System

Abstract

An object of a system according to an embodiment is to analyze handwriting of a child in real time and provide appropriate feedback.SOLUTION: A system includes a handwriting analysis unit, a feedback unit, a point management unit, and a parent-child communication unit. The handwriting analysis unit analyzes the handwriting of the child using a smartphone camera. The feedback unit provides feedback in real time based on the handwriting analyzed by the handwriting analysis unit. The point management unit gives a point when a character is written in a correct stroke order or shape. The parent-child communication unit notifies the parent of the learning status of the child.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 has the problem of making it difficult to analyze a child's handwriting in real time and provide appropriate feedback.

[0005] The system according to the embodiment aims to analyze a child's handwriting in real time and provide appropriate feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a handwriting analysis unit, a feedback unit, a points management unit, and a parent-child communication unit. The handwriting analysis unit analyzes a child's handwriting using a smartphone camera. The feedback unit provides real-time feedback based on the handwriting analyzed by the handwriting analysis unit. The points management unit awards points when characters are written in the correct stroke order and shape. The parent-child communication unit notifies parents of their child's learning progress. [Effects of the Invention]

[0007] The system according to the embodiment can analyze a child's handwriting in real time and provide appropriate feedback. [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) An interactive learning tool according to an embodiment of the present invention is a system for fostering beautiful handwriting in children. This system utilizes AI technology to analyze a child's handwriting using a smartphone camera and provides real-time feedback on whether the stroke order and shape are correct. This allows the interactive learning tool to analyze a child's handwriting in real time and provide feedback, thereby increasing their motivation to learn.

[0029] An interactive learning tool according to an embodiment includes a handwriting analysis unit, a feedback unit, a points management unit, and a parent-child communication unit. The handwriting analysis unit analyzes a child's handwriting using a smartphone camera. For example, it analyzes a handwriting image captured by the smartphone camera and determines the stroke order and shape. The feedback unit provides real-time feedback based on the handwriting analyzed by the handwriting analysis unit. For example, it determines whether the stroke order is correct and displays feedback such as "The stroke order is correct" or "Please write with a more rounded stroke." The points management unit awards points when characters are written in the correct order and shape. For example, a new character is unlocked when 100 points are accumulated. The parent-child communication unit notifies parents of their child's learning progress. For example, parents can check their child's progress and accumulated points through a parent app. This allows the interactive learning tool to analyze a child's handwriting in real time and provide feedback, thereby increasing their motivation to learn.

[0030] The feedback unit can analyze the subtle movements and pressure of handwriting and provide detailed feedback. For example, using a generation AI, the feedback unit can analyze the subtle movements and pressure of handwriting and evaluate not only the stroke order and shape, but also the strength and speed of pressure. For example, if the pressure is too strong, the feedback unit can provide feedback such as, "Write a little more lightly." The feedback unit can also analyze the subtle movements of handwriting and provide feedback on the balance and placement of characters. For example, it can provide specific advice such as, "The center of the character is off. Try writing a little closer to the center." The feedback unit can also provide feedback on the child's hand movements and posture when writing based on the results of the pressure analysis. For example, it can provide advice such as, "If you write with your wrist a little more stable, your characters will be more stable." This allows the system to provide more detailed feedback by analyzing the subtle movements and pressure of handwriting.

[0031] The feedback unit can be equipped with a personalized feedback function that proposes the optimal practice method for each individual child based on the handwriting analysis results. For example, based on the handwriting analysis results, the feedback unit identifies the weaknesses and strengths of the child and proposes a corresponding practice method. For example, it provides specific advice such as "You have difficulty with the hiragana 'a', so practice it specifically." Also, based on the handwriting analysis results of the child, the feedback unit automatically generates a practice plan according to the individual progress. For example, it makes a proposal such as "As the next step, let's start practicing katakana." In addition, based on the handwriting analysis results, the feedback unit proposes a practice method that suits the child's learning style. For example, it provides advice such as "Visual feedback is effective, so practice while watching a video." By proposing the optimal practice method for each individual child in this way, the learning effect can be enhanced.

[0032] The handwriting analysis unit can also support other languages and scripts. For example, the handwriting analysis unit expands the handwriting analysis function so that it can analyze not only hiragana and katakana but also the stroke order and shape of Chinese characters. For example, it provides feedback such as "The stroke order of the Chinese character'mu' is correct." Also, the handwriting analysis unit makes the handwriting analysis function compatible with other languages so that it can analyze the writing of English alphabets and calligraphy. For example, it provides feedback such as "The shape of the alphabet 'A' is correct." In addition, the handwriting analysis unit expands the handwriting analysis function so that it can analyze characters in different scripts (for example, Gothic or Mincho). For example, it provides feedback such as "The shape of the Gothic 'a' is correct." By supporting multiple languages and scripts in this way, it can meet a wide range of learning needs.

