system

Abstract

The system according to this embodiment aims to provide customized learning content tailored to the individual learning needs of children. [Solution] The system according to the embodiment comprises a determination unit, a customization unit, a provision unit, and a reporting unit. The determination unit determines the child's interests and abilities. The customization unit customizes the learning content according to the child's interests and abilities determined by the determination unit. The provision unit provides the child with the learning content customized by the customization unit. The reporting unit reports the child's learning progress and results in the learning content to the child's parents.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, customized learning content tailored to the individual learning needs of children has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide customized learning content tailored to the individual learning needs of children.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a determination unit, a customization unit, a provision unit, and a reporting unit. The determination unit determines the child's interests and abilities. The customization unit customizes the learning content according to the child's interests and abilities determined by the determination unit. The provision unit provides the child with the learning content customized by the customization unit. The reporting unit reports the child's learning progress and results in the learning content to the child's parents. [Effects of the Invention]

[0007] The system according to this embodiment can provide customized learning content tailored to the individual learning needs of children. [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 manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Kids Pod System according to an embodiment of the present invention is a system that provides customized learning content and instruction tailored to the learning needs of children, thereby promoting their autonomous learning. This Kids Pod System includes a determination unit that determines a child's interests and abilities. This determination unit analyzes the child's learning history and behavioral data to identify each child's interests and abilities. For example, if a child shows a high level of interest in a particular topic, the determination unit determines the child's interests based on that information. Next, it includes a customization unit that customizes the learning content according to the child's interests and abilities determined by the determination unit. This customization unit generates optimal learning content for the child based on the information obtained from the determination unit. For example, if a child is interested in mathematics, the customization unit provides mathematics-related content. Furthermore, it includes a provision unit that provides the child with the learning content customized by the customization unit. This provision unit provides the learning content through interaction with a character. For example, the child's motivation to learn is maintained by progressing through the learning while interacting with the character. Finally, it includes a reporting unit that reports the child's learning progress and results in the learning content to the child's parents. This reporting unit periodically reports the child's learning progress and results to the parents, allowing the parents to understand the child's learning situation. For example, parents can check the child's learning progress and provide necessary support. This system caters to the individual learning needs of each child, providing a safe and privacy-focused learning environment. It also allows parents to monitor their child's learning progress and provide appropriate support, thus maintaining their motivation and promoting their growth. In this way, the KidsPod system can provide customized learning content and instruction tailored to each child's learning needs, fostering their autonomous learning.

[0029] The Kids Pod system according to this embodiment comprises a determination unit, a customization unit, a provision unit, and a reporting unit. The determination unit determines the child's interests and abilities. For example, the determination unit analyzes the child's learning history and behavioral data to identify the individual child's interests and abilities. For example, if the determination unit finds that the child has shown a high level of interest in a particular topic, it will use that information to determine the child's interests. The determination unit can also determine the child's abilities based on the child's learning history. For example, the determination unit analyzes past test results and study time to evaluate the child's academic ability and skills. Furthermore, the determination unit can also identify the child's learning style based on the child's behavioral data. For example, the determination unit analyzes what learning methods are most effective for the child and proposes the optimal learning method. The customization unit customizes the learning content according to the child's interests and abilities determined by the determination unit. For example, the customization unit generates optimal learning content for the child based on the information obtained from the determination unit. For example, if the child is interested in mathematics, the customization unit provides mathematics-related content. The customization unit can also adjust the difficulty level of the learning content according to the child's abilities. For example, the customization unit provides learning materials in stages, from easy to difficult, according to the child's academic ability. Furthermore, the customization unit can also change the format of the learning content according to the child's learning style. For example, the customization unit provides visual content to visually-oriented children and audio content to auditory-oriented children. The delivery unit provides the child with the learning content customized by the customization unit. The delivery unit provides learning content, for example, through dialogue with a character. For example, the delivery unit maintains the child's motivation to learn by having them interact with a character as they progress through their learning. The delivery unit can also provide learning content in an interactive format. For example, the delivery unit allows children to learn through quizzes and games. Furthermore, the delivery unit can provide learning content in real time. For example, the delivery unit can immediately answer any questions or doubts the child has while learning. The reporting unit reports the child's learning progress and results in the learning content to the child's parents.The reporting unit, for example, regularly reports to parents on the child's learning progress and achievements. For example, the reporting unit provides weekly or monthly reports on the child's learning status. The reporting unit can also enable parents to check their child's learning progress in real time. For example, the reporting unit can allow parents to check their child's learning status at any time through a web application or mobile application. Furthermore, the reporting unit enables parents to provide appropriate support according to their child's learning status. For example, the reporting unit can suggest advice and support methods to parents based on their child's learning progress. In this way, the KidsPod system according to the embodiment can promote children's autonomous learning by providing customized learning content tailored to the child's interests and abilities and reporting learning progress to parents.

[0030] The assessment unit determines a child's interests and abilities. For example, it analyzes a child's learning history and behavioral data to identify each child's interests and abilities. Specifically, the assessment unit extracts from the learning history and behavioral data which topics a child shows a high level of interest in. For example, if a child frequently accesses a particular subject or theme, it uses that information to determine the child's interests. The assessment unit can also determine a child's abilities based on their learning history. For example, it analyzes past test results and study time to evaluate a child's academic ability and skills. Furthermore, the assessment unit can identify a child's learning style based on their behavioral data. For example, it analyzes what learning methods are most effective for a child and suggests the optimal learning method. This includes identifying learning styles such as whether a child prefers visual information, auditory information, or hands-on learning. The assessment unit comprehensively analyzes this data to create an optimal learning plan based on the child's interests, abilities, and learning style. In addition, the assessment unit can use AI to analyze data in real time and respond quickly to changes in a child's interests and abilities. For example, if a child starts to show interest in a new topic, the assessment unit immediately reflects that information and updates the learning plan. This allows the assessment unit to flexibly respond to the child's learning needs and provide the optimal learning environment.

[0031] The customization unit customizes learning content according to the child's interests and abilities as determined by the assessment unit. For example, the customization unit generates optimal learning content for the child based on information obtained from the assessment unit. Specifically, if the child is interested in mathematics, the customization unit provides mathematics-related content. The customization unit can also adjust the difficulty level of the learning content according to the child's abilities. For example, the customization unit provides problems in stages, from easy to difficult, according to the child's academic level. Furthermore, the customization unit can change the format of the learning content according to the child's learning style. For example, the customization unit provides visual content for visually-oriented children and audio content for auditory-oriented children. Based on this information, the customization unit generates optimal learning content for the child and sends it to the delivery unit. Furthermore, the customization unit can automatically generate learning content using AI. For example, the AI ​​generates optimal learning content based on the child's interests and abilities and sends it to the delivery unit. This allows the customization unit to flexibly respond to the child's learning needs and provide an optimal learning environment. In addition, the customization unit can continuously update the learning content according to the child's learning progress. For example, once a child masters a particular topic, the customization unit will provide new topics to continuously support the child's learning. This allows the customization unit to flexibly respond to the child's learning needs and provide an optimal learning environment.

[0032] The delivery unit provides children with learning content customized by the customization unit. The delivery unit provides learning content, for example, through dialogue with characters. Specifically, the delivery unit maintains children's motivation to learn by allowing them to progress through learning while interacting with characters. The delivery unit can also provide learning content in an interactive format. For example, it can enable children to learn through quizzes and games. Furthermore, the delivery unit can provide learning content in real time. For example, it can instantly answer any questions or doubts children have while learning. Through these functions, the delivery unit supports children's learning and maximizes the effectiveness of their learning. In addition, the delivery unit can monitor children's learning progress in real time and adjust learning content as needed. For example, if a child is struggling with a particular topic, the delivery unit sends that information to the customization unit to provide appropriate support. This allows the delivery unit to flexibly respond to children's learning needs and provide an optimal learning environment. Furthermore, the delivery unit can collect children's learning data and collaborate with the evaluation and customization units to continuously improve the quality of learning content. This allows the service provider to effectively support children's learning and maximize their learning outcomes.

[0033] The reporting department reports to parents on their child's learning progress and achievements in learning content. For example, the reporting department provides regular reports on the child's learning progress and achievements. Specifically, it provides weekly or monthly reports on the child's learning status. The reporting department can also enable parents to monitor their child's learning progress in real time. For example, it can provide a web or mobile application that allows parents to check their child's learning status at any time. Furthermore, the reporting department enables parents to provide appropriate support based on their child's learning situation. For example, it can suggest advice and support methods to parents based on their child's learning progress. This allows the reporting department to effectively support their child's learning and maximize their learning outcomes. Additionally, the reporting department can analyze the child's learning data and suggest specific areas for improvement and strengthening to parents. For example, if a child is struggling with a particular topic, the reporting department can analyze the cause and suggest specific countermeasures to parents. This allows the reporting department to effectively support their child's learning and maximize their learning outcomes. Furthermore, the reporting department can support collaboration between parents and teachers and optimize the child's learning environment. For example, the reporting department can provide a platform for parents and teachers to share the child's learning progress and collaborate to support the child's learning. This allows the reporting department to effectively support the child's learning and maximize learning outcomes.

[0034] The service provider can provide learning content through dialogue with a character. For example, the service provider can provide learning content through voice dialogue with a character. For example, the service provider can maintain the child's motivation to learn by having them converse with a character as they progress through their learning. The service provider can also provide learning content through text dialogue with a character. For example, the service provider can allow the child to learn by chatting with a character. Furthermore, the service provider can provide learning content through interactive dialogue with a character. For example, the service provider can allow the child to learn through games and quizzes with a character. This helps maintain the child's motivation to learn by providing learning content through dialogue with a character. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the content of the dialogue with the character into a generating AI and have the generating AI perform the dialogue generation.

