.

[0031] The Family Collaboration section provides features that allow families to monitor and support the user's learning progress. Specifically, it has a function to notify families of the user's learning progress in real time. For example, it sends alerts to families when the user achieves a specific learning goal or when they are experiencing difficulties in their studies. The Family Collaboration section also provides a dashboard that allows families to check the user's learning progress in detail. This dashboard displays the user's study time, progress, test results, etc., at a glance, allowing families to provide appropriate support based on this information. Furthermore, the Family Collaboration section provides resources and advice for families to support the user's learning. For example, it suggests specific methods and tools on how families should encourage and advise the user. The Family Collaboration section also has features to promote communication among family members. For example, it provides a chat function for families to exchange opinions on the user's learning progress and a forum function for sharing information related to learning. This allows the whole family to support the user's learning and enhance the learning effect. The Family Collaboration section uses AI to suggest the most effective timing and methods for family support. For example, the AI ​​analyzes the user's learning data to predict when and what kind of support the family should provide, and then sends notifications to the family based on that prediction. This allows the family to provide effective support at the right time.

[0032] The Gamification Department provides elements to make learning fun and sustainable. Specifically, the Gamification Department incorporates elements such as point systems, badges, and level-ups to increase user motivation to learn. For example, users can earn points each time they complete a specific task, and by accumulating these points, they can achieve badges and level ups. The Gamification Department also provides learning content that incorporates game elements so that users can progress through learning while having fun. For example, it provides educational materials in the form of adventure games where the story progresses as the user learns, and mini-games in the form of quizzes. This allows users to enjoy learning in a game-like way and maintain their motivation to learn. Furthermore, the Gamification Department also provides elements such as ranking systems where users can compete with each other, and team battles where users can cooperate to complete tasks. This allows users to increase their motivation to learn by competing or cooperating with other users. The Gamification Department uses AI to analyze user learning data and propose the most suitable game elements. For example, the AI ​​predicts which game elements will be most effective based on the user's learning history and interests, and customizes the learning content based on that prediction. This allows users to enjoy learning in a way that suits them best, thereby enhancing their learning effectiveness.

[0033] The Data Analysis Department analyzes users' learning data and provides individually optimized learning plans. Specifically, the Data Analysis Department collects and analyzes data such as users' learning history, test results, and study time. For example, it analyzes in detail how quickly users are learning and where they are struggling. Based on the user's learning data, the Data Analysis Department provides optimal learning plans tailored to individual learning goals and learning styles. For example, if a user has difficulty in a particular area, it will propose a learning plan that focuses on that area. It can also propose a schedule that meets the user's needs if they want to learn intensively in a short period of time. Furthermore, the Data Analysis Department uses AI to analyze users' learning data in real time and continuously optimize learning plans. For example, the AI ​​suggests what to study next and effective learning methods based on the user's learning data. It can also flexibly adjust the learning plan according to the user's learning progress. This allows users to always learn based on the optimal learning plan and maximize learning effectiveness. Based on the user's learning data, the Data Analysis Department also proposes long-term learning goals and career plans. For example, the system can propose a learning plan tailored to the user's future career goals and outline specific steps to achieve them. This allows users to effectively pursue not only short-term learning objectives but also long-term career goals.

[0034] The learning support unit can analyze the user's past learning history and select the optimal learning method. For example, the learning support unit can suggest what the user should learn next based on what they have learned in the past. The learning support unit can also identify and suggest effective learning methods from the user's past learning history. Furthermore, the learning support unit can analyze the user's past learning history and adjust the learning pace. This improves learning efficiency by selecting the optimal learning method based on past learning history. Some or all of the above processes in the learning support unit may be performed using AI, for example, or without AI. For example, the learning support unit can input the user's past learning data into a generating AI and have the generating AI select the optimal learning method.

[0035] The learning support unit can provide customized guides based on the user's current lifestyle and areas of interest during learning support. For example, the learning support unit can suggest relevant learning content based on the user's current lifestyle. It can also provide learning content that will pique the user's interest based on their areas of interest. Furthermore, the learning support unit can adjust the timing of learning to match the user's daily rhythm. This enhances the effectiveness of learning by providing customized guides based on the user's lifestyle and areas of interest. Some or all of the above processes in the learning support unit may be performed using AI, for example, or without AI. For example, the learning support unit can input the user's lifestyle data into a generating AI and have the generating AI perform the task of providing customized guides.

[0036] The learning support unit can prioritize providing highly relevant learning content by considering the user's geographical location during learning support. For example, the learning support unit can provide region-specific learning content based on the user's geographical location. It can also suggest learning content related to local events and services, taking the user's geographical location into consideration. Furthermore, the learning support unit can provide learning content related to local culture and history based on the user's geographical location. By providing highly relevant learning content while considering the user's geographical location, the effectiveness of learning can be enhanced. Some or all of the above processing in the learning support unit may be performed using AI, for example, or without AI. For example, the learning support unit can input the user's geographical location information into a generating AI and have the generating AI provide highly relevant learning content.

