System

Abstract

An object of a system according to an embodiment is to provide an optimal individual learning plan based on a child's personality and interests.SOLUTION: A system according to an embodiment includes a personality, characteristic, and interest analysis part, a learning plan generation part, and an interaction learning part. The personality, characteristic, and interest analysis unit analyzes the personality, characteristic, and interest of the child using the generated AI. A learning plan generation part generates an individual learning plan optimal to the child on the basis of the result analyzed by the personality, characteristic and interest analysis part. The interaction learning unit advances learning while interacting with the child on the basis of the learning plan generated by the learning plan generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology makes it difficult to provide individual learning plans based on a child's personality and interests, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal individual learning plan based on a child's personality and interests. [Means for solving the problem]

[0006] The system according to the embodiment includes a personality, characteristics, and interest analysis unit, a learning plan generation unit, and an interactive learning unit. The personality, characteristics, and interest analysis unit analyzes a child's personality, characteristics, and interests using a generation AI. The learning plan generation unit generates an optimal individual learning plan for the child based on the results of the analysis by the personality, characteristics, and interest analysis unit. The interactive learning unit advances learning through dialogue with the child based on the learning plan generated by the learning plan generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal individual learning plan based on the child's personality and interests. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The education provision system according to an embodiment of the present invention uses a generative AI to provide optimal education tailored to the personality, characteristics, and interests of each child. This allows the education provision system to provide optimal learning plans based on the child's personality, characteristics, and interests, enabling interactive learning.

[0029] An education provision system according to an embodiment includes a personality, trait, and interest analysis unit, a study plan generation unit, and an interactive learning unit. The personality, trait, and interest analysis unit analyzes a child's personality, trait, and interests. For example, the generation AI performs analysis based on the child's dialogue with the generation AI, past learning history, survey results, and the like. The generation AI understands a child's interests through questions such as, "What is your favorite subject?" and "What is your dream for the future?" The study plan generation unit generates an optimal individual study plan for a child based on the results of the analysis by the personality, trait, and interest analysis unit. For example, the generation AI provides more advanced math problems to a child who is good at math, and creates a plan to carefully teach weaker subjects from the basics. Furthermore, providing teaching materials and assignments related to the child's areas of interest can increase the child's motivation to learn. The interactive learning unit progresses the child's study through dialogue with the child based on the study plan generated by the study plan generation unit. For example, the generation AI checks the child's level of understanding through questions such as, "If you don't know how to solve this problem, what part is difficult?" and provides appropriate advice. This allows the education provision system to provide optimal learning plans based on the child's personality, characteristics, and interests, enabling interactive learning.

[0030] The personality, characteristics, and interest analysis unit can analyze a child's behavioral patterns or learning history to identify the child's learning style. The personality, characteristics, and interest analysis unit, for example, analyzes a child's learning history to identify what learning materials or learning methods were effective. For example, a visual child can be provided with learning materials that make extensive use of diagrams and graphs. The personality, characteristics, and interest analysis unit can also analyze a child's behavioral patterns to identify the child's learning style. For example, an auditory child can be provided with audio learning materials or podcasts. The personality, characteristics, and interest analysis unit can also analyze a child's learning history and behavioral patterns in combination to provide a learning plan that incorporates experiments and fieldwork for an experiential child. This makes it possible to identify a child's learning style and provide the optimal learning method.

[0031] The personality, traits, and interest analysis unit can collect data on a child's social media or online activities and analyze the child's interests. For example, the personality, traits, and interest analysis unit analyzes a child's social media accounts and identifies interests based on the types of posts and followers the child makes. For example, if there are many science-related posts, it can be determined that the child is interested in science. The personality, traits, and interest analysis unit also collects data on a child's online activities and analyzes the websites and videos the child views. For example, if the child watches many history-related videos, it can be determined that the child is interested in history. The personality, traits, and interest analysis unit can also collect data on a child's social media or online activities over the long term and analyze changes in interests. For example, it can use data over several months to determine whether interests are changing and reflect this in learning plans. This allows for a more detailed understanding of a child's interests based on data on a child's social media and online activities.

[0032] The personality, traits, and interest analysis unit can share the analysis results of a child's personality, traits, and interests with parents or teachers, allowing them to use them for support at home and at school. For example, the personality, traits, and interest analysis unit can provide parents with regular reports of the child's personality, traits, and interests analyzed by the generative AI. For example, it can provide monthly reports on changes in the child's learning status and interests. The personality, traits, and interest analysis unit can also share the analysis results with teachers to use them in lessons and support at school. For example, it can increase the number of lessons related to the child's areas of interest. The personality, traits, and interest analysis unit can also allow parents and teachers to adjust the learning environment at home and at school based on the analysis results. For example, if a child has a visual learning style, it can use more diagrams and graphs in the home learning space. Sharing the analysis results with parents and teachers can enhance support at home and at school.

[0033] The personality, characteristics, and interest analysis unit can analyze a child's personality, characteristics, and interests by also taking into account non-learning-related information such as pets or hobbies. The personality, characteristics, and interest analysis unit, for example, analyzes the type and name of a child's pet and identifies the child's interests based on that information. For example, it provides educational materials related to animals. The personality, characteristics, and interest analysis unit also collects information about a child's hobbies and special skills and reflects this in a learning plan. For example, a child whose hobby is music is provided with educational materials related to music. The personality, characteristics, and interest analysis unit also comprehensively analyzes non-learning-related information to obtain a more detailed understanding of a child's personality and characteristics. For example, a child who likes sports is provided with a learning plan that incorporates exercise. This enables a more comprehensive analysis by also taking into account non-learning-related information.