[0035] The assessment unit can analyze a child's learning history and behavioral data to identify each child's interests and abilities. For example, the assessment unit can analyze a child's learning history to identify each child's interests and abilities. For example, the assessment unit can evaluate a child's academic ability and skills based on past test results and study time. The assessment unit can also analyze a child's behavioral data to identify each child's interests and abilities. For example, the assessment unit can analyze what learning methods are most effective for a child and propose the optimal learning method. Furthermore, the assessment unit can comprehensively analyze a child's learning history and behavioral data to identify each child's interests and abilities. For example, if a child shows a high level of interest in a particular topic, the assessment unit can use that information to determine the child's interests. In this way, by analyzing a child's learning history and behavioral data, the interests and abilities of each child can be accurately identified. Some or all of the above processing in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit can input a child's learning history and behavioral data into a generating AI and have the generating AI perform the identification of interests and abilities.

[0036] The customization unit can generate learning content suitable for children based on information obtained from the assessment unit. For example, the customization unit generates optimal learning content for children based on information obtained from the assessment unit. For example, if a child is interested in mathematics, the customization unit will provide mathematics-related content. The customization unit can also adjust the difficulty level of the learning content according to the child's abilities. For example, the customization unit will provide problems in stages, from easy to difficult, according to the child's academic level. Furthermore, the customization unit can change the format of the learning content according to the child's learning style. For example, the customization unit will provide visual content for visually-oriented children and audio content for auditory-oriented children. In this way, by generating learning content based on information obtained from the assessment unit, the customization unit can provide children with an optimal learning experience. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input information obtained from the assessment unit into a generation AI and have the generation AI execute the generation of learning content.

[0037] The reporting unit can periodically report the child's learning progress and achievements to parents. For example, the reporting unit can periodically report the child's learning progress and achievements to parents. For example, the reporting unit can provide weekly or monthly reports on the child's learning status. The reporting unit can also enable parents to check their child's learning progress in real time. For example, the reporting unit can allow parents to check their child's learning status at any time through a web application or mobile application. Furthermore, the reporting unit can enable parents to provide appropriate support according to their child's learning status. For example, the reporting unit can suggest advice and support methods to parents based on their child's learning progress. This makes it easier for parents to understand their child's learning status by regularly reporting on learning progress and achievements. Some or all of the above processes in the reporting unit may be performed using AI, or not. For example, the reporting unit can input the child's learning progress and achievements into a generating AI and have the generating AI generate the report content.

[0038] The assessment unit can identify a child's interests and abilities by analyzing not only their learning history but also their daily behavioral data and hobbies and preferences. For example, the assessment unit can analyze a child's daily behavioral data to identify topics of interest at specific times of the day. For instance, if a child shows high interest in a particular activity after school, the assessment unit can use that information to determine the child's interests. The assessment unit can also analyze a child's hobbies and preferences and reflect them in the learning content. For example, if a child is interested in a particular sport or art, the assessment unit can customize the learning content based on that information. Furthermore, the assessment unit can identify the optimal learning time to maximize learning effectiveness based on the child's behavioral data. For example, the assessment unit can identify the time of day when a child can concentrate best and provide learning content during that time. This allows for a more comprehensive identification of interests and abilities by analyzing daily behavioral data and hobbies and preferences. Some or all of the above-described processes in the assessment unit may be performed using AI, or not. For example, the assessment unit can input a child's daily behavioral data and hobbies and preferences into a generating AI and have the generating AI perform the identification of interests and abilities.

[0039] The assessment unit can determine a child's interests and abilities based on their learning environment. For example, the assessment unit can analyze a child's home environment and identify the optimal learning environment. For instance, it can analyze environmental factors (e.g., a quiet place, appropriate lighting, etc.) when a child studies at home and propose the optimal learning environment. The assessment unit can also analyze a child's school environment and identify the optimal learning method to maximize learning effectiveness. For example, it can analyze environmental factors (e.g., the atmosphere of the class, the teacher's teaching methods, etc.) when a child studies at school and propose the optimal learning method. Furthermore, the assessment unit can comprehensively analyze a child's learning environment and optimally determine their interests and abilities. For example, it can consider both the home and school environments and propose the optimal learning environment for the child. This allows for a more appropriate determination of interests and abilities by considering the learning environment. Some or all of the above-described processes in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit can input the child's learning environment data into a generating AI and have the generating AI perform the determination of interests and abilities.

[0040] The assessment unit can determine a child's interests and abilities by incorporating feedback from parents and teachers, in addition to the child's learning history and behavioral data. For example, the assessment unit can determine a child's interests and abilities based on feedback from parents. For example, the assessment unit analyzes information about the child's interests and abilities provided by parents to identify the child's interests and abilities. The assessment unit can also evaluate a child's learning progress and determine their interests and abilities based on feedback from teachers. For example, the assessment unit analyzes information about the child's learning situation provided by teachers to evaluate the child's academic ability and skills. Furthermore, the assessment unit can comprehensively analyze feedback from parents and teachers to optimally determine a child's interests and abilities. For example, the assessment unit comprehensively evaluates a child's interests and abilities based on information provided by both parents and teachers. This allows for a more accurate determination of interests and abilities by incorporating feedback from parents and teachers. Some or all of the above processes in the assessment unit may be performed using AI, or not. For example, the assessment unit can input feedback data from parents and teachers into a generating AI and have the generating AI perform the determination of interests and abilities.

[0041] The assessment unit can analyze a child's learning history and behavioral data in real time and immediately reflect changes in their interests and abilities. For example, the assessment unit can analyze a child's learning history in real time and immediately reflect changes in their interests and abilities. For example, if a child shows a high level of interest in a particular topic, the assessment unit can immediately determine the child's interests based on that information. The assessment unit can also analyze a child's behavioral data in real time and immediately reflect changes in their interests and abilities. For example, if a child shows a high level of concentration on a particular learning method, the assessment unit can immediately suggest the most suitable learning method based on that information. Furthermore, the assessment unit can comprehensively analyze a child's learning history and behavioral data in real time and immediately reflect changes in their interests and abilities. For example, if a child shows a high level of interest in a particular topic, the assessment unit can immediately determine the child's interests based on that information. This allows for immediate reflection of changes in interests and abilities through real-time analysis. Some or all of the above-described processes in the assessment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the child's learning history and behavioral data into the generating AI, and have the generating AI reflect changes in their interests and abilities.

[0042] The customization unit can customize learning content according to a child's learning style. For example, the customization unit can customize learning content according to a child's learning style. For example, the customization unit can provide visual content to visually-oriented children. It can also provide audio content to auditory-oriented children. For example, if a child prefers learning through audio, the customization unit can provide audio content based on that information. Furthermore, the customization unit can provide interactive content to experiential-oriented children. For example, if a child prefers to learn by actually doing things, the customization unit can provide interactive content based on that information. In this way, by customizing learning content according to learning style, it is possible to provide children with the optimal learning experience. Some or all of the above processing in the customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input the child's learning style data into a generating AI and have the generating AI perform the customization of the learning content.

[0043] The customization unit can dynamically generate the next content a child should learn, based on their learning progress. For example, the customization unit can analyze a child's learning progress and automatically generate the next content. For instance, if a child is learning a specific topic, the customization unit can suggest the next content based on their progress. The customization unit can also suggest the optimal learning order based on the child's learning progress. For example, when a child is learning a specific topic, the customization unit can suggest the most effective order to learn the material. Furthermore, the customization unit can analyze a child's learning progress in real time and dynamically generate the next content. For example, while a child is learning a specific topic, the customization unit can instantly suggest the next content based on their progress. This enables effective learning by dynamically generating the next content according to learning progress. Some or all of the above processes in the customization unit may be performed using AI, or not. For example, the customization unit can input the child's learning progress data into a generating AI and have the generating AI generate the next content.

[0044] The customization section can attract children's interest by incorporating game elements into learning content. For example, the customization section can introduce a point system into learning content to attract children's interest. For example, the customization section can allow children to earn points as they progress through their studies. The customization section can also attract children's interest by introducing a level-up system into learning content. For example, the customization section can allow children to level up as they progress through their studies. Furthermore, the customization section can attract children's interest by introducing competitive elements into learning content. For example, the customization section can allow children to learn while competing with other children. In this way, by incorporating game elements, children's interest can be attracted and their motivation to learn can be increased. Some or all of the above processes in the customization section may be performed using AI, for example, or without AI. For example, the customization section can input data for incorporating game elements into learning content into a generating AI and have the generating AI execute the introduction of game elements.

[0045] The customization section can add options that parents and teachers can customize to the learning content. For example, the customization section can provide options that allow parents to customize the learning content. For example, the customization section can allow parents to adjust the difficulty level and format of the content according to their child's learning needs. The customization section can also provide options that allow teachers to customize the learning content. For example, the customization section can allow teachers to change the content according to their child's learning progress. Furthermore, the customization section can provide options that allow parents and teachers to customize the learning content together. For example, the customization section can allow parents and teachers to work together to provide content that meets their child's learning needs. This allows parents and teachers to customize the learning content, thereby providing children with the best possible learning experience. Some or all of the above processes in the customization section may be performed using AI, for example, or not using AI. For example, the customization section can input parent and teacher customization data into a generating AI and have the generating AI perform the customization of the learning content.