[0033] The feedback unit can provide the handwriting analysis results as audio feedback to support learning both visually and audibly. For example, the feedback unit provides the handwriting analysis results as audio feedback to support learning both visually and audibly. For example, it plays an audio message such as, "The stroke order is correct." The feedback unit also customizes the audio feedback based on the handwriting analysis results to provide it in a way that is easy for the child to understand. For example, it provides specific audio advice such as, "Try to write with a more rounded edge." The feedback unit also uses audio feedback to provide the child with points to note and areas for improvement when writing in real time. For example, it provides audio advice such as, "If you write with your wrist a little more stable, your letters will be more stable." This makes it possible to improve learning by supporting learning both visually and audibly.

[0034] The point management unit introduces an algorithm that evaluates a child's learning progress and effort, enabling more fair point awarding. The point management unit, for example, introduces an algorithm that evaluates a child's learning progress and effort to make the point system fairer. For example, points may be awarded based on the amount of time spent practicing each day and the level of achievement. The point management unit also develops an algorithm that awards points based on the results of a child's handwriting analysis to evaluate learning progress and effort. For example, points may be awarded if the child writes in the correct order and shape. The point management unit also introduces a point system that takes into account parental feedback to evaluate a child's learning progress and effort. For example, additional points may be awarded if a parent evaluates their child's effort. In this way, introducing an algorithm that evaluates learning progress and effort makes it possible to award points more fairly.

[0035] The point management department can introduce a system where points can be accumulated and exchanged for stationery and calligraphy tools. For example, the point management department introduces a system where points can be accumulated and exchanged for actual stationery and calligraphy tools. For example, 100 points can be exchanged for new pencils or erasers. The point management department can also introduce a system where points can be accumulated and exchanged for specific calligraphy tools or stationery sets. For example, 500 points can be exchanged for a high-quality calligraphy set. The point management department can also introduce a system where points can be accumulated and exchanged for limited edition stationery and calligraphy tools. For example, 1,000 points can be exchanged for a specially designed notebook or pen. By introducing a system where points can be accumulated and exchanged for actual stationery and calligraphy tools, it is possible to increase children's motivation to learn.

[0036] The point management unit can link the point system with other learning apps and educational services, enabling the mutual use of points. The point management unit, for example, links the point system with other learning apps and educational services, enabling the mutual use of points. For example, points accumulated in an English learning app can be used in this tool. The point management unit also links with other educational services, introducing a mechanism for mutual use of points. For example, points earned for online classes can be used in this tool. The point management unit also links the point system with other learning apps, enabling the mutual exchange of points. For example, points accumulated in a mathematics learning app can be used in this tool. In this way, by linking the point system with other learning apps and educational services, mutual use of points becomes possible, thereby increasing motivation to learn.

[0037] The point management unit can introduce a system whereby points can be accumulated to obtain the right to participate in events and workshops that parents and children can participate in together. The point management unit introduces a system whereby points can be accumulated to obtain the right to participate in events and workshops that parents and children can participate in together, for example. For example, 1,000 points can obtain the right to participate in a calligraphy class. The point management unit also introduces a system whereby points can be accumulated to obtain the right to participate in special events that parents and children can enjoy together. For example, 2,000 points can obtain the right to participate in a beautiful handwriting contest that parents and children can participate in together. The point management unit also introduces a system where points can be accumulated to obtain the right to participate in workshops that parents and children can learn together. For example, 1,500 points can obtain the right to participate in a handwriting analysis workshop that parents and children can participate in together. By introducing a system where points can be accumulated to obtain the right to participate in events and workshops that parents and children can participate in together, it is possible to deepen communication between parents and children.

[0038] The parent-child communication unit can add a dashboard function that allows parents to monitor their child's learning status in real time. The parent-child communication unit, for example, adds a dashboard function that allows parents to monitor their child's learning status in real time. For example, the child's learning progress and point acquisition status are displayed in graphs. The parent-child communication unit also uses the dashboard function to allow parents to check their child's learning status in detail. For example, the percentage of correct answers for each practice question and the study time are displayed. The parent-child communication unit also adds a notification function to the dashboard so that parents can monitor their child's learning status in real time. For example, a notification is sent when a child acquires new points. In this way, adding a dashboard function that allows parents to monitor their child's learning status in real time can deepen parent-child communication.

[0039] The parent-child communication section can introduce a system in which parents and children can set joint tasks and missions that they can work on together and share a sense of accomplishment. For example, the parent-child communication section introduces a system in which parents and children can work on joint tasks and missions that they can work on together and share a sense of accomplishment. For example, a parent and child can practice calligraphy together and earn points together. The parent-child communication section also introduces a system in which parent-child communication deepens communication through joint tasks and missions. For example, a mission in which parents and children work together to write specific characters can be set. The parent-child communication section also introduces a system in which special rewards can be earned by completing a mission that parents and children can work on together. For example, a parent and child can practice calligraphy together and challenge a special stage. In this way, parent-child communication can be deepened by setting joint tasks and missions that parents and children can work on together and introducing a system in which a sense of accomplishment can be shared.