[0034] The learning plan generation unit can set long-term learning goals based on the child's learning history and interests, and generate a step-by-step learning plan toward those goals. The learning plan generation unit, for example, analyzes the child's learning history and sets long-term learning goals. For example, it sets goals to be achieved in one year and provides a step-by-step learning plan toward those goals. The learning plan generation unit also sets long-term learning goals based on the child's interests and generates a learning plan toward those goals. For example, it sets goals related to areas in which the child is interested. The learning plan generation unit also combines the learning history and interests to set long-term learning goals and continuously adjusts the learning plan toward those goals. For example, it updates the learning plan according to the degree of goal achievement. In this way, by setting long-term learning goals and providing a step-by-step learning plan toward those goals, it is possible to improve the child's learning effectiveness.

[0035] The learning plan generation unit can generate a learning plan that incorporates elements of cooperative learning or competition with other children to increase motivation to learn. The learning plan generation unit, for example, incorporates elements of cooperative learning into the learning plan to provide opportunities to learn with other children. For example, it sets up group projects or joint assignments. The learning plan generation unit also incorporates elements of competition into the learning plan to increase motivation to learn for children. For example, it introduces quiz-style assignments or a ranking system. The learning plan generation unit also provides a learning plan that combines elements of cooperative learning and competition to continuously increase motivation to learn for children. For example, it sets up team game-style assignments. In this way, it is possible to increase motivation to learn for children by incorporating elements of cooperative learning and competition with other children.

[0036] The learning plan generation unit provides a customization function that allows parents or teachers to customize the learning plan, thereby strengthening support at home and at school. The learning plan generation unit, for example, provides a function that allows parents or teachers to customize the learning plan generated by the generation AI. For example, a function to adjust learning content and schedule is added. The learning plan generation unit also provides support to parents and teachers when they customize the learning plan. For example, it provides appropriate advice when customizing. The learning plan generation unit also strengthens learning support at home and at school based on the customized learning plan. For example, parents and teachers support their children based on the learning plan. This allows parents and teachers to customize the learning plan, thereby strengthening support at home and at school.

[0037] The learning plan generator can enable seamless use of the learning plan across different devices. For example, the learning plan generator can enable seamless use of the learning plan generated by the generation AI across different devices, such as smartphones, tablets, and PCs. For example, it can provide a cloud-based platform. The learning plan generator can also synchronize the learning plan across different devices, allowing children to continue their learning on any device. For example, starting learning on a smartphone and continuing on a PC. The learning plan generator can also automatically adjust the learning plan to support seamless use across devices. For example, it can optimize the learning content according to the characteristics of the device. This allows the learning plan to be used seamlessly across different devices, thereby enhancing learning continuity.

[0038] The dialogue learning unit can analyze a child's pronunciation or language skills and provide support for language learning. For example, the dialogue learning unit analyzes a child's pronunciation and provides feedback to help the child learn correct pronunciation. For example, it points out pronunciation errors and teaches correct pronunciation. The dialogue learning unit also analyzes a child's language skills and provides learning materials at an appropriate level. For example, it provides learning materials according to the child's vocabulary and level of grammar understanding. The dialogue learning unit also accumulates data on a child's pronunciation and language skills and evaluates long-term learning effectiveness. For example, it adjusts a learning plan based on past data. In this way, the effectiveness of language learning can be improved by analyzing a child's pronunciation and language skills and providing appropriate support.

[0039] The dialogue learning unit can record the content of the dialogue between the child and the generation AI so that it can be reviewed later. For example, the dialogue learning unit automatically records the content of the dialogue between the child and the generation AI so that it can be reviewed later. For example, it saves a log of the dialogue so that the child can access it at any time. The dialogue learning unit also saves the content of the dialogue as text data so that the child can search for it when reviewing it. For example, it can display related content of the dialogue by entering a specific keyword. The dialogue learning unit also saves the content of the dialogue as audio data so that it can be played back when the child reviews it. For example, it can save an audio file of the dialogue so that the child can listen to it again. In this way, the learning effect can be improved by recording the content of the dialogue so that it can be reviewed later.

[0040] The dialogue learning unit can provide a group dialogue function in which multiple children can participate simultaneously. The dialogue learning unit provides, for example, a group dialogue function in which the generation AI can dialogue with multiple children simultaneously. For example, it sets up an dialogue session in which multiple children can participate, like an online class. The dialogue learning unit also uses the group dialogue function to provide an environment in which children can learn cooperatively. For example, it sets up group discussions and joint assignments. The dialogue learning unit also improves children's communication skills through the group dialogue function. For example, it provides opportunities for children to hear the opinions of other children. As a result, providing a group dialogue function in which multiple children can participate simultaneously can promote cooperative learning and the improvement of communication skills.

[0041] The dialogue learning unit can analyze the content of a child's dialogue and report learning progress or challenges to parents or teachers. In the dialogue learning unit, for example, the generation AI analyzes the content of a child's dialogue and reports learning progress and challenges to parents or teachers. For example, it reports the learning situation in the form of a weekly report. The dialogue learning unit also evaluates the child's level of understanding and learning progress based on the content of the dialogue and provides feedback to parents and teachers. For example, it points out areas where understanding is low. In addition, the dialogue learning unit provides a dialogue log to parents and teachers so that they can check the content of the dialogue. For example, the dialogue log can be made available for viewing on an online platform. This makes it possible to analyze the content of the dialogue and report learning progress and challenges to parents and teachers, thereby strengthening learning support.

[0042] The dialogue learning unit can evaluate a child's level of comprehension in real time when providing learning support in a specialized field, and provide learning materials at an appropriate level of difficulty. The dialogue learning unit, for example, evaluates a child's level of comprehension in real time while learning a specialized field, and provides learning materials at an appropriate level of difficulty. For example, if the level of comprehension is high, the difficulty level is increased. The dialogue learning unit also provides learning materials at an optimal level of difficulty for the child based on the level of comprehension. For example, if the level of comprehension is low, learning materials that carefully teach the child from the basics are provided. The dialogue learning unit also accumulates comprehension data and evaluates long-term learning effectiveness. For example, the learning plan is adjusted based on past data. As a result, when providing learning support in a specialized field, learning effectiveness can be improved by providing learning materials at an appropriate level of difficulty according to the child's level of comprehension.