[0037] The learning support unit can analyze a user's social media activity and provide relevant learning content during learning support. For example, the learning support unit can identify topics of interest from the user's social media activity and provide relevant learning content. The learning support unit can also analyze the user's social media activity and suggest content that will increase learning motivation. Furthermore, the learning support unit can share the user's learning progress and provide feedback based on the user's social media activity. In this way, the effectiveness of learning can be enhanced by analyzing the user's social media activity and providing relevant learning content. Some or all of the above processes in the learning support unit may be performed using AI, for example, or not using AI. For example, the learning support unit can input the user's social media data into a generating AI and have the generating AI provide relevant learning content.

[0038] The family collaboration unit can provide optimal support methods by referring to the user's past learning progress during family collaboration. For example, the family collaboration unit can propose specific support methods to the family based on the user's past learning progress. The family collaboration unit can also refer to the user's past learning progress and suggest effective support timings to the family. Furthermore, the family collaboration unit can analyze the user's past learning progress and provide appropriate feedback to the family. This makes family support more effective by providing optimal support methods by referring to the user's past learning progress. Some or all of the above processes in the family collaboration unit may be performed using AI, for example, or not using AI. For example, the family collaboration unit can input the user's past learning data into a generating AI and have the generating AI perform the task of providing optimal support methods.

[0039] The Family Coordination Unit can provide the optimal support method when coordinating with a family, taking into account the user's geographical location. For example, the Family Coordination Unit can suggest nearby support resources to the family based on the user's geographical location. It can also suggest appropriate support timing to the family, taking into account the user's geographical location. Furthermore, the Family Coordination Unit can provide region-specific support methods to the family based on the user's geographical location. This makes family support more effective by providing the optimal support method, taking into account the user's geographical location. Some or all of the above processing in the Family Coordination Unit may be performed using AI, for example, or without AI. For example, the Family Coordination Unit can input the user's geographical location into a generating AI and have the generating AI perform the task of providing the optimal support method.

[0040] The gamification unit can provide optimal game elements by referring to the user's past learning history during gamification. For example, the gamification unit can provide game elements related to the learning content based on the user's past learning history. The gamification unit can also refer to the user's past learning history and provide game elements according to the user's learning progress. Furthermore, the gamification unit can analyze the user's past learning history and provide game elements that increase learning motivation. In this way, the effectiveness of learning can be enhanced by providing optimal game elements by referring to the user's past learning history. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without using AI. For example, the gamification unit can input the user's past learning data into a generating AI and have the generating AI perform the task of providing optimal game elements.

[0041] The gamification unit can provide highly relevant game elements during gamification, taking into account the user's geographical location information. For example, the gamification unit can provide region-specific game elements based on the user's geographical location information. It can also suggest game elements related to local events and services, taking into account the user's geographical location information. Furthermore, the gamification unit can provide game elements related to local culture and history based on the user's geographical location information. By providing highly relevant game elements while considering the user's geographical location information, the learning effect can be enhanced. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without AI. For example, the gamification unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant game elements.

[0042] The data analysis unit can provide an optimal learning plan by referring to past learning data during data analysis. For example, the data analysis unit can suggest what the user should learn next based on their past learning data. The data analysis unit can also refer to the user's past learning data to identify and suggest effective learning methods. Furthermore, the data analysis unit can analyze the user's past learning data and adjust the learning pace. This enhances the effectiveness of learning by providing an optimal learning plan based on past learning data. Some or all of the above processes in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input the user's past learning data into a generating AI and have the generating AI provide an optimal learning plan.

[0043] The data analysis unit can customize learning plans based on the user's current lifestyle during data analysis. For example, the data analysis unit can suggest relevant learning content based on the user's current lifestyle. It can also adjust the timing of learning to match the user's daily rhythm. Furthermore, the data analysis unit can adjust the pace of learning, taking into account the user's current lifestyle. This allows for enhanced learning effectiveness by customizing learning plans based on the user's current lifestyle. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For instance, the data analysis unit can input user lifestyle data into a generating AI and have the generating AI provide a customized learning plan.

[0044] The data analysis department can provide an optimal learning plan by considering the user's geographical location during data analysis. For example, the data analysis department can provide a region-specific learning plan based on the user's geographical location. It can also propose learning plans related to local events and services, taking the user's geographical location into consideration. Furthermore, the data analysis department can provide learning plans related to local culture and history based on the user's geographical location. This enhances the effectiveness of learning by providing an optimal learning plan that considers the user's geographical location. Some or all of the above processing in the data analysis department may be performed using AI, for example, or without AI. For example, the data analysis department can input the user's geographical location into a generating AI and have the generating AI provide an optimal learning plan.

[0045] The data analysis department can analyze users' social media activity during data analysis to optimize learning plans. For example, the data analysis department can identify topics of interest from users' social media activity and provide relevant learning plans. The data analysis department can also analyze users' social media activity and suggest content that will increase learning motivation. Furthermore, the data analysis department can share learning progress and provide feedback based on users' social media activity. In this way, the effectiveness of learning can be improved by analyzing users' social media activity and optimizing learning plans. Some or all of the above processes in the data analysis department may be performed using AI, for example, or not. For example, the data analysis department can input user social media data into a generating AI and have the generating AI perform the optimization of learning plans.

[0046] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0047] The learning support unit can monitor the user's health status and adjust learning content based on that status. For example, if the user is tired, it can provide simple learning content that can be completed in a short time. Conversely, if the user is energetic, it can provide learning content that requires concentration. Furthermore, if the user is feeling unwell, it can temporarily suspend learning and send a notification encouraging rest. In this way, the learning effect can be enhanced by adjusting learning content according to the user's health status.