[0040] The parent-child communication unit can add a function that enables parents to send customized messages of encouragement according to their child's learning progress. For example, the parent-child communication unit adds a function that enables parents to send customized messages of encouragement according to their child's learning progress. For example, sending a message such as "You're doing great! Keep it up!" The parent-child communication unit also adds a function that enables parents to send specific advice and messages of encouragement to their child according to their learning progress. For example, sending a message such as "You're almost at your goal! Let's do our best!" The parent-child communication unit also provides template messages so that parents can send customized messages of encouragement according to their child's learning progress. For example, sending a message such as "You're making great progress!" This allows parents to deepen communication between their children by sending customized messages of encouragement according to their child's learning progress.

[0041] The parent-child communication section can introduce a periodic report function that allows parents and children to review their learning outcomes together. For example, the parent-child communication section introduces a periodic report function that allows parents and children to review their learning outcomes together. For example, it provides a report summarizing weekly learning outcomes. The parent-child communication section also uses the periodic report function to allow parents and children to review their learning outcomes together and set the next goal. For example, it makes a suggestion such as, "Your results this week are excellent. Let's aim for this goal next time." The parent-child communication section also introduces a report function that allows parents and children to review their learning outcomes together, allowing parents to evaluate their children's efforts. For example, it provides a message such as, "Let's praise your efforts this week." Thus, by introducing a periodic report function that allows parents and children to review their learning outcomes together, parent-child communication can be deepened.

[0042] The existing teaching material linking unit can add a function to analyze the content of existing teaching materials and suggest supplementary teaching materials according to a child's level of understanding. The existing teaching material linking unit, for example, adds a function to analyze the content of existing teaching materials and suggest supplementary teaching materials according to a child's level of understanding. For example, supplementary teaching materials are provided for parts with low levels of understanding. The existing teaching material linking unit also analyzes the content of existing teaching materials and suggests supplementary teaching materials according to a child's learning progress. For example, supplementary teaching materials are provided for parts where progress is lagging. The existing teaching material linking unit also analyzes the content of existing teaching materials and suggests supplementary teaching materials that suit a child's learning style. For example, if visual teaching materials are effective, supplemental teaching materials that make extensive use of diagrams and illustrations are provided. In this way, learning effectiveness can be improved by analyzing the content of existing teaching materials and suggesting supplementary teaching materials according to a child's level of understanding.

[0043] The existing teaching material linking unit can be linked with existing teaching materials and introduce a function that allows home learning progress to be shared with school teachers. The existing teaching material linking unit, for example, can be linked with existing teaching materials and introduce a function that allows home learning progress to be shared with school teachers. For example, it allows teachers to check learning results at home. Furthermore, by sharing home learning progress with school teachers, the existing teaching material linking unit can ensure consistency between school and home learning. For example, teachers can understand the content of home learning and reflect it in lessons. Furthermore, the existing teaching material linking unit can be linked with existing teaching materials and introduce a function that allows home learning progress to be shared with school teachers in real time. For example, it allows teachers to check learning results at home in real time. In this way, by introducing a function that allows home learning progress to be shared with school teachers, it is possible to increase the consistency between home learning and school learning.

[0044] The existing teaching material linking unit can digitize the content of existing teaching materials and provide them as interactive practice questions. For example, the existing teaching material linking unit digitizes the content of existing teaching materials and provides them as interactive practice questions. For example, it can provide digital quizzes based on the content of textbooks. The existing teaching material linking unit also enables children to learn interactively using digitized teaching materials. For example, they can solve practice questions using a tablet. The existing teaching material linking unit also digitizes the content of existing teaching materials and enables children to learn at their own pace. For example, they can review at home using the digital teaching materials. In this way, by digitizing the content of existing teaching materials and providing them as interactive practice questions, it is possible to improve learning effectiveness.

[0045] The learning environment optimization unit can add a function to automatically generate an optimal learning plan for each child according to their learning progress. The learning environment optimization unit adds a function to automatically generate an optimal learning plan for each child according to their learning progress, for example. For example, it provides a practice plan that focuses on areas where progress is lagging. The learning environment optimization unit also analyzes the child's learning progress and automatically generates a learning plan tailored to individual needs. For example, it provides a plan to further develop areas of strength. The learning environment optimization unit also automatically generates an optimal learning plan for each child according to their learning progress and allows parents to check the plan. For example, it allows parents to check and support their child's learning plan. In this way, the learning effect can be improved by automatically generating an optimal learning plan for each child according to their learning progress.

[0046] The learning environment optimization unit can not only visually confirm learning outcomes, but also provide audio and video feedback. For example, the learning environment optimization unit can not only visually confirm learning outcomes, but also provide audio and video feedback. For example, it can display learning outcomes in a graph and provide audio feedback. The learning environment optimization unit can also provide video feedback to enable visual confirmation of learning outcomes. For example, it can explain learning progress in a video and suggest next steps. The learning environment optimization unit can not only visually confirm learning outcomes, but also use audio feedback to make it easier for children to understand. For example, it can play an audio message such as, "You're making great progress!" This makes it possible to not only visually confirm learning outcomes, but also provide audio and video feedback, thereby improving learning effectiveness.