[0046] The service provider can monitor a child's concentration and fatigue level when providing learning content and suggest appropriate breaks. For example, if a child's concentration level decreases, the service provider can suggest a break. For example, if a child loses concentration on a particular learning activity, the service provider can suggest a break based on that information. The service provider can also suggest a break if a child's fatigue level increases. For example, if a child feels fatigued during a particular learning activity, the service provider can suggest a break based on that information. Furthermore, the service provider can comprehensively monitor a child's concentration and fatigue level and suggest appropriate breaks. For example, if a child loses concentration on a particular learning activity and feels fatigued, the service provider can suggest a break based on that information. In this way, by monitoring a child's concentration and fatigue level, appropriate breaks can be suggested, improving learning efficiency. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the child's concentration and fatigue level into a generating AI and have the generating AI suggest breaks.

[0047] The delivery unit can adjust the pace of learning content delivery to match the child's learning pace. For example, if the child is learning quickly, the delivery unit can increase the pace. For example, if the child is quickly understanding a particular topic, the delivery unit can adjust the pace based on that information. The delivery unit can also decrease the pace if the child is learning slowly. For example, if the child is taking a long time to understand a particular topic, the delivery unit can adjust the pace based on that information. Furthermore, the delivery unit can monitor the child's learning pace in real time and adjust the pace accordingly. For example, while the child is learning a particular topic, the delivery unit can instantly adjust the pace based on their progress. By adjusting the pace to match the child's learning pace, more effective learning becomes possible. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the child's learning pace data into a generating AI and have the generating AI perform the adjustment of the pace.

[0048] The service provider can add a feature that allows parents and teachers to provide real-time feedback when providing learning content. For example, the service provider can add a feature that allows parents to provide real-time feedback. For example, the service provider can enable parents to check their child's learning progress in real time and provide feedback. The service provider can also add a feature that allows teachers to provide real-time feedback. For example, the service provider can enable teachers to check their child's learning progress in real time and provide feedback. Furthermore, the service provider can add a feature that allows parents and teachers to jointly provide real-time feedback. For example, the service provider can enable parents and teachers to collaborate to check their child's learning progress in real time and provide feedback. This allows parents and teachers to provide real-time feedback, thereby improving the quality of learning. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input feedback data from parents and teachers into a generating AI and have the generating AI perform real-time feedback provision.

[0049] The service provider can optimize the child's learning environment when providing learning content. For example, the service provider can optimize the volume when providing learning content. For example, the service provider can adjust the volume when the child is learning to provide an optimal learning environment. The service provider can also optimize the lighting when providing learning content. For example, the service provider can adjust the lighting when the child is learning to provide an optimal learning environment. Furthermore, the service provider can comprehensively optimize both volume and lighting when providing learning content. For example, the service provider can adjust both the volume and lighting when the child is learning to provide an optimal learning environment. By optimizing the learning environment in this way, the child's learning efficiency can be improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the child's learning environment data into a generating AI and have the generating AI perform the optimization of the learning environment.

[0050] The reporting unit can report learning progress and outcomes in a format that is easy for parents and teachers to understand. For example, the reporting unit can report learning progress in a format that is easy for parents to understand. For example, the reporting unit can visually display the child's learning status using graphs and charts. The reporting unit can also report learning outcomes in a format that is easy for teachers to understand. For example, the reporting unit can provide teachers with a text report on the child's learning outcomes. Furthermore, the reporting unit can report learning progress and outcomes in a format that is easy for parents and teachers to understand jointly. For example, the reporting unit can enable parents and teachers to collaboratively view the child's learning status on an interactive dashboard. This makes it easier for parents and teachers to understand the child's learning status by reporting in a format that is easy for them to understand. Some or all of the above processes in the reporting unit may be performed using AI, or not. For example, the reporting unit can input learning progress and outcome data into a generating AI and have the generating AI generate a report in an easy-to-understand format.

[0051] The reporting unit can visually display a child's learning history and behavioral data when reporting learning progress and results. For example, the reporting unit can visually display learning progress using graphs and charts. For example, the reporting unit can display a child's learning status using line graphs and bar graphs. The reporting unit can also visually display learning outcomes using visual reports. For example, the reporting unit can display a child's learning outcomes using infographics. Furthermore, the reporting unit can visually display learning history and behavioral data using interactive dashboards. For example, the reporting unit can allow parents and teachers to check a child's learning history and behavioral data in real time. This makes it easier for parents and teachers to understand a child's learning situation by visually displaying learning history and behavioral data. Some or all of the above processing in the reporting unit may be performed using AI, or not. For example, the reporting unit can input learning history and behavioral data into a generating AI and have the generating AI perform the visual display.

[0052] The reporting unit can add reporting options that parents and teachers can customize. For example, the reporting unit can provide options that allow parents to customize the content of their reports. For instance, it can allow parents to select reporting items and formats according to their child's learning progress. The reporting unit can also provide options that allow teachers to customize the content of their reports. For instance, it can allow teachers to select reporting items and formats according to their child's learning progress. Furthermore, the reporting unit can provide options that allow parents and teachers to jointly customize the content of their reports. For example, it can allow parents and teachers to collaborate to provide reports that are tailored to their child's learning progress. This allows parents and teachers to customize the content of their reports, leading to a more accurate understanding of their child's learning situation. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input customized data from parents and teachers into a generating AI and have the generating AI perform the customization of the report content.

[0053] The reporting unit can add a function to compare children's learning goals and achievement levels when reporting learning progress and results. For example, the reporting unit can report learning progress in comparison to the child's learning goals. For example, the reporting unit can report the current progress against the learning goals set by the child. The reporting unit can also report learning outcomes in comparison to the child's achievement level. For example, the reporting unit can report the outcomes achieved by the child in comparison to the learning goals. Furthermore, the reporting unit can also provide a comprehensive comparison of learning goals and achievement levels. For example, the reporting unit can comprehensively evaluate the child's learning goals and achievement levels and report the results. This allows for a clearer understanding of the child's learning situation by comparing learning goals and achievement levels. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input learning goal and achievement level data into a generating AI and have the generating AI report the comparison results.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The KidsPod system may further include a visualization unit that visually displays a child's learning progress. This visualization unit can, for example, display a child's learning progress in graphs or charts. For example, it can display a child's learning status in line graphs or bar graphs. The visualization unit can also visually display learning outcomes in visual reports. For example, it can display a child's learning outcomes in infographics. Furthermore, the visualization unit can visually display learning history and behavioral data in an interactive dashboard. For example, the visualization unit can allow parents and teachers to check a child's learning history and behavioral data in real time. This makes it easier for parents and teachers to understand a child's learning situation by visually displaying learning history and behavioral data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit can input learning history and behavioral data into a generating AI and have the generating AI perform the visual display.

[0056] The KidsPod system may further include a reward unit that provides rewards based on the child's learning progress. This reward unit may, for example, provide rewards when the child achieves a specific learning goal. For example, the reward unit may provide a reward based on information that the child has finished learning a particular topic. The reward unit may also change the type of reward according to the child's learning progress. For example, the reward unit may provide a special reward based on information that the child has achieved high learning results. Furthermore, the reward unit may analyze the child's learning progress in real time and provide rewards dynamically. For example, the reward unit may immediately provide a reward based on the child's progress while the child is learning a particular topic. This can increase the child's motivation to learn by providing rewards based on learning progress. Some or all of the above processing in the reward unit may be performed using AI, for example, or not using AI. For example, the reward unit may input the child's learning progress data into a generating AI and have the generating AI perform the provision of rewards.

[0057] The KidsPod system can also include a notification unit that informs parents and teachers of the child's learning progress. This notification unit can, for example, send notifications to parents and teachers when the child achieves a specific learning goal. For example, it can send a notification when the child has finished learning a particular topic. The notification unit can also change the content of notifications according to the child's learning progress. For example, it can send a special notification when the child achieves high learning results. Furthermore, the notification unit can analyze the child's learning progress in real time and send notifications dynamically. For example, it can send an immediate notification based on the child's progress while the child is learning a particular topic. This makes it easier for parents and teachers to understand the child's learning situation by sending notifications based on learning progress. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the child's learning progress data into a generating AI and have the generating AI execute the sending of notifications.

[0058] The KidsPod system can also include a dashboard that allows parents and teachers to monitor a child's learning progress in real time. This dashboard can, for example, display a child's learning progress in graphs or charts. The dashboard can also visually display learning outcomes in visual reports. For example, it can display a child's learning outcomes in infographics. Furthermore, the dashboard can display learning history and behavioral data in an interactive format. For example, the dashboard can allow parents and teachers to view a child's learning history and behavioral data in real time. This makes it easier for parents and teachers to understand a child's learning situation by visually displaying learning history and behavioral data. Some or all of the above processing in the dashboard may be performed using AI, or not. For example, the dashboard can input learning history and behavioral data into a generating AI and have the generating AI perform the visual display.

[0059] The KidsPod system may also include a customization section that allows parents and teachers to customize a child's learning progress. This customization section may, for example, provide an option for parents to customize learning content. For instance, it may allow parents to adjust the difficulty level and format of the content according to their child's learning needs. The customization section may also provide an option for teachers to customize learning content. For instance, it may allow teachers to change the content according to the child's learning progress. Furthermore, the customization section may provide an option for parents and teachers to collaboratively customize learning content. For example, it may allow parents and teachers to work together to provide content that meets the child's learning needs. This allows parents and teachers to customize learning content, thereby providing children with the best possible learning experience. Some or all of the above-described processes in the customization section may be performed using AI, for example, or not. For example, the customization section may input parent and teacher customization data into a generating AI and have the generating AI perform the customization of the learning content.