[0048] The learning support system can customize learning content based on the user's hobbies and interests. For example, if a user is interested in music, it can provide music-related learning content. Similarly, if a user is interested in sports, it can provide sports-related content. Furthermore, if a user is interested in cooking, it can provide cooking-related content. By customizing learning content based on the user's hobbies and interests, the effectiveness of learning can be enhanced.

[0049] The learning support unit can monitor the user's learning environment and provide an optimal learning environment. For example, if the user is in a noisy environment, it can provide noise cancellation to create a focused environment. If the user is in a dark environment, it can adjust the screen brightness to provide an eye-friendly environment. Furthermore, if the user is in a cold environment, it can send a notification recommending a warm drink. By optimizing the user's learning environment in this way, the effectiveness of learning can be enhanced.

[0050] The Learning Support Department can provide a dashboard that visualizes learning progress based on the user's learning history. For example, it can display what the user has learned in the past using graphs and charts, allowing users to see their learning progress at a glance. It can also display a list of goals the user has achieved and skills they have acquired. Furthermore, it can suggest what the user should learn next and support them in planning their studies. By visualizing the user's learning history in this way, the effectiveness of learning can be enhanced.

[0051] The learning support unit can customize learning content according to the user's learning style. For example, users with a visual learning style can be provided with learning content that makes extensive use of diagrams and illustrations. Users with an auditory learning style can be provided with learning content in the form of audio guides or podcasts. Furthermore, users with an experiential learning style can be provided with practical exercises and simulations. In this way, the effectiveness of learning can be enhanced by customizing learning content according to the user's learning style.

[0052] The Learning Support Department can provide a dashboard that visualizes learning progress based on the user's learning history. For example, it can display what the user has learned in the past using graphs and charts, allowing users to see their learning progress at a glance. It can also display a list of goals the user has achieved and skills they have acquired. Furthermore, it can suggest what the user should learn next and support them in planning their studies. By visualizing the user's learning history in this way, the effectiveness of learning can be enhanced.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The learning support unit provides customized guides and tutorials based on the user's learning pace and understanding. For example, it measures the user's learning pace and adjusts the content of guides and tutorials according to their progress. It can also evaluate the user's understanding and customize learning content based on test results and quiz accuracy. Step 2: The family collaboration section provides functions that allow family members to monitor and support the user's learning progress. For example, it can notify family members of the user's learning progress and send alerts when support is needed. It can also provide a dashboard that allows family members to check the user's learning progress in real time. Step 3: The gamification team provides elements to make learning fun and sustainable. For example, they incorporate elements such as point systems, badges, and level-ups to increase user motivation. They also provide learning content that incorporates game elements so that users can enjoy learning as they progress. Step 4: The data analysis department analyzes the user's learning data and provides an individually optimized learning plan. For example, it collects and analyzes data such as the user's learning history, test results, and study time. Based on this, it provides an optimal learning plan that matches the individual's learning goals and learning style.

[0055] (Example of form 2) The system according to an embodiment of the present invention is an AI agent system that provides customized learning content for seniors. This AI agent system supports learning in stages, from simple operations to complex operations, and even how to use local government services, according to the user's learning pace and level of understanding. For example, the AI ​​agent system can teach users how to start with basic smartphone operations, send and receive emails, use the internet, online shopping, and how to use local government online services. The AI ​​agent system also incorporates family collaboration support functions, gamification, and individual optimization through data analysis to maximize learning effectiveness. The family collaboration support function allows family members to check and support the user's learning progress. Gamification elements make learning fun and sustainable. Furthermore, data analysis analyzes the user's learning data and provides an individually optimized learning plan. This mechanism allows seniors to freely use digital devices and make the most of local government services. This improves the quality of daily life and bridges the digital divide. It also creates an environment where seniors can participate and contribute more to society. In addition, it reduces the workload of local governments and helps to control personnel. This allows the AI ​​agent system to enable seniors to freely use digital devices and make the most of locally tailored services.

[0056] The AI ​​agent system according to this embodiment comprises a learning support unit, a family collaboration unit, a gamification unit, and a data analysis unit. The learning support unit provides customized guides and tutorials based on the user's learning pace and level of understanding. For example, the learning support unit measures the user's learning pace and adjusts the content of the guides and tutorials according to the rate of progress. The learning support unit can also evaluate the user's level of understanding and customize the learning content based on test results and quiz accuracy rates. For example, the learning support unit provides content tailored to the user's individual learning goals and learning style to make it easy for the user to understand. The family collaboration unit provides a function that allows family members to check and support the user's learning progress. For example, the family collaboration unit notifies family members of the user's learning progress and sends alerts to family members when support is needed. The family collaboration unit can also provide a dashboard that allows family members to check the user's learning progress in real time. For example, the family collaboration unit allows family members to monitor the user's learning progress and provide support as needed. The gamification unit provides elements to make learning fun and sustainable. The Gamification Department enhances user motivation to learn by incorporating elements such as point systems, badges, and level-ups. They can also provide learning content that incorporates game elements to make learning enjoyable. For example, users can earn points as they progress through their studies, achieving badges and level-ups to gain a sense of accomplishment. The Data Analysis Department analyzes user learning data and provides individually optimized learning plans. This involves collecting and analyzing data such as user learning history, test results, and study time. Based on user learning data, they can also provide optimal learning plans tailored to individual learning goals and styles. For example, they can analyze user learning data and suggest what to study next and effective learning methods.As a result, the AI ​​agent system according to this embodiment can provide customized guides and tutorials based on the user's learning pace and level of understanding, and maximize learning effectiveness through family collaboration, gamification, and data analysis.