[0047] The learning environment optimization unit can add a function to provide advice for optimizing the learning environment at home according to the progress of learning. The learning environment optimization unit adds a function to provide advice for optimizing the learning environment at home according to the progress of learning. For example, the learning environment optimization unit provides advice such as, "Rearranging your desk will make it easier to concentrate." The learning environment optimization unit also analyzes the child's learning progress and provides specific advice for optimizing the learning environment at home. For example, the learning environment optimization unit provides advice such as, "It is effective to set a fixed study time." The learning environment optimization unit also provides advice for optimizing the learning environment at home according to the progress of learning, and enables parents to implement the advice. For example, the learning environment optimization unit provides advice such as, "Keeping your study space tidy will make it easier to concentrate." In this way, by providing advice for optimizing the learning environment at home according to the progress of learning, it is possible to improve learning effectiveness.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The interactive learning tool can further include a speech recognition unit. The speech recognition unit analyzes the sounds made by a child while writing and evaluates the accuracy and rhythm of pronunciation. For example, when a child writes "hiragana," it determines whether the pronunciation is correct and provides feedback such as "Your pronunciation is correct" or "Please pronounce it a little more slowly." The speech recognition unit also analyzes the sounds made by a child while writing and provides advice on stroke order and shape by voice. For example, it provides specific instructions by voice such as "Next, draw this line." In this way, speech recognition can be used to support learning both visually and aurally.

[0050] The interactive learning tool can further include a gamification section. The gamification section allows children to continue learning while having fun by progressing through their learning in a game format. For example, a game is provided in which a character grows when characters are written in the correct order and shape. The gamification section also provides level-ups and bonus stages according to the progress of learning. For example, a new stage is unlocked when a certain number of points are accumulated. The gamification section also provides a multiplayer mode that parents and children can enjoy together. For example, parents and children can work together to complete a specific mission and earn special rewards. In this way, incorporating game elements can increase children's motivation to learn.

[0051] The interactive learning tool can further include a virtual reality (VR) section. The VR section provides children with the experience of writing characters in a virtual space. For example, by wearing VR goggles, they can practice writing characters in a virtual calligraphy classroom. The VR section also provides real-time feedback on the results of handwriting analysis in the virtual space. For example, it displays a message in the virtual space saying, "Your stroke order is correct." The VR section also allows children to learn while having fun through learning in the virtual space. For example, they can earn rewards by completing a mission to write characters together with a virtual character. In this way, VR technology can be used to provide an environment where children can learn while having fun.

[0052] The interactive learning tool may further include a social collaboration unit. The social collaboration unit provides a function that allows children to share their learning results with other users and compete with each other. For example, they can post their learning results on a social networking site and share them with their friends. The social collaboration unit also displays the learning results in a ranking format, allowing them to compete with other users. For example, if they rank high in the weekly rankings, they can earn special rewards. The social collaboration unit also provides a function that allows parents and children to share their learning results together and deepen communication. For example, parents and children can post their learning results together on a social networking site and interact with other families. This can increase children's motivation to learn through social collaboration.

[0053] The interactive learning tool can further include a customization unit. The customization unit provides a function for customizing learning content and feedback according to a child's learning style and preferences. For example, the learning content can be customized based on a child's favorite character or theme. The customization unit also customizes feedback according to a child's learning progress and strengths. For example, it provides specific advice such as, "You're good at the hiragana character 'a,' so next you should practice 'i'." The customization unit also customizes the format of feedback to suit a child's learning style. For example, if visual feedback is effective, it can provide feedback that makes extensive use of diagrams and illustrations. In this way, learning effectiveness can be improved by customizing learning content and feedback according to a child's learning style and preferences.

[0054] The interactive learning tool can further include a reward section. The reward section provides a function that provides actual rewards according to children's learning achievements. For example, once a certain number of points have been accumulated, they can be exchanged for stationery or calligraphy tools. The reward section also provides the right to participate in special events or workshops according to learning achievements. For example, with 1,000 points, you can earn the right to participate in a calligraphy class. The reward section also provides special rewards that parents and children can enjoy together according to learning achievements. For example, you can earn the right to participate in a beautiful handwriting contest that parents and children can participate in together. This can increase children's motivation to learn by providing actual rewards according to learning achievements.

[0055] The interactive learning tool can further include an analytics section. The analytics section analyzes a child's learning data and provides a function for understanding learning trends and patterns. For example, it identifies which characters a child is good at and which characters they are weak at based on the child's learning data. The analytics section also analyzes the learning data and visualizes the child's learning progress. For example, it displays the learning progress using graphs and charts. The analytics section also analyzes the learning data and makes suggestions to maximize the child's learning effectiveness. For example, it provides advice such as, "It would be more effective if you continued practicing this character a little longer." In this way, it is possible to analyze the learning data and understand learning trends and patterns, thereby improving learning effectiveness.

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

[0057] Step 1: The handwriting analysis unit analyzes the child's handwriting using a smartphone camera. For example, it analyzes handwriting images captured by the smartphone camera and determines the stroke order and shape. Step 2: The feedback unit provides real-time feedback based on the handwriting analyzed by the handwriting analysis unit. For example, it determines whether the stroke order is correct and displays feedback such as "The stroke order is correct" or "Please write with a more rounded stroke." Step 3: The point management unit awards points when characters are written in the correct order and shape. For example, if you collect 100 points, a new character will be unlocked. Step 4: The Parent-Child Communication Department notifies parents of their child's learning status. For example, parents can check their child's progress and accumulated points through an app.