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

[0061] Step 1: The assessment unit determines the child's interests and abilities. The assessment unit analyzes, for example, the child's learning history and behavioral data to identify the individual child's interests and abilities. For example, if the assessment unit sees a child showing a high level of interest in a particular topic, it will use that information to determine the child's interests. The assessment unit can also determine the child's abilities based on the child's learning history. For example, it analyzes past test results and study time to evaluate the child's academic ability and skills. Furthermore, the assessment unit can identify the child's learning style based on the child's behavioral data. For example, it analyzes what learning methods are most effective for the child and suggests the optimal learning method. Step 2: The customization unit customizes the learning content according to the child's interests and abilities as determined by the assessment unit. For example, the customization unit generates optimal learning content for the child based on the information obtained from the assessment unit. For example, if the child is interested in mathematics, the customization unit will provide mathematics-related content. The customization unit can also adjust the difficulty level of the learning content according to the child's abilities. For example, the customization unit will provide problems in stages, from easy to difficult, according to the child's academic level. Furthermore, the customization unit can change the format of the learning content according to the child's learning style. For example, the customization unit will provide visual content for visually-oriented children and audio content for auditory-oriented children. Step 3: The delivery unit provides children with learning content customized by the customization unit. The delivery unit provides learning content, for example, through interaction with characters. For example, the delivery unit maintains the child's motivation to learn by having them interact with characters as they progress through their learning. The delivery unit can also provide learning content in an interactive format. For example, the delivery unit allows children to learn through quizzes and games. Furthermore, the delivery unit can provide learning content in real time. For example, the delivery unit can immediately answer any questions or doubts the child has while learning. Step 4: The reporting department reports to the child's parents on the child's learning progress and achievements in the learning content. The reporting department, for example, reports to parents on the child's learning progress and achievements on a regular basis. For example, the reporting department provides weekly or monthly reports on the child's learning status. The reporting department can also enable parents to check their child's learning progress in real time. For example, the reporting department can enable parents to check their child's learning status at any time through a web application or mobile application. Furthermore, the reporting department can enable parents to provide appropriate support according to their child's learning status. For example, the reporting department can suggest advice and support methods to parents based on their child's learning progress.

[0062] (Example of form 2) The Kids Pod System according to an embodiment of the present invention is a system that provides customized learning content and instruction tailored to the learning needs of children, thereby promoting their autonomous learning. This Kids Pod System includes a determination unit that assesses a child's interests and abilities. This determination unit analyzes the child's learning history and behavioral data to identify the individual child's interests and abilities. For example, if a child shows a high level of interest in a particular topic, the determination unit assesses the child's interests based on that information. Next, it includes a customization unit that customizes the learning content according to the child's interests and abilities as determined by the determination unit. This customization unit generates optimal learning content for the child based on the information obtained from the determination unit. For example, if a child is interested in mathematics, the customization unit provides mathematics-related content. Furthermore, it includes a provision unit that provides the child with the learning content customized by the customization unit. This provision unit provides the learning content through interaction with a character. For example, the child's motivation to learn is maintained by interacting with the character as they progress through their learning. Finally, it includes a reporting unit that reports the child's learning progress and results in the learning content to the child's parents. This reporting unit periodically reports the child's learning progress and results to the parents, allowing the parents to understand the child's learning situation. For example, parents can check the child's learning progress and provide necessary support. This system caters to the individual learning needs of each child, providing a safe and privacy-focused learning environment. It also allows parents to monitor their child's learning progress and provide appropriate support, thus maintaining their motivation and promoting their growth. In this way, the KidsPod system can provide customized learning content and instruction tailored to each child's learning needs, fostering their autonomous learning.

[0063] The Kids Pod system according to this embodiment comprises a determination unit, a customization unit, a provision unit, and a reporting unit. The determination unit determines the child's interests and abilities. For example, the determination unit analyzes the child's learning history and behavioral data to identify the individual child's interests and abilities. For example, if the determination unit finds that the child has shown a high level of interest in a particular topic, it will use that information to determine the child's interests. The determination unit can also determine the child's abilities based on the child's learning history. For example, the determination unit analyzes past test results and study time to evaluate the child's academic ability and skills. Furthermore, the determination unit can also identify the child's learning style based on the child's behavioral data. For example, the determination unit analyzes what learning methods are most effective for the child and proposes the optimal learning method. The customization unit customizes the learning content according to the child's interests and abilities determined by the determination unit. For example, the customization unit generates optimal learning content for the child based on the information obtained from the determination unit. For example, if the child is interested in mathematics, the customization unit provides mathematics-related content. The customization unit can also adjust the difficulty level of the learning content according to the child's abilities. For example, the customization unit provides learning materials in stages, from easy to difficult, according to the child's academic ability. Furthermore, the customization unit can also change the format of the learning content according to the child's learning style. For example, the customization unit provides visual content to visually-oriented children and audio content to auditory-oriented children. The delivery unit provides the child with the learning content customized by the customization unit. The delivery unit provides learning content, for example, through dialogue with a character. For example, the delivery unit maintains the child's motivation to learn by having them interact with a character as they progress through their learning. The delivery unit can also provide learning content in an interactive format. For example, the delivery unit allows children to learn through quizzes and games. Furthermore, the delivery unit can provide learning content in real time. For example, the delivery unit can immediately answer any questions or doubts the child has while learning. The reporting unit reports the child's learning progress and results in the learning content to the child's parents.The reporting unit, for example, regularly reports to parents on the child's learning progress and achievements. For example, the reporting unit provides weekly or monthly reports on the child's learning status. The reporting unit can also enable parents to check their child's learning progress in real time. For example, the reporting unit can allow parents to check their child's learning status at any time through a web application or mobile application. Furthermore, the reporting unit enables parents to provide appropriate support according to their child's learning status. For example, the reporting unit can suggest advice and support methods to parents based on their child's learning progress. In this way, the KidsPod system according to the embodiment can promote children's autonomous learning by providing customized learning content tailored to the child's interests and abilities and reporting learning progress to parents.

[0064] The assessment unit determines a child's interests and abilities. For example, it analyzes a child's learning history and behavioral data to identify each child's interests and abilities. Specifically, the assessment unit extracts from the learning history and behavioral data which topics a child shows a high level of interest in. For example, if a child frequently accesses a particular subject or theme, it uses that information to determine the child's interests. The assessment unit can also determine a child's abilities based on their learning history. For example, it analyzes past test results and study time to evaluate a child's academic ability and skills. Furthermore, the assessment unit can identify a child's learning style based on their behavioral data. For example, it analyzes what learning methods are most effective for a child and suggests the optimal learning method. This includes identifying learning styles such as whether a child prefers visual information, auditory information, or hands-on learning. The assessment unit comprehensively analyzes this data to create an optimal learning plan based on the child's interests, abilities, and learning style. In addition, the assessment unit can use AI to analyze data in real time and respond quickly to changes in a child's interests and abilities. For example, if a child starts to show interest in a new topic, the assessment unit immediately reflects that information and updates the learning plan. This allows the assessment unit to flexibly respond to the child's learning needs and provide the optimal learning environment.

[0065] The customization unit customizes learning content according to the child's interests and abilities as determined by the assessment unit. For example, the customization unit generates optimal learning content for the child based on information obtained from the assessment unit. Specifically, if the child is interested in mathematics, the customization unit provides mathematics-related content. The customization unit can also adjust the difficulty level of the learning content according to the child's abilities. For example, the customization unit provides problems in stages, from easy to difficult, according to the child's academic level. Furthermore, the customization unit can change the format of the learning content according to the child's learning style. For example, the customization unit provides visual content for visually-oriented children and audio content for auditory-oriented children. Based on this information, the customization unit generates optimal learning content for the child and sends it to the delivery unit. Furthermore, the customization unit can automatically generate learning content using AI. For example, the AI ​​generates optimal learning content based on the child's interests and abilities and sends it to the delivery unit. This allows the customization unit to flexibly respond to the child's learning needs and provide an optimal learning environment. In addition, the customization unit can continuously update the learning content according to the child's learning progress. For example, once a child masters a particular topic, the customization unit will provide new topics to continuously support the child's learning. This allows the customization unit to flexibly respond to the child's learning needs and provide an optimal learning environment.

[0066] The delivery unit provides children with learning content customized by the customization unit. The delivery unit provides learning content, for example, through dialogue with characters. Specifically, the delivery unit maintains children's motivation to learn by allowing them to progress through learning while interacting with characters. The delivery unit can also provide learning content in an interactive format. For example, it can enable children to learn through quizzes and games. Furthermore, the delivery unit can provide learning content in real time. For example, it can instantly answer any questions or doubts children have while learning. Through these functions, the delivery unit supports children's learning and maximizes the effectiveness of their learning. In addition, the delivery unit can monitor children's learning progress in real time and adjust learning content as needed. For example, if a child is struggling with a particular topic, the delivery unit sends that information to the customization unit to provide appropriate support. This allows the delivery unit to flexibly respond to children's learning needs and provide an optimal learning environment. Furthermore, the delivery unit can collect children's learning data and collaborate with the evaluation and customization units to continuously improve the quality of learning content. This allows the service provider to effectively support children's learning and maximize their learning outcomes.