[0057] The Learning Support Department provides customized guides and tutorials based on the user's learning pace and understanding. Specifically, the Learning Support Department monitors learning time and progress speed in real time to measure the user's learning pace. For example, if a user is spending too much time on a particular task, the Learning Support Department adjusts the difficulty level of that task or provides additional explanations and hints. It also regularly conducts quizzes and tests to assess the user's understanding and analyzes the results. This allows the department to identify where the user is struggling and provide necessary supplementary materials and review content. Furthermore, the Learning Support Department customizes materials according to the user's learning style. For example, users who prefer visual learning are provided with materials that heavily utilize diagrams and graphs, while users who prefer auditory learning are provided with audio guides and podcast-style materials. This allows users to learn in a way that is best suited to them, thereby enhancing learning effectiveness. The Learning Support Department uses AI to analyze this data and automatically generate the optimal learning plan for each user. For example, the AI ​​predicts, based on past learning data, what pace of learning is most effective for the user and proposes a learning schedule based on that prediction. This allows users to learn efficiently and without difficulty.

[0058] The Family Collaboration section provides features that allow families to monitor and support the user's learning progress. Specifically, it has a function to notify families of the user's learning progress in real time. For example, it sends alerts to families when the user achieves a specific learning goal or when they are experiencing difficulties in their studies. The Family Collaboration section also provides a dashboard that allows families to check the user's learning progress in detail. This dashboard displays the user's study time, progress, test results, etc., at a glance, allowing families to provide appropriate support based on this information. Furthermore, the Family Collaboration section provides resources and advice for families to support the user's learning. For example, it suggests specific methods and tools on how families should encourage and advise the user. The Family Collaboration section also has features to promote communication among family members. For example, it provides a chat function for families to exchange opinions on the user's learning progress and a forum function for sharing information related to learning. This allows the whole family to support the user's learning and enhance the learning effect. The Family Collaboration section uses AI to suggest the most effective timing and methods for family support. For example, the AI ​​analyzes the user's learning data to predict when and what kind of support the family should provide, and then sends notifications to the family based on that prediction. This allows the family to provide effective support at the right time.

[0059] The Gamification Department provides elements to make learning fun and sustainable. Specifically, the Gamification Department incorporates elements such as point systems, badges, and level-ups to increase user motivation to learn. For example, users can earn points each time they complete a specific task, and by accumulating these points, they can achieve badges and level ups. The Gamification Department also provides learning content that incorporates game elements so that users can progress through learning while having fun. For example, it provides educational materials in the form of adventure games where the story progresses as the user learns, and mini-games in the form of quizzes. This allows users to enjoy learning in a game-like way and maintain their motivation to learn. Furthermore, the Gamification Department also provides elements such as ranking systems where users can compete with each other, and team battles where users can cooperate to complete tasks. This allows users to increase their motivation to learn by competing or cooperating with other users. The Gamification Department uses AI to analyze user learning data and propose the most suitable game elements. For example, the AI ​​predicts which game elements will be most effective based on the user's learning history and interests, and customizes the learning content based on that prediction. This allows users to enjoy learning in a way that suits them best, thereby enhancing their learning effectiveness.

[0060] The Data Analysis Department analyzes users' learning data and provides individually optimized learning plans. Specifically, the Data Analysis Department collects and analyzes data such as users' learning history, test results, and study time. For example, it analyzes in detail how quickly users are learning and where they are struggling. Based on the user's learning data, the Data Analysis Department provides optimal learning plans tailored to individual learning goals and learning styles. For example, if a user has difficulty in a particular area, it will propose a learning plan that focuses on that area. It can also propose a schedule that meets the user's needs if they want to learn intensively in a short period of time. Furthermore, the Data Analysis Department uses AI to analyze users' learning data in real time and continuously optimize learning plans. For example, the AI ​​suggests what to study next and effective learning methods based on the user's learning data. It can also flexibly adjust the learning plan according to the user's learning progress. This allows users to always learn based on the optimal learning plan and maximize learning effectiveness. Based on the user's learning data, the Data Analysis Department also proposes long-term learning goals and career plans. For example, the system can propose a learning plan tailored to the user's future career goals and outline specific steps to achieve them. This allows users to effectively pursue not only short-term learning objectives but also long-term career goals.