[0043] When providing learning support in specialized fields, the interactive learning unit can suggest future career paths based on the child's learning history. The interactive learning unit, for example, analyzes the child's learning history and suggests future career paths. For example, an IT-related career is suggested for a child who is interested in programming. The interactive learning unit also suggests career paths that are suitable for the child based on their learning history. For example, a career as a researcher or engineer is suggested for a child who is interested in science. The interactive learning unit also accumulates learning history data and suggests long-term career paths. For example, it suggests the optimal career path based on past data. In this way, when providing learning support in specialized fields, it is possible to clarify the child's future direction by suggesting future career paths based on the child's learning history.

[0044] The interactive learning unit can utilize online communities to promote interaction with other children and experts when providing learning support in specialized fields. The interactive learning unit, for example, utilizes online communities to promote interaction with other children and experts when providing learning support in specialized fields. For example, it provides online forums and chat rooms. The interactive learning unit also provides an environment through the online community where children can cooperate and learn. For example, it sets up joint projects and discussions. The interactive learning unit also increases children's motivation to learn through interaction with experts. For example, it provides online lectures and Q&A sessions by experts. In this way, the interactive learning unit can utilize online communities to promote interaction with other children and experts when providing learning support in specialized fields, thereby increasing motivation to learn.

[0045] The interactive learning unit can help students acquire practical skills through actual projects or assignments when providing learning support in specialized fields. For example, the interactive learning unit can help students acquire practical skills through actual projects or assignments when providing learning support in specialized fields. For example, the interactive learning unit can set up programming projects or scientific experiments. The interactive learning unit can also improve children's skills through practical assignments. For example, the interactive learning unit can provide history research projects or art production assignments. The interactive learning unit can also increase children's motivation to learn through actual projects. For example, the interactive learning unit can set up projects that address social issues or assignments to create a business plan. In this way, the learning effect can be improved by helping students acquire practical skills through actual projects or assignments when providing learning support in specialized fields.

[0046] When managing learning progress, the interactive learning unit can set individual learning goals based on the child's learning data and evaluate the degree of achievement of those goals. The interactive learning unit, for example, analyzes the child's learning data and sets individual learning goals. For example, it sets a goal to be achieved in one month and evaluates the degree of achievement of that goal. The interactive learning unit also sets optimal learning goals for the child based on the learning data. For example, it adjusts the goals according to the child's level of understanding and progress. The interactive learning unit also evaluates the degree of achievement of the learning goals and adjusts the learning plan as necessary. For example, if the goal is not achieved, it provides supplementary lessons or additional assignments. In this way, when managing learning progress, it is possible to improve learning effectiveness by setting individual learning goals based on the child's learning data and evaluating the degree of achievement of those goals.

[0047] The interactive learning unit can visualize a child's learning data when managing learning progress and provide parents or teachers with reports that are easy to understand intuitively. For example, the interactive learning unit visualizes a child's learning data in graphs or charts and provides them to parents or teachers. For example, this allows them to grasp learning progress and level of understanding at a glance. The interactive learning unit also generates reports that are easy for parents and teachers to understand intuitively based on the learning data. For example, it provides weekly or monthly reports. The interactive learning unit also makes it easier for parents and teachers to understand a child's learning situation based on the visualized data. For example, it clarifies areas where learning needs reinforcement. As a result, when managing learning progress, learning support can be enhanced by visualizing a child's learning data and providing parents and teachers with reports that are easy to understand intuitively.

[0048] The interactive learning unit can strengthen collaboration with different learning platforms or teaching materials when managing learning progress, thereby realizing integrated learning management. The interactive learning unit, for example, strengthens collaboration with different learning platforms and teaching materials to realize integrated learning management. For example, it collaborates with online teaching materials and apps to centrally manage learning data. The interactive learning unit also integrates data from learning platforms and teaching materials to comprehensively evaluate a child's learning progress. For example, it compiles progress data from multiple teaching materials into a single report. The interactive learning unit also makes a child's learning experience seamless through collaboration with different platforms and teaching materials. For example, it synchronizes learning data between different devices and apps. This strengthens collaboration with different learning platforms and teaching materials when managing learning progress, thereby realizing integrated learning management and improving learning effectiveness.

[0049] When managing learning progress, the interactive learning unit can evaluate the effectiveness of learning based on the child's learning data and suggest the most appropriate learning method. The interactive learning unit, for example, analyzes the child's learning data and evaluates the effectiveness of learning. For example, it evaluates the effectiveness of the learning method based on the level of understanding and progress. The interactive learning unit also suggests the most appropriate learning method for the child based on the learning data. For example, it provides learning materials that make extensive use of diagrams and graphs for visually-inclined children. The interactive learning unit also continuously evaluates the effectiveness of learning and suggests the most appropriate learning method. For example, it adjusts the learning plan based on past data. In this way, when managing learning progress, it is possible to improve learning effectiveness by evaluating the effectiveness of learning based on the child's learning data and suggesting the most appropriate learning method.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The education provision system can also monitor a child's physical health and reflect this in the learning plan. For example, it can monitor a child's sleep time and exercise amount and set appropriate break times. It can also adjust the learning load according to the child's health condition. For example, if the child is not feeling well, it can provide lighter tasks. This makes it possible to provide a learning plan that takes the child's health condition into consideration.

[0052] The education provision system can also provide creative tasks to bring out children's creativity. For example, tasks such as drawing a picture, writing a story, or composing music can be set. Creative tasks can also enhance children's ability to express themselves. For example, presentations or exhibitions can be held. This makes it possible to provide a learning plan that fosters children's creativity.

[0053] The education delivery system can also provide collaborative learning opportunities to foster children's social skills, such as setting up group projects and discussions. It can also help children improve their communication skills through interactions with other children, such as by providing online forums and chat rooms. This can help provide a learning plan that fosters children's social skills.