[0067] The reporting department reports to parents on their child's learning progress and achievements in learning content. For example, the reporting department provides regular reports on the child's learning progress and achievements. Specifically, it provides weekly or monthly reports on the child's learning status. The reporting department can also enable parents to monitor their child's learning progress in real time. For example, it can provide a web or mobile application that allows parents to check their child's learning status at any time. Furthermore, the reporting department enables parents to provide appropriate support based on their child's learning situation. For example, it can suggest advice and support methods to parents based on their child's learning progress. This allows the reporting department to effectively support their child's learning and maximize their learning outcomes. Additionally, the reporting department can analyze the child's learning data and suggest specific areas for improvement and strengthening to parents. For example, if a child is struggling with a particular topic, the reporting department can analyze the cause and suggest specific countermeasures to parents. This allows the reporting department to effectively support their child's learning and maximize their learning outcomes. Furthermore, the reporting department can support collaboration between parents and teachers and optimize the child's learning environment. For example, the reporting department can provide a platform for parents and teachers to share the child's learning progress and collaborate to support the child's learning. This allows the reporting department to effectively support the child's learning and maximize learning outcomes.

[0068] The service provider can provide learning content through dialogue with a character. For example, the service provider can provide learning content through voice dialogue with a character. For example, the service provider can maintain the child's motivation to learn by having them converse with a character as they progress through their learning. The service provider can also provide learning content through text dialogue with a character. For example, the service provider can allow the child to learn by chatting with a character. Furthermore, the service provider can provide learning content through interactive dialogue with a character. For example, the service provider can allow the child to learn through games and quizzes with a character. This helps maintain the child's motivation to learn by providing learning content through dialogue with a character. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the content of the dialogue with the character into a generating AI and have the generating AI perform the dialogue generation.

[0069] The assessment unit can analyze a child's learning history and behavioral data to identify each child's interests and abilities. For example, the assessment unit can analyze a child's learning history to identify each child's interests and abilities. For example, the assessment unit can evaluate a child's academic ability and skills based on past test results and study time. The assessment unit can also analyze a child's behavioral data to identify each child's interests and abilities. For example, the assessment unit can analyze what learning methods are most effective for a child and propose the optimal learning method. Furthermore, the assessment unit can comprehensively analyze a child's learning history and behavioral data to identify each child's interests and abilities. For example, if a child shows a high level of interest in a particular topic, the assessment unit can use that information to determine the child's interests. In this way, by analyzing a child's learning history and behavioral data, the interests and abilities of each child can be accurately identified. Some or all of the above processing in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit can input a child's learning history and behavioral data into a generating AI and have the generating AI perform the identification of interests and abilities.

[0070] The customization unit can generate learning content suitable for children based on information obtained from the assessment unit. For example, the customization unit generates optimal learning content for children based on information obtained from the assessment unit. For example, if a child is interested in mathematics, the customization unit will provide mathematics-related content. The customization unit can also adjust the difficulty level of the learning content according to the child's abilities. For example, the customization unit will provide problems in stages, from easy to difficult, according to the child's academic level. Furthermore, the customization unit can change the format of the learning content according to the child's learning style. For example, the customization unit will provide visual content for visually-oriented children and audio content for auditory-oriented children. In this way, by generating learning content based on information obtained from the assessment unit, the customization unit can provide children with an optimal learning experience. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input information obtained from the assessment unit into a generation AI and have the generation AI execute the generation of learning content.

[0071] The reporting unit can periodically report the child's learning progress and achievements to parents. For example, the reporting unit can periodically report the child's learning progress and achievements to parents. For example, the reporting unit can provide weekly or monthly reports on the child's learning status. The reporting unit can also enable parents to check their child's learning progress in real time. For example, the reporting unit can allow parents to check their child's learning status at any time through a web application or mobile application. Furthermore, the reporting unit can enable parents to provide appropriate support according to their child's learning status. For example, the reporting unit can suggest advice and support methods to parents based on their child's learning progress. This makes it easier for parents to understand their child's learning status by regularly reporting on learning progress and achievements. Some or all of the above processes in the reporting unit may be performed using AI, or not. For example, the reporting unit can input the child's learning progress and achievements into a generating AI and have the generating AI generate the report content.

[0072] The assessment unit can estimate a child's emotions and improve the accuracy of its assessment of interests and abilities based on the estimated emotions. For example, if a child is enjoying something, the assessment unit can collect data to further explore topics of interest and improve assessment accuracy. For example, if a child shows a high level of interest in a particular topic, the assessment unit can use that information to determine the child's interests. The assessment unit can also identify the cause of stress if a child is experiencing stress, collect data to eliminate it, and improve assessment accuracy. For example, if a child is experiencing stress with a particular learning activity, the assessment unit can adjust the learning content based on that information. Furthermore, if a child is concentrating, the assessment unit can collect data to maintain that concentration and improve assessment accuracy. For example, if a child shows high concentration on a particular learning method, the assessment unit can suggest the optimal learning method based on that information. By improving assessment accuracy based on a child's emotions, it becomes possible to more accurately determine interests and abilities. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, 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 judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the child's emotional data into a generating AI and cause the generating AI to improve the accuracy of its assessment of interests and abilities.

[0073] The assessment unit can identify a child's interests and abilities by analyzing not only their learning history but also their daily behavioral data and hobbies and preferences. For example, the assessment unit can analyze a child's daily behavioral data to identify topics of interest at specific times of the day. For instance, if a child shows high interest in a particular activity after school, the assessment unit can use that information to determine the child's interests. The assessment unit can also analyze a child's hobbies and preferences and reflect them in the learning content. For example, if a child is interested in a particular sport or art, the assessment unit can customize the learning content based on that information. Furthermore, the assessment unit can identify the optimal learning time to maximize learning effectiveness based on the child's behavioral data. For example, the assessment unit can identify the time of day when a child can concentrate best and provide learning content during that time. This allows for a more comprehensive identification of interests and abilities by analyzing daily behavioral data and hobbies and preferences. Some or all of the above-described processes in the assessment unit may be performed using AI, or not. For example, the assessment unit can input a child's daily behavioral data and hobbies and preferences into a generating AI and have the generating AI perform the identification of interests and abilities.

[0074] The assessment unit can determine a child's interests and abilities based on their learning environment. For example, the assessment unit can analyze a child's home environment and identify the optimal learning environment. For instance, it can analyze environmental factors (e.g., a quiet place, appropriate lighting, etc.) when a child studies at home and propose the optimal learning environment. The assessment unit can also analyze a child's school environment and identify the optimal learning method to maximize learning effectiveness. For example, it can analyze environmental factors (e.g., the atmosphere of the class, the teacher's teaching methods, etc.) when a child studies at school and propose the optimal learning method. Furthermore, the assessment unit can comprehensively analyze a child's learning environment and optimally determine their interests and abilities. For example, it can consider both the home and school environments and propose the optimal learning environment for the child. This allows for a more appropriate determination of interests and abilities by considering the learning environment. Some or all of the above-described processes in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit can input the child's learning environment data into a generating AI and have the generating AI perform the determination of interests and abilities.

[0075] The judgment unit can estimate a child's emotions and determine the priority of judgment results based on the estimated emotions. For example, if a child is enjoying something, the judgment unit prioritizes that emotion and determines topics of interest. For example, if a child shows high interest in a particular topic, the judgment unit uses that information to determine the child's interests. The judgment unit can also prioritize a child's stressed emotions and make judgments to eliminate the cause of the stress. For example, if a child is stressed by a particular learning activity, the judgment unit adjusts the learning content based on that information. Furthermore, if a child is concentrating, the judgment unit can prioritize that emotion and make judgments to maintain concentration. For example, if a child shows high concentration on a particular learning method, the judgment unit suggests the optimal learning method based on that information. In this way, by determining the priority of judgment results based on a child's emotions, it becomes possible to provide more appropriate learning content. 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-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the child's emotional data into a generating AI and have the generating AI determine the priority of the judgment results.

[0076] The assessment unit can determine a child's interests and abilities by incorporating feedback from parents and teachers, in addition to the child's learning history and behavioral data. For example, the assessment unit can determine a child's interests and abilities based on feedback from parents. For example, the assessment unit analyzes information about the child's interests and abilities provided by parents to identify the child's interests and abilities. The assessment unit can also evaluate a child's learning progress and determine their interests and abilities based on feedback from teachers. For example, the assessment unit analyzes information about the child's learning situation provided by teachers to evaluate the child's academic ability and skills. Furthermore, the assessment unit can comprehensively analyze feedback from parents and teachers to optimally determine a child's interests and abilities. For example, the assessment unit comprehensively evaluates a child's interests and abilities based on information provided by both parents and teachers. This allows for a more accurate determination of interests and abilities by incorporating feedback from parents and teachers. Some or all of the above processes in the assessment unit may be performed using AI, or not. For example, the assessment unit can input feedback data from parents and teachers into a generating AI and have the generating AI perform the determination of interests and abilities.

[0077] The assessment unit can analyze a child's learning history and behavioral data in real time and immediately reflect changes in their interests and abilities. For example, the assessment unit can analyze a child's learning history in real time and immediately reflect changes in their interests and abilities. For example, if a child shows a high level of interest in a particular topic, the assessment unit can immediately determine the child's interests based on that information. The assessment unit can also analyze a child's behavioral data in real time and immediately reflect changes in their interests and abilities. For example, if a child shows a high level of concentration on a particular learning method, the assessment unit can immediately suggest the most suitable learning method based on that information. Furthermore, the assessment unit can comprehensively analyze a child's learning history and behavioral data in real time and immediately reflect changes in their interests and abilities. For example, if a child shows a high level of interest in a particular topic, the assessment unit can immediately determine the child's interests based on that information. This allows for immediate reflection of changes in interests and abilities through real-time analysis. Some or all of the above-described processes in the assessment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the child's learning history and behavioral data into the generating AI, and have the generating AI reflect changes in their interests and abilities.