[0061] The learning support unit can estimate the user's emotions and adjust the content of guides and tutorials based on the estimated emotions. For example, if the user is stressed, the learning support unit can provide a simple and intuitive guide with minimal steps. If the user is relaxed, the learning support unit can provide a guide with detailed explanations and delve deeper into the learning content. Furthermore, if the user is excited, the learning support unit can provide a tutorial with visually appealing effects. In this way, the effectiveness of learning can be enhanced by adjusting the content of guides and tutorials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 learning support unit may be performed using AI or not using AI. For example, the learning support unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0062] The learning support unit can analyze the user's past learning history and select the optimal learning method. For example, the learning support unit can suggest what the user should learn next based on what they have learned in the past. The learning support unit can also identify and suggest effective learning methods from the user's past learning history. Furthermore, the learning support unit can analyze the user's past learning history and adjust the learning pace. This improves learning efficiency by selecting the optimal learning method based on past learning history. Some or all of the above processes in the learning support unit may be performed using AI, for example, or without AI. For example, the learning support unit can input the user's past learning data into a generating AI and have the generating AI select the optimal learning method.

[0063] The learning support unit can provide customized guides based on the user's current lifestyle and areas of interest during learning support. For example, the learning support unit can suggest relevant learning content based on the user's current lifestyle. It can also provide learning content that will pique the user's interest based on their areas of interest. Furthermore, the learning support unit can adjust the timing of learning to match the user's daily rhythm. This enhances the effectiveness of learning by providing customized guides based on the user's lifestyle and areas of interest. Some or all of the above processes in the learning support unit may be performed using AI, for example, or without AI. For example, the learning support unit can input the user's lifestyle data into a generating AI and have the generating AI perform the task of providing customized guides.

[0064] The learning support unit can estimate the user's emotions and adjust the learning pace based on the estimated emotions. For example, if the user is stressed, the learning support unit can slow down the learning pace to provide time for deeper understanding. Conversely, if the user is relaxed, the learning support unit can speed up the learning pace to promote efficient learning. Furthermore, if the user is excited, the learning support unit can adjust the learning pace to maintain concentration. In this way, the effectiveness of learning can be enhanced by adjusting the learning pace according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 learning support unit may be performed using AI, or not using AI. For example, the learning support unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0065] The learning support unit can prioritize providing highly relevant learning content by considering the user's geographical location during learning support. For example, the learning support unit can provide region-specific learning content based on the user's geographical location. It can also suggest learning content related to local events and services, taking the user's geographical location into consideration. Furthermore, the learning support unit can provide learning content related to local culture and history based on the user's geographical location. By providing highly relevant learning content while considering the user's geographical location, the effectiveness of learning can be enhanced. Some or all of the above processing in the learning support unit may be performed using AI, for example, or without AI. For example, the learning support unit can input the user's geographical location information into a generating AI and have the generating AI provide highly relevant learning content.

[0066] The learning support unit can analyze a user's social media activity and provide relevant learning content during learning support. For example, the learning support unit can identify topics of interest from the user's social media activity and provide relevant learning content. The learning support unit can also analyze the user's social media activity and suggest content that will increase learning motivation. Furthermore, the learning support unit can share the user's learning progress and provide feedback based on the user's social media activity. In this way, the effectiveness of learning can be enhanced by analyzing the user's social media activity and providing relevant learning content. Some or all of the above processes in the learning support unit may be performed using AI, for example, or not using AI. For example, the learning support unit can input the user's social media data into a generating AI and have the generating AI provide relevant learning content.

[0067] The family communication unit can estimate the user's emotions and adjust the content of notifications sent to the family based on the estimated emotions. For example, if the user is feeling stressed, the family communication unit can send a notification to the family requesting support. It can also send a notification to the family reporting on the user's learning progress if the user is relaxed. Furthermore, if the user is excited, the family communication unit can send a notification sharing learning achievements with the family. This allows for more effective family support by adjusting notifications according to the user'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-described processes in the family communication unit may be performed using AI or not. For example, the family communication unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0068] The family collaboration unit can provide optimal support methods by referring to the user's past learning progress during family collaboration. For example, the family collaboration unit can propose specific support methods to the family based on the user's past learning progress. The family collaboration unit can also refer to the user's past learning progress and suggest effective support timings to the family. Furthermore, the family collaboration unit can analyze the user's past learning progress and provide appropriate feedback to the family. This makes family support more effective by providing optimal support methods by referring to the user's past learning progress. Some or all of the above processes in the family collaboration unit may be performed using AI, for example, or not using AI. For example, the family collaboration unit can input the user's past learning data into a generating AI and have the generating AI perform the task of providing optimal support methods.

[0069] The family collaboration unit can estimate the user's emotions and adjust the timing of support requests to family members based on the estimated emotions. For example, if the user is feeling stressed, the family collaboration unit can send an immediate support request to the family. It can also send periodic support requests to family members if the user is relaxed. Furthermore, if the user is excited, the family collaboration unit can adjust the timing of sharing learning outcomes with the family. This makes family support more effective by adjusting the timing of support requests to family members according to the user'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 family collaboration unit may be performed using AI or not. For example, the family collaboration unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0070] The Family Coordination Unit can provide the optimal support method when coordinating with a family, taking into account the user's geographical location. For example, the Family Coordination Unit can suggest nearby support resources to the family based on the user's geographical location. It can also suggest appropriate support timing to the family, taking into account the user's geographical location. Furthermore, the Family Coordination Unit can provide region-specific support methods to the family based on the user's geographical location. This makes family support more effective by providing the optimal support method, taking into account the user's geographical location. Some or all of the above processing in the Family Coordination Unit may be performed using AI, for example, or without AI. For example, the Family Coordination Unit can input the user's geographical location into a generating AI and have the generating AI perform the task of providing the optimal support method.