[0054] The education provision system can further provide a self-assessment function to enhance children's self-management abilities. For example, a function for self-assessing learning progress and achievement level can be added. The system can also adjust the learning plan based on the self-assessment. For example, the difficulty of the assignments can be adjusted according to the results of the self-assessment. This makes it possible to provide a learning plan that enhances children's self-management abilities.

[0055] The education provision system can also introduce a reward system to further motivate children to learn. For example, points are awarded for each task completed, and children can receive rewards when they collect a certain number of points. The reward system can also help maintain children's motivation. For example, a ranking system can be introduced to add an element of competition with other children. This can further increase children's motivation to learn.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The personality, characteristics, and interest analysis unit analyzes the child's personality, characteristics, and interests. For example, the generation AI performs analysis based on the child's dialogue with the generation AI, past learning history, and survey results. The generation AI grasps the child's interests through questions such as, "What is your favorite subject?" and "What is your dream for the future?" Step 2: The learning plan generator generates an optimal individual learning plan for each child based on the results of the analysis by the personality, characteristics, and interest analysis unit. For example, the generator AI provides more advanced math problems to children who are good at math, and creates a plan to carefully teach weaker subjects from the basics. It can also increase motivation to learn by providing learning materials and assignments related to areas of interest. Step 3: The interactive learning unit engages the child in dialogue based on the learning plan generated by the learning plan generation unit. For example, the AI ​​can check the child's level of understanding by asking questions such as, "If you don't know how to solve this problem, which part is difficult?" and provide appropriate advice.

[0058] (Example 2) The education provision system according to an embodiment of the present invention uses a generative AI to provide optimal education tailored to the personality, characteristics, and interests of each child. This allows the education provision system to provide optimal learning plans based on the child's personality, characteristics, and interests, enabling interactive learning.

[0059] An education provision system according to an embodiment includes a personality, trait, and interest analysis unit, a study plan generation unit, and an interactive learning unit. The personality, trait, and interest analysis unit analyzes a child's personality, trait, and interests. For example, the generation AI performs analysis based on the child's dialogue with the generation AI, past learning history, survey results, and the like. The generation AI understands a child's interests through questions such as, "What is your favorite subject?" and "What is your dream for the future?" The study plan generation unit generates an optimal individual study plan for a child based on the results of the analysis by the personality, trait, and interest analysis unit. For example, the generation AI provides more advanced math problems to a child who is good at math, and creates a plan to carefully teach weaker subjects from the basics. Furthermore, providing teaching materials and assignments related to the child's areas of interest can increase the child's motivation to learn. The interactive learning unit progresses the child's study through dialogue with the child based on the study plan generated by the study plan generation unit. For example, the generation AI checks the child's level of understanding through questions such as, "If you don't know how to solve this problem, what part is difficult?" and provides appropriate advice. This allows the education provision system to provide optimal learning plans based on the child's personality, characteristics, and interests, enabling interactive learning.

[0060] The personality, traits, and interest analysis unit uses the emotion estimation function to analyze a child's emotions based on the child's dialogue data and reevaluate the child's personality and traits in response to changes in emotion. For example, the personality, traits, and interest analysis unit uses the emotion estimation function to analyze a child's emotions in real time during a dialogue between the child and the generating AI. For example, if a child says, "I had fun today," the emotion is evaluated as positive and reflected in the reevaluation of the child's personality and traits. The personality, traits, and interest analysis unit also accumulates the child's dialogue data and analyzes changes in emotion over the long term. For example, based on dialogue data over several months, the system can grasp a child's emotional trends and reevaluate their personality and traits. The personality, traits, and interest analysis unit also uses the emotion estimation function to analyze the child's emotions toward specific topics. For example, if a child says, "I hate math," the emotion is evaluated as negative and reflected in adjusting the learning plan. This allows the system to reevaluate the child's personality and traits in response to changes in emotion and provide a more appropriate learning plan.

[0061] The personality, characteristics, and interest analysis unit can analyze a child's behavioral patterns or learning history to identify the child's learning style. The personality, characteristics, and interest analysis unit, for example, analyzes a child's learning history to identify what learning materials or learning methods were effective. For example, a visual child can be provided with learning materials that make extensive use of diagrams and graphs. The personality, characteristics, and interest analysis unit can also analyze a child's behavioral patterns to identify the child's learning style. For example, an auditory child can be provided with audio learning materials or podcasts. The personality, characteristics, and interest analysis unit can also analyze a child's learning history and behavioral patterns in combination to provide a learning plan that incorporates experiments and fieldwork for an experiential child. This makes it possible to identify a child's learning style and provide the optimal learning method.

[0062] The personality, traits, and interest analysis unit can collect data on a child's social media or online activities and analyze the child's interests. For example, the personality, traits, and interest analysis unit analyzes a child's social media accounts and identifies interests based on the types of posts and followers the child makes. For example, if there are many science-related posts, it can be determined that the child is interested in science. The personality, traits, and interest analysis unit also collects data on a child's online activities and analyzes the websites and videos the child views. For example, if the child watches many history-related videos, it can be determined that the child is interested in history. The personality, traits, and interest analysis unit can also collect data on a child's social media or online activities over the long term and analyze changes in interests. For example, it can use data over several months to determine whether interests are changing and reflect this in learning plans. This allows for a more detailed understanding of a child's interests based on data on a child's social media and online activities.

[0063] The personality, traits, and interest analysis unit can share the analysis results of a child's personality, traits, and interests with parents or teachers, allowing them to use them for support at home and at school. For example, the personality, traits, and interest analysis unit can provide parents with regular reports of the child's personality, traits, and interests analyzed by the generative AI. For example, it can provide monthly reports on changes in the child's learning status and interests. The personality, traits, and interest analysis unit can also share the analysis results with teachers to use them in lessons and support at school. For example, it can increase the number of lessons related to the child's areas of interest. The personality, traits, and interest analysis unit can also allow parents and teachers to adjust the learning environment at home and at school based on the analysis results. For example, if a child has a visual learning style, it can use more diagrams and graphs in the home learning space. Sharing the analysis results with parents and teachers can enhance support at home and at school.