[0078] The customization unit can estimate a child's emotions and adjust the difficulty level and format of the learning content based on those emotions. For example, if a child is enjoying the content, the customization unit can increase the difficulty level and provide more challenging content. For example, if a child shows a high level of interest in a particular topic, the customization unit can adjust the difficulty level based on that information. The customization unit can also lower the difficulty level and provide more relaxing content if a child is feeling stressed. For example, if a child is feeling stressed by a particular learning activity, the customization unit can adjust the difficulty level based on that information. Furthermore, if a child is concentrating, the customization unit can change the format to provide a new way of learning. For example, if a child is showing a high level of concentration on a particular learning method, the customization unit can adjust the format based on that information. By adjusting the difficulty level and format of the learning content based on a child's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, 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 customization unit may be performed using AI, for example, or without AI. For example, the customization section can input children's emotional data into a generating AI, which can then adjust the difficulty level and format of the learning content.

[0079] The customization unit can customize learning content according to a child's learning style. For example, the customization unit can customize learning content according to a child's learning style. For example, the customization unit can provide visual content to visually-oriented children. It can also provide audio content to auditory-oriented children. For example, if a child prefers learning through audio, the customization unit can provide audio content based on that information. Furthermore, the customization unit can provide interactive content to experiential-oriented children. For example, if a child prefers to learn by actually doing things, the customization unit can provide interactive content based on that information. In this way, by customizing learning content according to learning style, it is possible to provide children with the optimal learning experience. Some or all of the above processing in the customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input the child's learning style data into a generating AI and have the generating AI perform the customization of the learning content.

[0080] The customization unit can dynamically generate the next content a child should learn, based on their learning progress. For example, the customization unit can analyze a child's learning progress and automatically generate the next content. For instance, if a child is learning a specific topic, the customization unit can suggest the next content based on their progress. The customization unit can also suggest the optimal learning order based on the child's learning progress. For example, when a child is learning a specific topic, the customization unit can suggest the most effective order to learn the material. Furthermore, the customization unit can analyze a child's learning progress in real time and dynamically generate the next content. For example, while a child is learning a specific topic, the customization unit can instantly suggest the next content based on their progress. This enables effective learning by dynamically generating the next content according to learning progress. Some or all of the above processes in the customization unit may be performed using AI, or not. For example, the customization unit can input the child's learning progress data into a generating AI and have the generating AI generate the next content.

[0081] The customization unit can estimate a child's emotions and adjust the order in which learning content is presented based on those emotions. For example, if a child is enjoying themselves, the customization unit will prioritize topics of interest. For example, if a child shows a high level of interest in a particular topic, the customization unit will adjust the order based on that information. The customization unit can also prioritize relaxing topics if a child is feeling stressed. For example, if a child is feeling stressed by a particular learning activity, the customization unit will adjust the order based on that information. Furthermore, if a child is concentrating, the customization unit can prioritize more challenging topics. For example, if a child shows a high level of concentration on a particular learning method, the customization unit will adjust the order based on that information. By adjusting the order in which learning content is presented based on a child's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, 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 customization unit may be performed using AI, for example, or without AI. For example, the customization section can input children's emotional data into a generating AI and have the AI ​​adjust the order in which learning content is provided.

[0082] The customization section can attract children's interest by incorporating game elements into learning content. For example, the customization section can introduce a point system into learning content to attract children's interest. For example, the customization section can allow children to earn points as they progress through their studies. The customization section can also attract children's interest by introducing a level-up system into learning content. For example, the customization section can allow children to level up as they progress through their studies. Furthermore, the customization section can attract children's interest by introducing competitive elements into learning content. For example, the customization section can allow children to learn while competing with other children. In this way, by incorporating game elements, children's interest can be attracted and their motivation to learn can be increased. Some or all of the above processes in the customization section may be performed using AI, for example, or without AI. For example, the customization section can input data for incorporating game elements into learning content into a generating AI and have the generating AI execute the introduction of game elements.

[0083] The customization section can add options that parents and teachers can customize to the learning content. For example, the customization section can provide options that allow parents to customize the learning content. For example, the customization section can allow parents to adjust the difficulty level and format of the content according to their child's learning needs. The customization section can also provide options that allow teachers to customize the learning content. For example, the customization section can allow teachers to change the content according to their child's learning progress. Furthermore, the customization section can provide options that allow parents and teachers to customize the learning content together. For example, the customization section can allow parents and teachers to work together to provide content that meets their child's learning needs. This allows parents and teachers to customize the learning content, thereby providing children with the best possible learning experience. Some or all of the above processes in the customization section may be performed using AI, for example, or not using AI. For example, the customization section can input parent and teacher customization data into a generating AI and have the generating AI perform the customization of the learning content.

[0084] The delivery unit can estimate a child's emotions and adjust the delivery method of learning content based on the estimated emotions. For example, if the child is enjoying the content, the delivery unit will deliver it in an interactive way. For example, if the child shows a high level of interest in a particular topic, the delivery unit will provide an interactive learning method based on that information. The delivery unit can also deliver learning content in a relaxing way if the child is feeling stressed. For example, if the child is feeling stressed by a particular learning activity, the delivery unit will provide a relaxing learning method based on that information. Furthermore, if the child is concentrating, the delivery unit can deliver learning content in a way that helps maintain their concentration. For example, if the child is showing a high level of concentration on a particular learning method, the delivery unit will provide a learning method that helps maintain their concentration based on that information. In this way, by adjusting the delivery method based on the child's emotions, it becomes possible to deliver more appropriate learning content. 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 processing described above in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input children's emotional data into a generating AI and have the generating AI adjust the method of providing learning content.

[0085] The service provider can monitor a child's concentration and fatigue level when providing learning content and suggest appropriate breaks. For example, if a child's concentration level decreases, the service provider can suggest a break. For example, if a child loses concentration on a particular learning activity, the service provider can suggest a break based on that information. The service provider can also suggest a break if a child's fatigue level increases. For example, if a child feels fatigued during a particular learning activity, the service provider can suggest a break based on that information. Furthermore, the service provider can comprehensively monitor a child's concentration and fatigue level and suggest appropriate breaks. For example, if a child loses concentration on a particular learning activity and feels fatigued, the service provider can suggest a break based on that information. In this way, by monitoring a child's concentration and fatigue level, appropriate breaks can be suggested, improving learning efficiency. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the child's concentration and fatigue level into a generating AI and have the generating AI suggest breaks.

[0086] The delivery unit can adjust the pace of learning content delivery to match the child's learning pace. For example, if the child is learning quickly, the delivery unit can increase the pace. For example, if the child is quickly understanding a particular topic, the delivery unit can adjust the pace based on that information. The delivery unit can also decrease the pace if the child is learning slowly. For example, if the child is taking a long time to understand a particular topic, the delivery unit can adjust the pace based on that information. Furthermore, the delivery unit can monitor the child's learning pace in real time and adjust the pace accordingly. For example, while the child is learning a particular topic, the delivery unit can instantly adjust the pace based on their progress. By adjusting the pace to match the child's learning pace, more effective learning becomes possible. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the child's learning pace data into a generating AI and have the generating AI perform the adjustment of the pace.

[0087] The delivery unit can estimate a child's emotions and adjust the timing of the delivery of learning content based on the estimated emotions. For example, if the child is enjoying the content, the delivery unit may speed up the delivery of the learning content. For example, if the child shows a high level of interest in a particular topic, the delivery unit may adjust the delivery timing based on that information. The delivery unit can also delay the delivery of learning content if the child is feeling stressed. For example, if the child is feeling stressed about a particular learning activity, the delivery unit may adjust the delivery timing based on that information. Furthermore, the delivery unit can optimize the delivery timing of learning content if the child is concentrating. For example, if the child is showing a high level of concentration on a particular learning method, the delivery unit may adjust the delivery timing based on that information. In this way, learning content can be delivered at a more appropriate time by adjusting the delivery timing based on the child'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 delivery unit may be performed using AI, for example, or without AI. For example, the service provider can input children's emotional data into a generating AI and have the AI ​​adjust the timing of the delivery of learning content.

[0088] The service provider can add a feature that allows parents and teachers to provide real-time feedback when providing learning content. For example, the service provider can add a feature that allows parents to provide real-time feedback. For example, the service provider can enable parents to check their child's learning progress in real time and provide feedback. The service provider can also add a feature that allows teachers to provide real-time feedback. For example, the service provider can enable teachers to check their child's learning progress in real time and provide feedback. Furthermore, the service provider can add a feature that allows parents and teachers to jointly provide real-time feedback. For example, the service provider can enable parents and teachers to collaborate to check their child's learning progress in real time and provide feedback. This allows parents and teachers to provide real-time feedback, thereby improving the quality of learning. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input feedback data from parents and teachers into a generating AI and have the generating AI perform real-time feedback provision.

[0089] The service provider can optimize the child's learning environment when providing learning content. For example, the service provider can optimize the volume when providing learning content. For example, the service provider can adjust the volume when the child is learning to provide an optimal learning environment. The service provider can also optimize the lighting when providing learning content. For example, the service provider can adjust the lighting when the child is learning to provide an optimal learning environment. Furthermore, the service provider can comprehensively optimize both volume and lighting when providing learning content. For example, the service provider can adjust both the volume and lighting when the child is learning to provide an optimal learning environment. By optimizing the learning environment in this way, the child's learning efficiency can be improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the child's learning environment data into a generating AI and have the generating AI perform the optimization of the learning environment.