[0071] The gamification unit can estimate the user's emotions and adjust the content of gamification elements based on the estimated user emotions. For example, if the user is stressed, the gamification unit can provide simple and relaxing game elements. If the user is relaxed, the gamification unit can also provide challenging game elements. Furthermore, if the user is excited, the gamification unit can provide visually stimulating game elements. By adjusting the content of gamification elements according to the user's emotions, the learning effect can be enhanced. Emotion estimation is achieved using an emotion estimation function, for example, using 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 gamification unit may be performed using AI, for example, or without AI. For example, the gamification unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0072] The gamification unit can provide optimal game elements by referring to the user's past learning history during gamification. For example, the gamification unit can provide game elements related to the learning content based on the user's past learning history. The gamification unit can also refer to the user's past learning history and provide game elements according to the user's learning progress. Furthermore, the gamification unit can analyze the user's past learning history and provide game elements that increase learning motivation. In this way, the effectiveness of learning can be enhanced by providing optimal game elements by referring to the user's past learning history. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without using AI. For example, the gamification unit can input the user's past learning data into a generating AI and have the generating AI perform the task of providing optimal game elements.

[0073] The gamification unit can estimate the user's emotions and adjust the game difficulty based on the estimated emotions. For example, if the user is stressed, the gamification unit can lower the game difficulty to help them relax. Conversely, if the user is relaxed, the gamification unit can increase the game difficulty to provide a challenging experience. Furthermore, if the user is excited, the gamification unit can adjust the game difficulty to maintain their concentration. This allows for improved learning effectiveness by adjusting the game difficulty according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 gamification unit may be performed using AI, or not using AI. For example, the gamification unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The gamification unit can provide highly relevant game elements during gamification, taking into account the user's geographical location information. For example, the gamification unit can provide region-specific game elements based on the user's geographical location information. It can also suggest game elements related to local events and services, taking into account the user's geographical location information. Furthermore, the gamification unit can provide game elements related to local culture and history based on the user's geographical location information. By providing highly relevant game elements while considering the user's geographical location information, the learning effect can be enhanced. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without AI. For example, the gamification unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant game elements.

[0075] The data analysis unit can estimate the user's emotions and adjust the analysis method of the training data based on the estimated user emotions. For example, if the user is stressed, the data analysis unit can analyze the data using a simple analysis method. If the user is relaxed, the data analysis unit can also analyze the data using a more detailed analysis method. Furthermore, if the user is excited, the data analysis unit can provide visually appealing analysis results. This allows for improved learning effectiveness by adjusting the analysis method of the training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using AI, for example, or not using AI. For example, the data analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0076] The data analysis unit can provide an optimal learning plan by referring to past learning data during data analysis. For example, the data analysis unit can suggest what the user should learn next based on their past learning data. The data analysis unit can also refer to the user's past learning data to identify and suggest effective learning methods. Furthermore, the data analysis unit can analyze the user's past learning data and adjust the learning pace. This enhances the effectiveness of learning by providing an optimal learning plan based on past learning data. Some or all of the above processes in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input the user's past learning data into a generating AI and have the generating AI provide an optimal learning plan.

[0077] The data analysis unit can customize learning plans based on the user's current lifestyle during data analysis. For example, the data analysis unit can suggest relevant learning content based on the user's current lifestyle. It can also adjust the timing of learning to match the user's daily rhythm. Furthermore, the data analysis unit can adjust the pace of learning, taking into account the user's current lifestyle. This allows for enhanced learning effectiveness by customizing learning plans based on the user's current lifestyle. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For instance, the data analysis unit can input user lifestyle data into a generating AI and have the generating AI provide a customized learning plan.

[0078] The data analysis unit can estimate the user's emotions and prioritize learning plans based on those estimated emotions. For example, if the user is stressed, the data analysis unit can prioritize providing relaxing learning content. If the user is relaxed, the data analysis unit can also prioritize providing challenging learning content. Furthermore, if the user is excited, the data analysis unit can prioritize providing learning content that helps maintain concentration. This allows for improved learning effectiveness by prioritizing learning plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 analysis unit may be performed using AI, or not. For example, the data analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The data analysis department can provide an optimal learning plan by considering the user's geographical location during data analysis. For example, the data analysis department can provide a region-specific learning plan based on the user's geographical location. It can also propose learning plans related to local events and services, taking the user's geographical location into consideration. Furthermore, the data analysis department can provide learning plans related to local culture and history based on the user's geographical location. This enhances the effectiveness of learning by providing an optimal learning plan that considers the user's geographical location. Some or all of the above processing in the data analysis department may be performed using AI, for example, or without AI. For example, the data analysis department can input the user's geographical location into a generating AI and have the generating AI provide an optimal learning plan.

[0080] The data analysis department can analyze users' social media activity during data analysis to optimize learning plans. For example, the data analysis department can identify topics of interest from users' social media activity and provide relevant learning plans. The data analysis department can also analyze users' social media activity and suggest content that will increase learning motivation. Furthermore, the data analysis department can share learning progress and provide feedback based on users' social media activity. In this way, the effectiveness of learning can be improved by analyzing users' social media activity and optimizing learning plans. Some or all of the above processes in the data analysis department may be performed using AI, for example, or not. For example, the data analysis department can input user social media data into a generating AI and have the generating AI perform the optimization of learning plans.