[0064] The personality, characteristics, and interest analysis unit can analyze a child's personality, characteristics, and interests by also taking into account non-learning-related information such as pets or hobbies. The personality, characteristics, and interest analysis unit, for example, analyzes the type and name of a child's pet and identifies the child's interests based on that information. For example, it provides educational materials related to animals. The personality, characteristics, and interest analysis unit also collects information about a child's hobbies and special skills and reflects this in a learning plan. For example, a child whose hobby is music is provided with educational materials related to music. The personality, characteristics, and interest analysis unit also comprehensively analyzes non-learning-related information to obtain a more detailed understanding of a child's personality and characteristics. For example, a child who likes sports is provided with a learning plan that incorporates exercise. This enables a more comprehensive analysis by also taking into account non-learning-related information.

[0065] The personality, traits, and interest analysis unit uses the emotion estimation function to detect stress or anxiety felt by children while they are studying in real time and suggest appropriate relaxation methods. The personality, traits, and interest analysis unit, for example, analyzes a child's facial expressions and voice while they are studying to detect stress or anxiety in real time. For example, it estimates emotions based on facial tension and tone of voice. Furthermore, if the personality, traits, and interest analysis unit detects stress or anxiety, the generative AI suggests relaxation methods. For example, it may advise the child to take deep breaths or a short break. Furthermore, the personality, traits, and interest analysis unit adjusts the child's learning environment based on the suggested relaxation methods. For example, it may take measures such as playing relaxing music. This allows the system to detect a child's stress or anxiety in real time and suggest appropriate relaxation methods, thereby improving the learning environment.

[0066] The learning plan generation unit can use the emotion estimation function to analyze a child's emotions while learning and generate a learning plan that elicits positive emotions. The learning plan generation unit, for example, analyzes a child's emotions while learning in real time and generates a learning plan that elicits positive emotions. For example, it provides game-style learning materials that allow children to learn while having fun. The learning plan generation unit also uses the emotion estimation function to identify learning content that evokes positive emotions in children and adjusts the learning plan based on that. For example, it provides tasks related to topics that interest the child. The learning plan generation unit also accumulates emotional data during learning and continuously improves the optimal learning plan for eliciting positive emotions. For example, it updates the learning plan based on past data. In this way, by generating a learning plan that elicits positive emotions in children, it is possible to increase their motivation to learn.

[0067] The learning plan generation unit can set long-term learning goals based on the child's learning history and interests, and generate a step-by-step learning plan toward those goals. The learning plan generation unit, for example, analyzes the child's learning history and sets long-term learning goals. For example, it sets goals to be achieved in one year and provides a step-by-step learning plan toward those goals. The learning plan generation unit also sets long-term learning goals based on the child's interests and generates a learning plan toward those goals. For example, it sets goals related to areas in which the child is interested. The learning plan generation unit also combines the learning history and interests to set long-term learning goals and continuously adjusts the learning plan toward those goals. For example, it updates the learning plan according to the degree of goal achievement. In this way, by setting long-term learning goals and providing a step-by-step learning plan toward those goals, it is possible to improve the child's learning effectiveness.

[0068] The learning plan generation unit can generate a learning plan that incorporates elements of cooperative learning or competition with other children to increase motivation to learn. The learning plan generation unit, for example, incorporates elements of cooperative learning into the learning plan to provide opportunities to learn with other children. For example, it sets up group projects or joint assignments. The learning plan generation unit also incorporates elements of competition into the learning plan to increase motivation to learn for children. For example, it introduces quiz-style assignments or a ranking system. The learning plan generation unit also provides a learning plan that combines elements of cooperative learning and competition to continuously increase motivation to learn for children. For example, it sets up team game-style assignments. In this way, it is possible to increase motivation to learn for children by incorporating elements of cooperative learning and competition with other children.

[0069] The learning plan generation unit provides a customization function that allows parents or teachers to customize the learning plan, thereby strengthening support at home and at school. The learning plan generation unit, for example, provides a function that allows parents or teachers to customize the learning plan generated by the generation AI. For example, a function to adjust learning content and schedule is added. The learning plan generation unit also provides support to parents and teachers when they customize the learning plan. For example, it provides appropriate advice when customizing. The learning plan generation unit also strengthens learning support at home and at school based on the customized learning plan. For example, parents and teachers support their children based on the learning plan. This allows parents and teachers to customize the learning plan, thereby strengthening support at home and at school.

[0070] The learning plan generator can enable seamless use of the learning plan across different devices. For example, the learning plan generator can enable seamless use of the learning plan generated by the generation AI across different devices, such as smartphones, tablets, and PCs. For example, it can provide a cloud-based platform. The learning plan generator can also synchronize the learning plan across different devices, allowing children to continue their learning on any device. For example, starting learning on a smartphone and continuing on a PC. The learning plan generator can also automatically adjust the learning plan to support seamless use across devices. For example, it can optimize the learning content according to the characteristics of the device. This allows the learning plan to be used seamlessly across different devices, thereby enhancing learning continuity.

[0071] The learning plan generation unit uses the emotion estimation function to monitor a child's emotional reactions to the learning plan and continuously evaluate and improve the effectiveness of the plan. For example, the learning plan generation unit monitors a child's emotional reactions to the learning plan in real time and evaluates the effectiveness of the plan based on that data. For example, it prioritizes the adoption of plans that have a high percentage of positive emotions. The learning plan generation unit also uses the emotion estimation function to continuously evaluate the effectiveness of the learning plan and improve the plan as needed. For example, it adjusts the plan if there are a high percentage of negative emotions. The learning plan generation unit also accumulates emotional reaction data to the learning plan and evaluates its long-term effectiveness. For example, it generates an optimal learning plan based on past data. This allows the child's emotional reactions to be monitored and the effectiveness of the learning plan to be continuously evaluated and improved, thereby improving learning effectiveness.