[0090] The reporting system can estimate a child's emotions and adjust its report based on those estimates. For example, if a child is having fun, the system will provide a positive report. For example, if a child shows a high level of interest in a particular topic, the system will provide a positive report based on that information. The system can also identify the cause of stress if a child is experiencing stress and reflect that in the report. For example, if a child is experiencing stress from a particular learning activity, the system will report the cause of the stress based on that information. Furthermore, if a child is concentrating, the system can provide a report to help maintain that concentration. For example, if a child is showing high levels of concentration on a particular learning method, the system will provide a report to help maintain that concentration based on that information. By adjusting the report based on the child's emotions, parents and teachers can provide more appropriate support. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, 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 reporting department may be performed using AI, for example, or without AI. For example, the reporting department may input children's emotional data into a generating AI and have the generating AI adjust the content of the report.

[0091] The reporting unit can report learning progress and outcomes in a format that is easy for parents and teachers to understand. For example, the reporting unit can report learning progress in a format that is easy for parents to understand. For example, the reporting unit can visually display the child's learning status using graphs and charts. The reporting unit can also report learning outcomes in a format that is easy for teachers to understand. For example, the reporting unit can provide teachers with a text report on the child's learning outcomes. Furthermore, the reporting unit can report learning progress and outcomes in a format that is easy for parents and teachers to understand jointly. For example, the reporting unit can enable parents and teachers to collaboratively view the child's learning status on an interactive dashboard. This makes it easier for parents and teachers to understand the child's learning status by reporting in a format that is easy for them to understand. Some or all of the above processes in the reporting unit may be performed using AI, or not. For example, the reporting unit can input learning progress and outcome data into a generating AI and have the generating AI generate a report in an easy-to-understand format.

[0092] The reporting unit can visually display a child's learning history and behavioral data when reporting learning progress and results. For example, the reporting unit can visually display learning progress using graphs and charts. For example, the reporting unit can display a child's learning status using line graphs and bar graphs. The reporting unit can also visually display learning outcomes using visual reports. For example, the reporting unit can display a child's learning outcomes using infographics. Furthermore, the reporting unit can visually display learning history and behavioral data using interactive dashboards. For example, the reporting unit can allow parents and teachers to check a child's learning history and behavioral data in real time. This makes it easier for parents and teachers to understand a child's learning situation by visually displaying learning history and behavioral data. Some or all of the above processing in the reporting unit may be performed using AI, or not. For example, the reporting unit can input learning history and behavioral data into a generating AI and have the generating AI perform the visual display.

[0093] The reporting unit can estimate a child's emotions and adjust the frequency of reports based on the estimated emotions. For example, the reporting unit can increase the frequency of reports if the child is having fun. For example, if the reporting unit is showing a high level of interest in a particular topic, it can adjust the frequency of reports based on that information. The reporting unit can also decrease the frequency of reports if the child is feeling stressed. For example, if the reporting unit is feeling stressed about a particular learning activity, it can adjust the frequency of reports based on that information. Furthermore, the reporting unit can optimize the frequency of reports if the child is concentrating. For example, if the reporting unit is showing a high level of concentration on a particular learning method, it can adjust the frequency of reports based on that information. By adjusting the frequency of reports based on the child's emotions, parents and teachers can provide support at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting department can input children's emotional data into a generating AI and have the AI ​​adjust the frequency of reports.

[0094] The reporting unit can add reporting options that parents and teachers can customize. For example, the reporting unit can provide options that allow parents to customize the content of their reports. For instance, it can allow parents to select reporting items and formats according to their child's learning progress. The reporting unit can also provide options that allow teachers to customize the content of their reports. For instance, it can allow teachers to select reporting items and formats according to their child's learning progress. Furthermore, the reporting unit can provide options that allow parents and teachers to jointly customize the content of their reports. For example, it can allow parents and teachers to collaborate to provide reports that are tailored to their child's learning progress. This allows parents and teachers to customize the content of their reports, leading to a more accurate understanding of their child's learning situation. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input customized data from parents and teachers into a generating AI and have the generating AI perform the customization of the report content.

[0095] The reporting unit can add a function to compare children's learning goals and achievement levels when reporting learning progress and results. For example, the reporting unit can report learning progress in comparison to the child's learning goals. For example, the reporting unit can report the current progress against the learning goals set by the child. The reporting unit can also report learning outcomes in comparison to the child's achievement level. For example, the reporting unit can report the outcomes achieved by the child in comparison to the learning goals. Furthermore, the reporting unit can also provide a comprehensive comparison of learning goals and achievement levels. For example, the reporting unit can comprehensively evaluate the child's learning goals and achievement levels and report the results. This allows for a clearer understanding of the child's learning situation by comparing learning goals and achievement levels. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input learning goal and achievement level data into a generating AI and have the generating AI report the comparison results.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The KidsPod system may further include a visualization unit that visually displays a child's learning progress. This visualization unit can, for example, display a child's learning progress in graphs or charts. For example, it can display a child's learning status in line graphs or bar graphs. The visualization unit can also visually display learning outcomes in visual reports. For example, it can display a child's learning outcomes in infographics. Furthermore, the visualization unit can visually display learning history and behavioral data in an interactive dashboard. For example, the visualization unit can allow parents and teachers to check a child's learning history and behavioral data in real time. This makes it easier for parents and teachers to understand a child's learning situation by visually displaying learning history and behavioral data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit can input learning history and behavioral data into a generating AI and have the generating AI perform the visual display.

[0098] The KidsPod system may further include a reward unit that provides rewards based on the child's learning progress. This reward unit may, for example, provide rewards when the child achieves a specific learning goal. For example, the reward unit may provide a reward based on information that the child has finished learning a particular topic. The reward unit may also change the type of reward according to the child's learning progress. For example, the reward unit may provide a special reward based on information that the child has achieved high learning results. Furthermore, the reward unit may analyze the child's learning progress in real time and provide rewards dynamically. For example, the reward unit may immediately provide a reward based on the child's progress while the child is learning a particular topic. This can increase the child's motivation to learn by providing rewards based on learning progress. Some or all of the above processing in the reward unit may be performed using AI, for example, or not using AI. For example, the reward unit may input the child's learning progress data into a generating AI and have the generating AI perform the provision of rewards.

[0099] The KidsPod system can also include a notification unit that informs parents and teachers of the child's learning progress. This notification unit can, for example, send notifications to parents and teachers when the child achieves a specific learning goal. For example, it can send a notification when the child has finished learning a particular topic. The notification unit can also change the content of notifications according to the child's learning progress. For example, it can send a special notification when the child achieves high learning results. Furthermore, the notification unit can analyze the child's learning progress in real time and send notifications dynamically. For example, it can send an immediate notification based on the child's progress while the child is learning a particular topic. This makes it easier for parents and teachers to understand the child's learning situation by sending notifications based on learning progress. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the child's learning progress data into a generating AI and have the generating AI execute the sending of notifications.

[0100] The KidsPod system can also include a dashboard that allows parents and teachers to monitor a child's learning progress in real time. This dashboard can, for example, display a child's learning progress in graphs or charts. The dashboard can also visually display learning outcomes in visual reports. For example, it can display a child's learning outcomes in infographics. Furthermore, the dashboard can display learning history and behavioral data in an interactive format. For example, the dashboard can allow parents and teachers to view a child's learning history and behavioral data in real time. This makes it easier for parents and teachers to understand a child's learning situation by visually displaying learning history and behavioral data. Some or all of the above processing in the dashboard may be performed using AI, or not. For example, the dashboard can input learning history and behavioral data into a generating AI and have the generating AI perform the visual display.

[0101] The KidsPod system may also include a customization section that allows parents and teachers to customize a child's learning progress. This customization section may, for example, provide an option for parents to customize learning content. For instance, it may allow parents to adjust the difficulty level and format of the content according to their child's learning needs. The customization section may also provide an option for teachers to customize learning content. For instance, it may allow teachers to change the content according to the child's learning progress. Furthermore, the customization section may provide an option for parents and teachers to collaboratively customize learning content. For example, it may allow parents and teachers to work together to provide content that meets the child's learning needs. This allows parents and teachers to customize learning content, thereby providing children with the best possible learning experience. Some or all of the above-described processes in the customization section may be performed using AI, for example, or not. For example, the customization section may input parent and teacher customization data into a generating AI and have the generating AI perform the customization of the learning content.

[0102] The assessment unit can estimate a child's emotions and adjust the method of delivering learning content based on the estimated emotions. For example, if the child is enjoying the content, it can deliver it in an interactive way. For example, if the assessment unit shows a high level of interest in a particular topic, it can use that information to provide an interactive learning method. The assessment unit can also deliver learning content in a relaxing way if the child is feeling stressed. For example, if the assessment unit is stressed by a particular learning activity, it can use that information to provide a relaxing learning method. Furthermore, if the child is concentrating, the assessment unit can deliver learning content in a way that helps maintain their concentration. For example, if the assessment unit shows a high level of concentration on a particular learning method, it can use that information to provide a learning method that helps maintain their concentration. This allows for the delivery of more appropriate learning content by adjusting the delivery method based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the assessment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the child's emotional data into the generating AI, which can then adjust the method of providing learning content.

[0103] The judgment unit can estimate a child's emotions and adjust the timing of providing learning content based on the estimated emotions. For example, if the child is enjoying themselves, the timing of providing the learning content can be accelerated. For example, if the judgment unit shows a high level of interest in a particular topic, the timing of provision can be adjusted based on that information. The judgment unit can also delay the timing of providing learning content if the child is feeling stressed. For example, if the judgment unit is feeling stressed about a particular learning activity, the timing of provision can be adjusted based on that information. Furthermore, the judgment unit can optimize the timing of providing learning content if the child is concentrating. For example, if the judgment unit shows a high level of concentration on a particular learning method, the timing of provision can be adjusted based on that information. In this way, learning content can be provided at a more appropriate time by adjusting the timing of provision based on the child'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 judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input the child's emotional data into the generating AI, and have the generating AI adjust the timing of providing learning content.