[0081] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0082] The learning support unit can monitor the user's health status and adjust learning content based on that status. For example, if the user is tired, it can provide simple learning content that can be completed in a short time. Conversely, if the user is energetic, it can provide learning content that requires concentration. Furthermore, if the user is feeling unwell, it can temporarily suspend learning and send a notification encouraging rest. In this way, the learning effect can be enhanced by adjusting learning content according to the user's health status.

[0083] The learning support system can customize learning content based on the user's hobbies and interests. For example, if a user is interested in music, it can provide music-related learning content. Similarly, if a user is interested in sports, it can provide sports-related content. Furthermore, if a user is interested in cooking, it can provide cooking-related content. By customizing learning content based on the user's hobbies and interests, the effectiveness of learning can be enhanced.

[0084] The learning support unit can estimate the user's emotions and adjust learning feedback based on those emotions. For example, if the user is stressed, it can provide positive feedback to increase their motivation to learn. If the user is relaxed, it can provide detailed feedback to deepen their understanding of the learning material. Furthermore, if the user is excited, it can provide visually engaging feedback. By adjusting learning feedback according to the user's emotions, the effectiveness of learning can be enhanced.

[0085] The learning support unit can monitor the user's learning environment and provide an optimal learning environment. For example, if the user is in a noisy environment, it can provide noise cancellation to create a focused environment. If the user is in a dark environment, it can adjust the screen brightness to provide an eye-friendly environment. Furthermore, if the user is in a cold environment, it can send a notification recommending a warm drink. By optimizing the user's learning environment in this way, the effectiveness of learning can be enhanced.

[0086] The learning support unit can estimate the user's emotions and adjust the timing of learning breaks based on those estimates. For example, if the user is feeling stressed, it can encourage them to take a break earlier. Conversely, if the user is relaxed, it can encourage them to continue learning. Furthermore, if the user is excited, it can suggest a short break to maintain concentration. By adjusting the timing of learning breaks according to the user's emotions, the effectiveness of learning can be enhanced.

[0087] The Learning Support Department can provide a dashboard that visualizes learning progress based on the user's learning history. For example, it can display what the user has learned in the past using graphs and charts, allowing users to see their learning progress at a glance. It can also display a list of goals the user has achieved and skills they have acquired. Furthermore, it can suggest what the user should learn next and support them in planning their studies. By visualizing the user's learning history in this way, the effectiveness of learning can be enhanced.

[0088] The learning support unit can estimate the user's emotions and provide content that enhances learning motivation based on those emotions. For example, if the user is feeling stressed, it can provide relaxing music or videos. If the user is relaxed, it can provide challenging tasks. Furthermore, if the user is excited, it can provide visually stimulating content. By providing content that increases learning motivation according to the user's emotions, the effectiveness of learning can be enhanced.

[0089] The learning support unit can customize learning content according to the user's learning style. For example, users with a visual learning style can be provided with learning content that makes extensive use of diagrams and illustrations. Users with an auditory learning style can be provided with learning content in the form of audio guides or podcasts. Furthermore, users with an experiential learning style can be provided with practical exercises and simulations. In this way, the effectiveness of learning can be enhanced by customizing learning content according to the user's learning style.

[0090] The learning support unit can estimate the user's emotions and adjust learning feedback based on those emotions. For example, if the user is stressed, it can provide positive feedback to increase their motivation to learn. If the user is relaxed, it can provide detailed feedback to deepen their understanding of the learning material. Furthermore, if the user is excited, it can provide visually engaging feedback. By adjusting learning feedback according to the user's emotions, the effectiveness of learning can be enhanced.

[0091] The Learning Support Department can provide a dashboard that visualizes learning progress based on the user's learning history. For example, it can display what the user has learned in the past using graphs and charts, allowing users to see their learning progress at a glance. It can also display a list of goals the user has achieved and skills they have acquired. Furthermore, it can suggest what the user should learn next and support them in planning their studies. By visualizing the user's learning history in this way, the effectiveness of learning can be enhanced.

[0092] The following briefly describes the processing flow for example form 2.