[0072] The dialogue learning unit can use the emotion estimation function to analyze the child's emotions during a dialogue and provide appropriate feedback according to the emotions. The dialogue learning unit, for example, analyzes the child's emotions during a dialogue in real time and provides feedback according to the emotions. For example, if the child is in trouble, it will offer words of encouragement. The dialogue learning unit also uses the emotion estimation function to provide feedback according to the child's emotions. For example, if the child is happy, it will offer words of praise. The dialogue learning unit also accumulates data on the child's emotions and learns feedback patterns according to emotions. For example, it provides optimal feedback based on past data. In this way, by providing feedback according to the child's emotions, it is possible to improve learning effectiveness.

[0073] The dialogue learning unit can analyze a child's pronunciation or language skills and provide support for language learning. For example, the dialogue learning unit analyzes a child's pronunciation and provides feedback to help the child learn correct pronunciation. For example, it points out pronunciation errors and teaches correct pronunciation. The dialogue learning unit also analyzes a child's language skills and provides learning materials at an appropriate level. For example, it provides learning materials according to the child's vocabulary and level of grammar understanding. The dialogue learning unit also accumulates data on a child's pronunciation and language skills and evaluates long-term learning effectiveness. For example, it adjusts a learning plan based on past data. In this way, the effectiveness of language learning can be improved by analyzing a child's pronunciation and language skills and providing appropriate support.

[0074] The dialogue learning unit can record the content of the dialogue between the child and the generation AI so that it can be reviewed later. For example, the dialogue learning unit automatically records the content of the dialogue between the child and the generation AI so that it can be reviewed later. For example, it saves a log of the dialogue so that the child can access it at any time. The dialogue learning unit also saves the content of the dialogue as text data so that the child can search for it when reviewing it. For example, it can display related content of the dialogue by entering a specific keyword. The dialogue learning unit also saves the content of the dialogue as audio data so that it can be played back when the child reviews it. For example, it can save an audio file of the dialogue so that the child can listen to it again. In this way, the learning effect can be improved by recording the content of the dialogue so that it can be reviewed later.

[0075] The dialogue learning unit can provide a group dialogue function in which multiple children can participate simultaneously. The dialogue learning unit provides, for example, a group dialogue function in which the generation AI can dialogue with multiple children simultaneously. For example, it sets up an dialogue session in which multiple children can participate, like an online class. The dialogue learning unit also uses the group dialogue function to provide an environment in which children can learn cooperatively. For example, it sets up group discussions and joint assignments. The dialogue learning unit also improves children's communication skills through the group dialogue function. For example, it provides opportunities for children to hear the opinions of other children. As a result, providing a group dialogue function in which multiple children can participate simultaneously can promote cooperative learning and the improvement of communication skills.

[0076] The dialogue learning unit can analyze the content of a child's dialogue and report learning progress or challenges to parents or teachers. In the dialogue learning unit, for example, the generation AI analyzes the content of a child's dialogue and reports learning progress and challenges to parents or teachers. For example, it reports the learning situation in the form of a weekly report. The dialogue learning unit also evaluates the child's level of understanding and learning progress based on the content of the dialogue and provides feedback to parents and teachers. For example, it points out areas where understanding is low. In addition, the dialogue learning unit provides a dialogue log to parents and teachers so that they can check the content of the dialogue. For example, the dialogue log can be made available for viewing on an online platform. This makes it possible to analyze the content of the dialogue and report learning progress and challenges to parents and teachers, thereby strengthening learning support.

[0077] The dialogue learning unit can use the emotion estimation function to detect in real time the excitement or interest that a child feels during a dialogue and adjust the dialogue content accordingly. For example, the dialogue learning unit analyzes the child's emotions during a dialogue in real time to detect excitement or interest. For example, if the child is excited, the dialogue learning unit delves deeper into the topic. The dialogue learning unit also uses the emotion estimation function to provide dialogue content that will attract the child's interest. For example, it asks questions related to a topic that interests the child. The dialogue learning unit also continuously adjusts the dialogue content based on the child's emotion data. For example, if interest is waning, it provides a new topic. In this way, by detecting the child's excitement or interest in real time and adjusting the dialogue content, it is possible to improve learning effectiveness.

[0078] The interactive learning unit can analyze a child's emotions and provide learning materials that elicit positive emotions when providing learning support in a specialized field. For example, the interactive learning unit analyzes a child's emotions in real time while learning a specialized field and provides learning materials that elicit positive emotions. For example, it provides interactive learning materials that allow children to learn while having fun. The interactive learning unit also uses an emotion estimation function to identify learning materials that evoke positive emotions in children and adjusts the learning plan based on that. For example, it provides learning materials related to topics that interest the child. The interactive learning unit also accumulates emotional data during learning and continuously improves the optimal learning materials for eliciting positive emotions. For example, it updates the learning materials based on past data. This makes it possible to increase motivation to learn by providing learning materials that elicit positive emotions in children when providing learning support in a specialized field.

[0079] The dialogue learning unit can evaluate a child's level of comprehension in real time when providing learning support in a specialized field, and provide learning materials at an appropriate level of difficulty. The dialogue learning unit, for example, evaluates a child's level of comprehension in real time while learning a specialized field, and provides learning materials at an appropriate level of difficulty. For example, if the level of comprehension is high, the difficulty level is increased. The dialogue learning unit also provides learning materials at an optimal level of difficulty for the child based on the level of comprehension. For example, if the level of comprehension is low, learning materials that carefully teach the child from the basics are provided. The dialogue learning unit also accumulates comprehension data and evaluates long-term learning effectiveness. For example, the learning plan is adjusted based on past data. As a result, when providing learning support in a specialized field, learning effectiveness can be improved by providing learning materials at an appropriate level of difficulty according to the child's level of comprehension.