[0058] (Example 2) An interactive learning tool according to an embodiment of the present invention is a system for fostering beautiful handwriting in children. This system utilizes AI technology to analyze a child's handwriting using a smartphone camera and provides real-time feedback on whether the stroke order and shape are correct. This allows the interactive learning tool to analyze a child's handwriting in real time and provide feedback, thereby increasing their motivation to learn.

[0059] An interactive learning tool according to an embodiment includes a handwriting analysis unit, a feedback unit, a points management unit, and a parent-child communication unit. The handwriting analysis unit analyzes a child's handwriting using a smartphone camera. For example, it analyzes a handwriting image captured by the smartphone camera and determines the stroke order and shape. The feedback unit provides real-time feedback based on the handwriting analyzed by the handwriting analysis unit. For example, it determines whether the stroke order is correct and displays feedback such as "The stroke order is correct" or "Please write with a more rounded stroke." The points management unit awards points when characters are written in the correct order and shape. For example, a new character is unlocked when 100 points are accumulated. The parent-child communication unit notifies parents of their child's learning progress. For example, parents can check their child's progress and accumulated points through a parent app. This allows the interactive learning tool to analyze a child's handwriting in real time and provide feedback, thereby increasing their motivation to learn.

[0060] The feedback unit can analyze the subtle movements and pressure of handwriting and provide detailed feedback. For example, using a generation AI, the feedback unit can analyze the subtle movements and pressure of handwriting and evaluate not only the stroke order and shape, but also the strength and speed of pressure. For example, if the pressure is too strong, the feedback unit can provide feedback such as, "Write a little more lightly." The feedback unit can also analyze the subtle movements of handwriting and provide feedback on the balance and placement of characters. For example, it can provide specific advice such as, "The center of the character is off. Try writing a little closer to the center." The feedback unit can also provide feedback on the child's hand movements and posture when writing based on the results of the pressure analysis. For example, it can provide advice such as, "If you write with your wrist a little more stable, your characters will be more stable." This allows the system to provide more detailed feedback by analyzing the subtle movements and pressure of handwriting.

[0061] The feedback unit can be equipped with a personalized feedback function that suggests the optimal practice method for each child based on the handwriting analysis results. For example, the feedback unit identifies a child's weak points and strengths based on the handwriting analysis results and suggests a practice method accordingly. For example, it provides specific advice such as, "You're not good at hiragana 'a', so you should practice that in particular." The feedback unit also automatically generates a practice plan based on the child's handwriting analysis results according to each child's progress. For example, it suggests, "As your next step, start practicing katakana." The feedback unit also suggests a practice method that suits the child's learning style based on the handwriting analysis results. For example, it provides advice such as, "Visual feedback is effective, so practice while watching a video." This allows the learning effect to be improved by suggesting the optimal practice method for each child.

[0062] The feedback unit can analyze the emotions of a child when writing using an emotion estimation function and provide feedback for eliciting positive emotions. For example, the feedback unit analyzes the emotions of a child when writing in real time using an emotion estimation function and provides feedback for eliciting positive emotions. For example, it displays a message such as "You seem to be enjoying writing! Keep it up." Also, when the feedback unit analyzes the emotions of a child when writing and detects negative emotions, it provides an encouraging message. For example, it gives feedback such as "It may be a bit difficult, but keep it up!" In addition, the feedback unit analyzes the emotions of a child when writing using an emotion estimation function and provides voice feedback for eliciting positive emotions. For example, it plays a voice message such as "Great! Keep it up like that." By analyzing the emotions of a child and eliciting positive emotions, the learning motivation can be enhanced.

[0063] The handwriting analysis unit can also support other languages and scripts. For example, the handwriting analysis unit expands the handwriting analysis function so that it can analyze not only hiragana and katakana but also the stroke order and shape of Chinese characters. For example, it provides feedback such as "The stroke order of the Chinese character 'wood' is correct." Also, the handwriting analysis unit makes the handwriting analysis function compatible with other languages so that it can analyze the writing of English alphabets and calligraphy. For example, it provides feedback such as "The shape of the alphabet 'A' is correct." In addition, the handwriting analysis unit expands the handwriting analysis function so that it can analyze characters in different scripts (e.g., Gothic and Mincho). For example, it provides feedback such as "The shape of the Gothic 'a' is correct." By supporting multiple languages and scripts, it can meet a wide range of learning needs.

[0064] The feedback unit can provide the handwriting analysis results as audio feedback to support learning both visually and audibly. For example, the feedback unit provides the handwriting analysis results as audio feedback to support learning both visually and audibly. For example, it plays an audio message such as, "The stroke order is correct." The feedback unit also customizes the audio feedback based on the handwriting analysis results to provide it in a way that is easy for the child to understand. For example, it provides specific audio advice such as, "Try to write with a more rounded edge." The feedback unit also uses audio feedback to provide the child with points to note and areas for improvement when writing in real time. For example, it provides audio advice such as, "If you write with your wrist a little more stable, your letters will be more stable." This makes it possible to improve learning by supporting learning both visually and audibly.