[0093] Step 1: The learning support unit provides customized guides and tutorials based on the user's learning pace and understanding. For example, it measures the user's learning pace and adjusts the content of guides and tutorials according to their progress. It can also evaluate the user's understanding and customize learning content based on test results and quiz accuracy. Step 2: The family collaboration section provides functions that allow family members to monitor and support the user's learning progress. For example, it can notify family members of the user's learning progress and send alerts when support is needed. It can also provide a dashboard that allows family members to check the user's learning progress in real time. Step 3: The gamification team provides elements to make learning fun and sustainable. For example, they incorporate elements such as point systems, badges, and level-ups to increase user motivation. They also provide learning content that incorporates game elements so that users can enjoy learning as they progress. Step 4: The data analysis department analyzes the user's learning data and provides an individually optimized learning plan. For example, it collects and analyzes data such as the user's learning history, test results, and study time. Based on this, it provides an optimal learning plan that matches the individual's learning goals and learning style.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] Each of the multiple elements described above, including the learning support unit, family collaboration unit, gamification unit, and data analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning support unit is implemented by the control unit 46A of the smart device 14 and provides customized guides and tutorials based on the user's learning pace and level of understanding. The family collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a function for family members to check and support the user's learning progress. The gamification unit is implemented by the control unit 46A of the smart device 14 and provides elements to make learning fun and sustainable. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's learning data and provides an individually optimized learning plan. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0098] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the learning support unit, family collaboration unit, gamification unit, and data analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning support unit is implemented by the control unit 46A of the smart glasses 214 and provides customized guides and tutorials based on the user's learning pace and level of understanding. The family collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a function for family members to check and support the user's learning progress. The gamification unit is implemented by the control unit 46A of the smart glasses 214 and provides elements to make learning fun and sustainable. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's learning data and provides an individually optimized learning plan. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0114] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the learning support unit, family collaboration unit, gamification unit, and data analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the learning support unit is implemented by the control unit 46A of the headset terminal 314 and provides customized guides and tutorials based on the user's learning pace and level of understanding. The family collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a function for family members to check and support the user's learning progress. The gamification unit is implemented by the control unit 46A of the headset terminal 314 and provides elements to make learning fun and sustainable. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's learning data and provides an individually optimized learning plan. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0130] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the learning support unit, family collaboration unit, gamification unit, and data analysis unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the learning support unit is implemented by the control unit 46A of the robot 414 and provides customized guides and tutorials based on the user's learning pace and level of understanding. The family collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a function for family members to check and support the user's learning progress. The gamification unit is implemented by the control unit 46A of the robot 414 and provides elements to make learning fun and sustainable. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's learning data and provides an individually optimized learning plan. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] (Note 1) The learning support department provides customized guides and tutorials based on the user's learning pace and level of understanding, The Family Collaboration Department allows family members to monitor and support the user's learning progress. The gamification department aims to make learning fun and sustainable, It includes a data analysis unit that analyzes the user's learning data and provides an individually optimized learning plan. A system characterized by the following features. (Note 2) The aforementioned Learning Support Department, It estimates the user's emotions and adjusts the content of guides and tutorials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned Learning Support Department, Analyze the user's past learning history and select the optimal learning method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned Learning Support Department, When providing learning support, we offer customized guides based on the user's current life situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned Learning Support Department, It estimates the user's emotions and adjusts the learning speed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned Learning Support Department, When providing learning support, the system prioritizes providing highly relevant learning content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned Learning Support Department, When providing learning support, the system analyzes the user's social media activity and provides relevant learning content. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned Family Coordination Department It estimates the user's emotions and adjusts the content of notifications sent to family members based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned Family Coordination Department When family members are involved, the system provides optimal support by referencing the user's past learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned Family Coordination Department The system estimates the user's emotions and adjusts the timing of support requests to family members based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned Family Coordination Department When linking with family members, the system provides the optimal support method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The gamification unit is, The system estimates the user's emotions and adjusts the content of gamification elements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The gamification unit is, During gamification, the system provides optimal game elements by referencing the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The gamification unit is, The system estimates the user's emotions and adjusts the game's difficulty based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The gamification unit is, When gamifying, consider the user's geographical location to provide highly relevant game elements. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned data analysis unit, It estimates the user's emotions and adjusts the analysis method of the training data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned data analysis unit, During data analysis, we provide the optimal learning plan by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned data analysis unit, During data analysis, the learning plan is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned data analysis unit, It estimates the user's emotions and prioritizes the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned data analysis unit, When analyzing data, we provide an optimal learning plan that takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned data analysis unit, During data analysis, we analyze users' social media activity to optimize learning plans. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0166] 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 learning support department provides customized guides and tutorials based on the user's learning pace and level of understanding, The Family Collaboration Department allows family members to monitor and support the user's learning progress. The gamification department aims to make learning fun and sustainable, It includes a data analysis unit that analyzes the user's learning data and provides an individually optimized learning plan. A system characterized by the following features.

2. The aforementioned Learning Support Department, It estimates the user's emotions and adjusts the content of guides and tutorials based on those estimated emotions. The system according to feature 1.

3. The aforementioned Learning Support Department, Analyze the user's past learning history and select the optimal learning method. The system according to feature 1.

4. The aforementioned Learning Support Department, When providing learning support, we offer customized guides based on the user's current life situation and areas of interest. The system according to feature 1.

5. The aforementioned Learning Support Department, It estimates the user's emotions and adjusts the learning speed based on the estimated emotions. The system according to feature 1.

6. The aforementioned Learning Support Department, When providing learning support, the system prioritizes providing highly relevant learning content by considering the user's geographical location. The system according to feature 1.

7. The aforementioned Learning Support Department, When providing learning support, the system analyzes the user's social media activity and provides relevant learning content. The system according to feature 1.

8. The aforementioned Family Coordination Department It estimates the user's emotions and adjusts the content of notifications sent to family members based on the estimated emotions. The system according to feature 1.

9. The aforementioned Family Coordination Department When family members are involved, the system provides optimal support by referencing the user's past learning progress. The system according to feature 1.

10. The aforementioned Family Coordination Department The system estimates the user's emotions and adjusts the timing of support requests to family members based on those estimated emotions. The system according to feature 1.

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