[0104] The assessment unit can estimate a child's emotions and adjust the order in which learning content is provided based on the estimated emotions. For example, if the child is enjoying themselves, it can prioritize providing topics of interest. For example, if the assessment unit detects that the child has shown a high level of interest in a particular topic, it can adjust the order of provision based on that information. The assessment unit can also prioritize providing relaxing topics if the child is feeling stressed. For example, if the assessment unit detects that the child is feeling stressed by a particular learning activity, it can adjust the order of provision based on that information. Furthermore, if the child is concentrating, the assessment unit can prioritize providing more challenging topics. For example, if the assessment unit detects that the child is showing a high level of concentration on a particular learning method, it can adjust the order of provision based on that information. By adjusting the order of provision of learning content based on the child's emotions, more effective learning becomes possible. 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 assessment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the child's emotional data into the generating AI, and have the generating AI adjust the order in which learning content is provided.

[0105] The judgment unit can estimate a child's emotions and prioritize judgment results based on the estimated emotions. For example, if a child is enjoying themselves, the judgment unit prioritizes that emotion to determine topics of interest. For example, if a child shows high interest in a particular topic, the judgment unit uses that information to determine the child's interests. The judgment unit can also prioritize a child's stressed emotions and make judgments to eliminate the cause of stress. For example, if a child is stressed by a particular learning activity, the judgment unit adjusts the learning content based on that information. Furthermore, if a child is concentrating, the judgment unit can prioritize that emotion to maintain concentration. For example, if a child shows high concentration on a particular learning method, the judgment unit suggests the optimal learning method based on that information. In this way, prioritizing judgment results based on a child's emotions makes it possible to provide more appropriate learning content. 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-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the child's emotional data into a generating AI and have the generating AI determine the priority of the judgment results.

[0106] The customization unit can estimate a child's emotions and adjust the difficulty level and format of the learning content based on those estimates. For example, if a child is enjoying themselves, the difficulty level can be increased to provide more challenging content. For example, if a child shows a high level of interest in a particular topic, the customization unit can adjust the difficulty level based on that information. The customization unit can also lower the difficulty level and provide more relaxing content if a child is feeling stressed. For example, if a child is feeling stressed by a particular learning activity, the customization unit can adjust the difficulty level based on that information. Furthermore, if a child is concentrating, the customization unit can change the format to provide a new way of learning. For example, if a child shows a high level of concentration on a particular learning method, the customization unit can adjust the format based on that information. By adjusting the difficulty level and format of the learning content based on a child's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization section can input children's emotional data into a generating AI, which can then adjust the difficulty level and format of the learning content.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The assessment unit determines the child's interests and abilities. The assessment unit analyzes, for example, the child's learning history and behavioral data to identify the individual child's interests and abilities. For example, if the assessment unit sees a child showing a high level of interest in a particular topic, it will use that information to determine the child's interests. The assessment unit can also determine the child's abilities based on the child's learning history. For example, it analyzes past test results and study time to evaluate the child's academic ability and skills. Furthermore, the assessment unit can identify the child's learning style based on the child's behavioral data. For example, it analyzes what learning methods are most effective for the child and suggests the optimal learning method. Step 2: The customization unit customizes the learning content according to the child's interests and abilities as determined by the assessment unit. For example, the customization unit generates optimal learning content for the child based on the information obtained from the assessment unit. For example, if the child is interested in mathematics, the customization unit will provide mathematics-related content. The customization unit can also adjust the difficulty level of the learning content according to the child's abilities. For example, the customization unit will provide problems in stages, from easy to difficult, according to the child's academic level. Furthermore, the customization unit can change the format of the learning content according to the child's learning style. For example, the customization unit will provide visual content for visually-oriented children and audio content for auditory-oriented children. Step 3: The delivery unit provides children with learning content customized by the customization unit. The delivery unit provides learning content, for example, through interaction with characters. For example, the delivery unit maintains the child's motivation to learn by having them interact with characters as they progress through their learning. The delivery unit can also provide learning content in an interactive format. For example, the delivery unit allows children to learn through quizzes and games. Furthermore, the delivery unit can provide learning content in real time. For example, the delivery unit can immediately answer any questions or doubts the child has while learning. Step 4: The reporting department reports to the child's parents on the child's learning progress and achievements in the learning content. The reporting department, for example, reports to parents on the child's learning progress and achievements on a regular basis. For example, the reporting department provides weekly or monthly reports on the child's learning status. The reporting department can also enable parents to check their child's learning progress in real time. For example, the reporting department can enable parents to check their child's learning status at any time through a web application or mobile application. Furthermore, the reporting department can enable parents to provide appropriate support according to their child's learning status. For example, the reporting department can suggest advice and support methods to parents based on their child's learning progress.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] For example, the determination unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the customization unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the provision unit is implemented by the control unit 46A of the smart device 14. For example, the reporting unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] For example, the determination unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the customization unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the provision unit is implemented by the control unit 46A of the smart glasses 214. For example, the reporting unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] For example, the determination unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the customization unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the provision unit is implemented by the control unit 46A of the headset terminal 314. For example, the reporting unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] For example, the determination unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the customization unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the provision unit is implemented by the control unit 46A of the robot 414. For example, the reporting unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A judgment unit that determines the child's interests and abilities, A customization unit that customizes learning content according to the child's interests and abilities determined by the determination unit, A providing unit that provides the learning content customized by the customization unit to the child, The system includes a reporting unit that reports the child's learning progress and results in the learning content to the child's parents. A system characterized by the following features. (Note 2) The aforementioned supply unit is, The learning content is provided through interaction with the character. The system described in Appendix 1, characterized by the features described herein. (Note 3) The determination unit, Analyze children's learning history and behavioral data to identify individual children's interests and abilities. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned customization unit is Based on the information obtained from the judgment unit, learning content suitable for the child is generated. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reporting department, Regularly report to parents on their child's learning progress and achievements. The system described in Appendix 1, characterized by the features described herein. (Note 6) The determination unit, This system estimates children's emotions and improves the accuracy of determining their interests and abilities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The determination unit, In addition to a child's learning history, their daily behavioral data and hobbies and preferences are analyzed to identify their interests and abilities. The system described in Appendix 1, characterized by the features described herein. (Note 8) The determination unit, Determining a child's interests and abilities based on their learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The determination unit, The system estimates the child's emotions and determines the priority of the judgment results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The determination unit, In addition to children's learning history and behavioral data, feedback from parents and teachers is incorporated to determine their interests and abilities. The system described in Appendix 1, characterized by the features described herein. (Note 11) The determination unit, It analyzes children's learning history and behavioral data in real time, instantly reflecting changes in their interests and abilities. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned customization unit is It estimates a child's emotions and adjusts the difficulty level and format of learning content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned customization unit is Customize learning content according to the child's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned customization unit is Dynamically generates the next learning content based on the child's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned customization unit is The system estimates the child's emotions and adjusts the order in which learning content is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned customization unit is Incorporate game elements into learning content to capture children's interest. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned customization unit is Add options to learning content that parents and teachers can customize. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, The system estimates children's emotions and adjusts how learning content is delivered based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing learning content, monitor children's concentration and fatigue levels and suggest appropriate breaks. The system described in Appendix 2, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing learning content, adjust the pace to match the child's learning speed. The system described in Appendix 2, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the child's emotions and adjusts the timing of providing learning content based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 22) The aforementioned supply unit is, We will add a feature that allows parents and teachers to provide real-time feedback when delivering learning content. The system described in Appendix 2, characterized by the features described herein. (Note 23) The aforementioned supply unit is, Optimizing the learning environment for children when providing learning content. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned reporting department, The system estimates the child's emotions and adjusts the report based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reporting department, Report learning progress and results in a format that is easy for parents and teachers to understand. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reporting department, When reporting on learning progress and achievements, the child's learning history and behavioral data are displayed visually. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reporting department, The system estimates the child's emotions and adjusts the frequency of reports based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reporting department, Add customizable reporting options for parents and teachers. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reporting department, Add a feature to compare children's learning goals and achievement levels when reporting learning progress and results. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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. A judgment unit that determines the child's interests and abilities, A customization unit that customizes learning content according to the child's interests and abilities determined by the determination unit, A providing unit that provides the learning content customized by the customization unit to the child, The system includes a reporting unit that reports the child's learning progress and results in the learning content to the child's parents. A system characterized by the following features.

2. The aforementioned supply unit is, The learning content is provided through interaction with the character. The system according to feature 1.

3. The determination unit, The learning history and behavioral data of the aforementioned child are analyzed to identify the child's interests and abilities. The system according to feature 1.

4. The aforementioned reporting department, The parents will be regularly informed of the child's learning progress and achievements. The system according to feature 1.

5. The determination unit, The system estimates the child's emotions and improves the accuracy of determining their interests and abilities based on the estimated emotions. The system according to feature 1.

6. The determination unit, In addition to the child's learning history, their interests and abilities are identified by analyzing their daily behavioral data and hobbies and preferences. The system according to feature 1.

7. The determination unit, Based on the aforementioned learning environment of the child, we will determine their interests and abilities. The system according to feature 1.

8. The determination unit, The system estimates the child's emotions and determines the priority of the judgment results based on the estimated emotions of the child. The system according to feature 1.

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