[0080] When providing learning support in specialized fields, the interactive learning unit can suggest future career paths based on the child's learning history. The interactive learning unit, for example, analyzes the child's learning history and suggests future career paths. For example, an IT-related career is suggested for a child who is interested in programming. The interactive learning unit also suggests career paths that are suitable for the child based on their learning history. For example, a career as a researcher or engineer is suggested for a child who is interested in science. The interactive learning unit also accumulates learning history data and suggests long-term career paths. For example, it suggests the optimal career path based on past data. In this way, when providing learning support in specialized fields, it is possible to clarify the child's future direction by suggesting future career paths based on the child's learning history.

[0081] The interactive learning unit can utilize online communities to promote interaction with other children and experts when providing learning support in specialized fields. The interactive learning unit, for example, utilizes online communities to promote interaction with other children and experts when providing learning support in specialized fields. For example, it provides online forums and chat rooms. The interactive learning unit also provides an environment through the online community where children can cooperate and learn. For example, it sets up joint projects and discussions. The interactive learning unit also increases children's motivation to learn through interaction with experts. For example, it provides online lectures and Q&A sessions by experts. In this way, the interactive learning unit can utilize online communities to promote interaction with other children and experts when providing learning support in specialized fields, thereby increasing motivation to learn.

[0082] The interactive learning unit can help students acquire practical skills through actual projects or assignments when providing learning support in specialized fields. For example, the interactive learning unit can help students acquire practical skills through actual projects or assignments when providing learning support in specialized fields. For example, the interactive learning unit can set up programming projects or scientific experiments. The interactive learning unit can also improve children's skills through practical assignments. For example, the interactive learning unit can provide history research projects or art production assignments. The interactive learning unit can also increase children's motivation to learn through actual projects. For example, the interactive learning unit can set up projects that address social issues or assignments to create a business plan. In this way, the learning effect can be improved by helping students acquire practical skills through actual projects or assignments when providing learning support in specialized fields.

[0083] The dialogue learning unit can use the emotion estimation function to detect in real time the excitement or interest a child feels while learning a specialized field and adjust the learning content. The dialogue learning unit, for example, analyzes the child's emotions while learning a specialized field in real time to detect excitement or interest. For example, if the child is excited, the dialogue learning unit delves deeper into the topic. The dialogue learning unit also uses the emotion estimation function to provide learning content that will interest the child. For example, it provides learning materials related to a topic that interests the child. The dialogue learning unit also continuously adjusts the learning content based on the child's emotion data. For example, if interest is waning, it provides a new topic. In this way, in learning support for specialized fields, the learning effect can be improved by detecting the child's excitement or interest in real time and adjusting the learning content.

[0084] The interactive learning unit can analyze a child's emotions when managing learning progress and providing feedback, and provide feedback that elicits positive emotions. The interactive learning unit, for example, analyzes a child's emotions regarding their learning progress in real time and provides feedback that elicits positive emotions. For example, it may give the child words of praise that make them feel a sense of accomplishment. The interactive learning unit also uses an emotion estimation function to provide feedback that corresponds to the child's emotions. For example, it may offer words of encouragement if the child is in trouble. The interactive learning unit also accumulates data on the child's emotions and learns feedback patterns that correspond to emotions. For example, it provides optimal feedback based on past data. In this way, when managing learning progress and providing feedback, it is possible to analyze a child's emotions and provide feedback that elicits positive emotions, thereby increasing motivation to learn.

[0085] When managing learning progress, the interactive learning unit can set individual learning goals based on the child's learning data and evaluate the degree of achievement of those goals. The interactive learning unit, for example, analyzes the child's learning data and sets individual learning goals. For example, it sets a goal to be achieved in one month and evaluates the degree of achievement of that goal. The interactive learning unit also sets optimal learning goals for the child based on the learning data. For example, it adjusts the goals according to the child's level of understanding and progress. The interactive learning unit also evaluates the degree of achievement of the learning goals and adjusts the learning plan as necessary. For example, if the goal is not achieved, it provides supplementary lessons or additional assignments. In this way, when managing learning progress, it is possible to improve learning effectiveness by setting individual learning goals based on the child's learning data and evaluating the degree of achievement of those goals.

[0086] The interactive learning unit can visualize a child's learning data when managing learning progress and provide parents or teachers with reports that are easy to understand intuitively. For example, the interactive learning unit visualizes a child's learning data in graphs or charts and provides them to parents or teachers. For example, this allows them to grasp learning progress and level of understanding at a glance. The interactive learning unit also generates reports that are easy for parents and teachers to understand intuitively based on the learning data. For example, it provides weekly or monthly reports. The interactive learning unit also makes it easier for parents and teachers to understand a child's learning situation based on the visualized data. For example, it clarifies areas where learning needs reinforcement. As a result, when managing learning progress, learning support can be enhanced by visualizing a child's learning data and providing parents and teachers with reports that are easy to understand intuitively.

[0087] The interactive learning unit can strengthen collaboration with different learning platforms or teaching materials when managing learning progress, thereby realizing integrated learning management. The interactive learning unit, for example, strengthens collaboration with different learning platforms and teaching materials to realize integrated learning management. For example, it collaborates with online teaching materials and apps to centrally manage learning data. The interactive learning unit also integrates data from learning platforms and teaching materials to comprehensively evaluate a child's learning progress. For example, it compiles progress data from multiple teaching materials into a single report. The interactive learning unit also makes a child's learning experience seamless through collaboration with different platforms and teaching materials. For example, it synchronizes learning data between different devices and apps. This strengthens collaboration with different learning platforms and teaching materials when managing learning progress, thereby realizing integrated learning management and improving learning effectiveness.

[0088] When managing learning progress, the interactive learning unit can evaluate the effectiveness of learning based on the child's learning data and suggest the most appropriate learning method. The interactive learning unit, for example, analyzes the child's learning data and evaluates the effectiveness of learning. For example, it evaluates the effectiveness of the learning method based on the level of understanding and progress. The interactive learning unit also suggests the most appropriate learning method for the child based on the learning data. For example, it provides learning materials that make extensive use of diagrams and graphs for visually-inclined children. The interactive learning unit also continuously evaluates the effectiveness of learning and suggests the most appropriate learning method. For example, it adjusts the learning plan based on past data. In this way, when managing learning progress, it is possible to improve learning effectiveness by evaluating the effectiveness of learning based on the child's learning data and suggesting the most appropriate learning method.