[0065] The feedback unit can use the emotion estimation function to monitor the child's emotions when writing in real time and provide feedback according to the emotions. For example, the feedback unit can use the emotion estimation function to monitor the child's emotions when writing in real time and provide feedback to elicit positive emotions. For example, it can display a message such as, "It looks like you're enjoying writing! Keep it up." The feedback unit can also monitor the child's emotions when writing in real time and, if a negative emotion is detected, provide an encouraging message. For example, it can provide feedback such as, "It might be a little difficult, but do your best!" The feedback unit can also use the emotion estimation function to monitor the child's emotions when writing in real time and provide audio feedback according to the emotion. For example, it can play an audio message such as, "Great! Keep it up." In this way, by monitoring the child's emotions in real time and providing feedback according to the emotion, it is possible to increase the child's motivation to learn.

[0066] The point management unit introduces an algorithm that evaluates a child's learning progress and effort, enabling more fair point awarding. The point management unit, for example, introduces an algorithm that evaluates a child's learning progress and effort to make the point system fairer. For example, points may be awarded based on the amount of time spent practicing each day and the level of achievement. The point management unit also develops an algorithm that awards points based on the results of a child's handwriting analysis to evaluate learning progress and effort. For example, points may be awarded if the child writes in the correct order and shape. The point management unit also introduces a point system that takes into account parental feedback to evaluate a child's learning progress and effort. For example, additional points may be awarded if a parent evaluates their child's effort. In this way, introducing an algorithm that evaluates learning progress and effort makes it possible to award points more fairly.

[0067] The point management department can introduce a system where points can be accumulated and exchanged for stationery and calligraphy tools. For example, the point management department introduces a system where points can be accumulated and exchanged for actual stationery and calligraphy tools. For example, 100 points can be exchanged for new pencils or erasers. The point management department can also introduce a system where points can be accumulated and exchanged for specific calligraphy tools or stationery sets. For example, 500 points can be exchanged for a high-quality calligraphy set. The point management department can also introduce a system where points can be accumulated and exchanged for limited edition stationery and calligraphy tools. For example, 1,000 points can be exchanged for a specially designed notebook or pen. By introducing a system where points can be accumulated and exchanged for actual stationery and calligraphy tools, it is possible to increase children's motivation to learn.

[0068] The point management unit can link the point system with other learning apps and educational services, enabling the mutual use of points. The point management unit, for example, links the point system with other learning apps and educational services, enabling the mutual use of points. For example, points accumulated in an English learning app can be used in this tool. The point management unit also links with other educational services, introducing a mechanism for mutual use of points. For example, points earned for online classes can be used in this tool. The point management unit also links the point system with other learning apps, enabling the mutual exchange of points. For example, points accumulated in a mathematics learning app can be used in this tool. In this way, by linking the point system with other learning apps and educational services, mutual use of points becomes possible, thereby increasing motivation to learn.

[0069] The point management unit can introduce a system whereby points can be accumulated to obtain the right to participate in events and workshops that parents and children can participate in together. The point management unit introduces a system whereby points can be accumulated to obtain the right to participate in events and workshops that parents and children can participate in together, for example. For example, 1,000 points can obtain the right to participate in a calligraphy class. The point management unit also introduces a system whereby points can be accumulated to obtain the right to participate in special events that parents and children can enjoy together. For example, 2,000 points can obtain the right to participate in a beautiful handwriting contest that parents and children can participate in together. The point management unit also introduces a system where points can be accumulated to obtain the right to participate in workshops that parents and children can learn together. For example, 1,500 points can obtain the right to participate in a handwriting analysis workshop that parents and children can participate in together. By introducing a system where points can be accumulated to obtain the right to participate in events and workshops that parents and children can participate in together, it is possible to deepen communication between parents and children.

[0070] The point management unit can use the emotion estimation function to analyze the emotions a child has when selecting a reward and suggest the reward that will be most appreciated. For example, the point management unit can use the emotion estimation function to analyze the emotions a child has when selecting a reward and suggest the reward that will be most appreciated. For example, the point management unit can display a message such as, "This reward is very popular. Why not try it?" The point management unit can also analyze the emotions a child has when selecting a reward and suggest a reward that will elicit positive emotions. For example, the point management unit can provide advice such as, "Many children enjoy this reward." The point management unit can also use the emotion estimation function to analyze the emotions a child has when selecting a reward and suggest the reward that will be most appreciated. For example, the point management unit can display a personalized message such as, "This reward is perfect for you." In this way, by analyzing the emotions a child has when selecting a reward and suggesting the reward that will be most appreciated, it is possible to increase a child's motivation to learn.

[0071] The parent-child communication unit can add a dashboard function that allows parents to monitor their child's learning status in real time. The parent-child communication unit, for example, adds a dashboard function that allows parents to monitor their child's learning status in real time. For example, the child's learning progress and point acquisition status are displayed in graphs. The parent-child communication unit also uses the dashboard function to allow parents to check their child's learning status in detail. For example, the percentage of correct answers for each practice question and the study time are displayed. The parent-child communication unit also adds a notification function to the dashboard so that parents can monitor their child's learning status in real time. For example, a notification is sent when a child acquires new points. In this way, adding a dashboard function that allows parents to monitor their child's learning status in real time can deepen parent-child communication.

[0072] The parent-child communication section can introduce a system in which parents and children can set joint tasks and missions that they can work on together and share a sense of accomplishment. For example, the parent-child communication section introduces a system in which parents and children can work on joint tasks and missions that they can work on together and share a sense of accomplishment. For example, a parent and child can practice calligraphy together and earn points together. The parent-child communication section also introduces a system in which parent-child communication deepens communication through joint tasks and missions. For example, a mission in which parents and children work together to write specific characters can be set. The parent-child communication section also introduces a system in which special rewards can be earned by completing a mission that parents and children can work on together. For example, a parent and child can practice calligraphy together and challenge a special stage. In this way, parent-child communication can be deepened by setting joint tasks and missions that parents and children can work on together and introducing a system in which a sense of accomplishment can be shared.

[0073] The parent-child communication unit can use the emotion estimation function to analyze the quality of parent-child communication and make suggestions to promote positive communication. For example, the parent-child communication unit can use the emotion estimation function to analyze the quality of parent-child communication and make suggestions to promote positive communication. For example, the parent-child communication unit can provide advice such as, "Try giving your child more compliments." The parent-child communication unit can also analyze the quality of parent-child communication and make suggestions to elicit positive emotions. For example, the parent-child communication unit can display a message such as, "Have fun with your child." The parent-child communication unit can also use the emotion estimation function to analyze the quality of parent-child communication and provide audio feedback to promote positive communication. For example, the parent-child communication unit can play an audio message such as, "Great! Keep it up." In this way, the parent-child relationship can be deepened by analyzing the quality of parent-child communication and making suggestions to promote positive communication.

[0074] The parent-child communication unit can add a function that enables parents to send customized messages of encouragement according to their child's learning progress. For example, the parent-child communication unit adds a function that enables parents to send customized messages of encouragement according to their child's learning progress. For example, sending a message such as "You're doing great! Keep it up!" The parent-child communication unit also adds a function that enables parents to send specific advice and messages of encouragement to their child according to their learning progress. For example, sending a message such as "You're almost at your goal! Let's do our best!" The parent-child communication unit also provides template messages so that parents can send customized messages of encouragement according to their child's learning progress. For example, sending a message such as "You're making great progress!" This allows parents to deepen communication between their children by sending customized messages of encouragement according to their child's learning progress.

[0075] The parent-child communication section can introduce a periodic report function that allows parents and children to review their learning outcomes together. For example, the parent-child communication section introduces a periodic report function that allows parents and children to review their learning outcomes together. For example, it provides a report summarizing weekly learning outcomes. The parent-child communication section also uses the periodic report function to allow parents and children to review their learning outcomes together and set the next goal. For example, it makes a suggestion such as, "Your results this week are excellent. Let's aim for this goal next time." The parent-child communication section also introduces a report function that allows parents and children to review their learning outcomes together, allowing parents to evaluate their children's efforts. For example, it provides a message such as, "Let's praise your efforts this week." Thus, by introducing a periodic report function that allows parents and children to review their learning outcomes together, parent-child communication can be deepened.

[0076] The parent-child communication unit can use the emotion estimation function to analyze emotions during parent-child communication and make suggestions for sending a cheering message at the optimal timing. For example, the parent-child communication unit can use the emotion estimation function to analyze emotions during parent-child communication and make suggestions for sending a cheering message at the optimal timing. For example, the parent-child communication unit can send a notification such as, "Now is the time to cheer." The parent-child communication unit can also analyze emotions during parent-child communication and make suggestions for sending a cheering message to elicit positive emotions. For example, the parent-child communication unit can provide advice such as, "Try giving your child more compliments." The parent-child communication unit can also use the emotion estimation function to analyze emotions during parent-child communication and provide audio feedback for sending a cheering message at the optimal timing. For example, the parent-child communication unit can play an audio message such as, "Great! Keep it up." In this way, parent-child communication can be deepened by analyzing emotions during parent-child communication and making suggestions for sending a cheering message at the optimal timing.

[0077] The existing teaching material linking unit can add a function to analyze the content of existing teaching materials and suggest supplementary teaching materials according to a child's level of understanding. The existing teaching material linking unit, for example, adds a function to analyze the content of existing teaching materials and suggest supplementary teaching materials according to a child's level of understanding. For example, supplementary teaching materials are provided for parts with low levels of understanding. The existing teaching material linking unit also analyzes the content of existing teaching materials and suggests supplementary teaching materials according to a child's learning progress. For example, supplementary teaching materials are provided for parts where progress is lagging. The existing teaching material linking unit also analyzes the content of existing teaching materials and suggests supplementary teaching materials that suit a child's learning style. For example, if visual teaching materials are effective, supplemental teaching materials that make extensive use of diagrams and illustrations are provided. In this way, learning effectiveness can be improved by analyzing the content of existing teaching materials and suggesting supplementary teaching materials according to a child's level of understanding.