[0089] The dialogue learning unit can use the emotion estimation function to monitor a child's emotional reactions to their learning progress and adjust the content of their feedback. For example, the dialogue learning unit monitors a child's emotional reactions to their learning progress in real time and adjusts the content of their feedback based on that data. For example, if there are a lot of positive emotions, it increases the amount of praise. The dialogue learning unit also uses the emotion estimation function to provide feedback according to the child's emotions. For example, if a child is in trouble, it offers words of encouragement. The dialogue learning unit also accumulates data on the child's emotions and learns feedback patterns according to emotions. For example, it provides optimal feedback based on past data. In this way, by monitoring a child's emotional reactions to their learning progress and adjusting the content of their feedback, it is possible to improve learning effectiveness.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The education provision system can also monitor a child's physical health and reflect this in the learning plan. For example, it can monitor a child's sleep time and exercise amount and set appropriate break times. It can also adjust the learning load according to the child's health condition. For example, if the child is not feeling well, it can provide lighter tasks. This makes it possible to provide a learning plan that takes the child's health condition into consideration.

[0092] The education provision system can also provide creative tasks to bring out children's creativity. For example, tasks such as drawing a picture, writing a story, or composing music can be set. Creative tasks can also enhance children's ability to express themselves. For example, presentations or exhibitions can be held. This makes it possible to provide a learning plan that fosters children's creativity.

[0093] The education delivery system can also provide collaborative learning opportunities to foster children's social skills, such as setting up group projects and discussions. It can also help children improve their communication skills through interactions with other children, such as by providing online forums and chat rooms. This can help provide a learning plan that fosters children's social skills.

[0094] The education provision system can further provide a self-assessment function to enhance children's self-management abilities. For example, a function for self-assessing learning progress and achievement level can be added. The system can also adjust the learning plan based on the self-assessment. For example, the difficulty of the assignments can be adjusted according to the results of the self-assessment. This makes it possible to provide a learning plan that enhances children's self-management abilities.

[0095] The education provision system can also introduce a reward system to further motivate children to learn. For example, points are awarded for each task completed, and children can receive rewards when they collect a certain number of points. The reward system can also help maintain children's motivation. For example, a ranking system can be introduced to add an element of competition with other children. This can further increase children's motivation to learn.

[0096] The education provision system can also analyze a child's emotions and provide a learning environment that suits the child's emotions. For example, if a child is feeling stressed, it can play relaxing music. It can also adjust the progress of learning according to the child's emotions. For example, if a child is tired, it can suggest taking a break. In this way, it is possible to provide a learning environment that suits the child's emotions.

[0097] The education provision system can also analyze a child's emotions and provide feedback according to their emotions. For example, if a child is in trouble, it can offer words of encouragement. It can also adjust the content of the feedback according to their emotions. For example, if a child is happy, it can provide more praise. In this way, it can provide feedback according to the child's emotions.

[0098] The education provision system can further analyze a child's emotions and provide a learning plan that corresponds to the child's emotions. For example, it can provide tasks related to topics that interest the child. It can also adjust the learning progress according to the child's emotions. For example, it can provide game-style learning materials that allow the child to learn in a fun way. In this way, it can provide a learning plan that corresponds to the child's emotions.

[0099] The education provision system can further analyze the child's emotions and set learning goals according to the emotions. For example, it can set goals that will make the child feel positive. It can also evaluate the degree of goal achievement according to the emotions. For example, it can set goals that will make the child feel a sense of accomplishment. In this way, it is possible to set learning goals according to the child's emotions.

[0100] The education provision system can further analyze the child's emotions and manage the learning progress according to the emotions. For example, it can prioritize the evaluation of progress that makes the child feel positive. It can also adjust the progress management method according to the emotions. For example, it can ease the evaluation of progress if the child is in difficulty. In this way, it is possible to manage the learning progress according to the child's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The personality, characteristics, and interest analysis unit analyzes the child's personality, characteristics, and interests. For example, the generation AI performs analysis based on the child's dialogue with the generation AI, past learning history, and survey results. The generation AI grasps the child's interests through questions such as, "What is your favorite subject?" and "What is your dream for the future?" Step 2: The learning plan generator generates an optimal individual learning plan for each child based on the results of the analysis by the personality, characteristics, and interest analysis unit. For example, the generator AI provides more advanced math problems to children who are good at math, and creates a plan to carefully teach weaker subjects from the basics. It can also increase motivation to learn by providing learning materials and assignments related to areas of interest. Step 3: The interactive learning unit engages the child in dialogue based on the learning plan generated by the learning plan generation unit. For example, the AI ​​can check the child's level of understanding by asking questions such as, "If you don't know how to solve this problem, which part is difficult?" and provide appropriate advice.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Using generative AI, A personality, traits, and interest analysis section that analyzes a child's personality, traits, and interests; a learning plan generation unit that generates an optimal individual learning plan for a child based on the results of the analysis by the personality, characteristics, and interest analysis unit; an interactive learning unit that advances learning through dialogue with the child based on the learning plan generated by the learning plan generation unit; A system characterized by:

2. The personality, characteristics, and interest analysis unit Analyzing the child's emotions based on the conversation data of the child, and re-evaluating the personality and characteristics in response to changes in the emotions.

2. The system of claim 1.

3. The personality, characteristics, and interest analysis unit Analyzing the child's behavioral patterns or learning history to identify the child's learning style 2. The system of claim 1.

4. The personality, characteristics, and interest analysis unit Collect data on the child's social media or online activities and analyze the child's interests 2. The system of claim 1.

5. The personality, characteristics, and interest analysis unit The results of the analysis of the child's personality, characteristics, and interests will be shared with parents or teachers, and used to provide support at home and at school.

2. The system of claim 1.

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