[0078] The existing teaching material linking unit can use the emotion estimation function to analyze a child's emotions when working with existing teaching materials and suggest supplementary teaching materials to elicit positive emotions. For example, the existing teaching material linking unit can use the emotion estimation function to analyze a child's emotions when working with existing teaching materials and suggest supplementary teaching materials to elicit positive emotions. For example, the existing teaching material linking unit can display a message such as, "This teaching material is fun to learn with." The existing teaching material linking unit can also analyze a child's emotions when working with existing teaching materials and suggest supplementary teaching materials to elicit positive emotions. For example, the existing teaching material linking unit can provide advice such as, "Many children enjoy this teaching material." The existing teaching material linking unit can also use the emotion estimation function to analyze a child's emotions when working with existing teaching materials and provide audio feedback to elicit positive emotions. For example, the existing teaching material linking unit can play an audio message such as, "Great! Keep it up!" This allows the existing teaching material linking unit to analyze a child's emotions when working with existing teaching materials and suggest supplementary teaching materials to elicit positive emotions, thereby increasing motivation to learn.

[0079] The existing teaching material linking unit can be linked with existing teaching materials and introduce a function that allows home learning progress to be shared with school teachers. The existing teaching material linking unit, for example, can be linked with existing teaching materials and introduce a function that allows home learning progress to be shared with school teachers. For example, it allows teachers to check learning results at home. Furthermore, by sharing home learning progress with school teachers, the existing teaching material linking unit can ensure consistency between school and home learning. For example, teachers can understand the content of home learning and reflect it in lessons. Furthermore, the existing teaching material linking unit can be linked with existing teaching materials and introduce a function that allows home learning progress to be shared with school teachers in real time. For example, it allows teachers to check learning results at home in real time. In this way, by introducing a function that allows home learning progress to be shared with school teachers, it is possible to increase the consistency between home learning and school learning.

[0080] The existing teaching material linking unit can digitize the content of existing teaching materials and provide them as interactive practice questions. For example, the existing teaching material linking unit digitizes the content of existing teaching materials and provides them as interactive practice questions. For example, it can provide digital quizzes based on the content of textbooks. The existing teaching material linking unit also enables children to learn interactively using digitized teaching materials. For example, they can solve practice questions using a tablet. The existing teaching material linking unit also digitizes the content of existing teaching materials and enables children to learn at their own pace. For example, they can review at home using the digital teaching materials. In this way, by digitizing the content of existing teaching materials and providing them as interactive practice questions, it is possible to improve learning effectiveness.

[0081] The existing teaching material linking unit can use the emotion estimation function to monitor a child's emotions in real time as they work on existing teaching materials and provide feedback at the optimal timing. For example, the existing teaching material linking unit can use the emotion estimation function to monitor a child's emotions in real time as they work on existing teaching materials and provide feedback at the optimal timing. For example, it can send a notification such as, "Now is the time for feedback." The existing teaching material linking unit can also monitor a child's emotions in real time as they work on existing teaching materials and provide feedback to elicit positive emotions. For example, it can display a message such as, "Great! Keep it up!" The existing teaching material linking unit can also use the emotion estimation function to monitor a child's emotions in real time as they work on existing teaching materials and, if negative emotions are detected, provide an encouraging message. For example, it can provide feedback such as, "It might be a little difficult, but let's do our best!" In this way, by monitoring a child's emotions in real time as they work on existing teaching materials and providing feedback at the optimal timing, it is possible to increase their motivation to learn.

[0082] The learning environment optimization unit can add a function to automatically generate an optimal learning plan for each child according to their learning progress. The learning environment optimization unit adds a function to automatically generate an optimal learning plan for each child according to their learning progress, for example. For example, it provides a practice plan that focuses on areas where progress is lagging. The learning environment optimization unit also analyzes the child's learning progress and automatically generates a learning plan tailored to individual needs. For example, it provides a plan to further develop areas of strength. The learning environment optimization unit also automatically generates an optimal learning plan for each child according to their learning progress and allows parents to check the plan. For example, it allows parents to check and support their child's learning plan. In this way, the learning effect can be improved by automatically generating an optimal learning plan for each child according to their learning progress.

[0083] The learning environment optimization unit can not only visually confirm learning outcomes, but also provide audio and video feedback. For example, the learning environment optimization unit can not only visually confirm learning outcomes, but also provide audio and video feedback. For example, it can display learning outcomes in a graph and provide audio feedback. The learning environment optimization unit can also provide video feedback to enable visual confirmation of learning outcomes. For example, it can explain learning progress in a video and suggest next steps. The learning environment optimization unit can not only visually confirm learning outcomes, but also use audio feedback to make it easier for children to understand. For example, it can play an audio message such as, "You're making great progress!" This makes it possible to not only visually confirm learning outcomes, but also provide audio and video feedback, thereby improving learning effectiveness.

[0084] The learning environment optimization unit can use the emotion estimation function to analyze a child's emotions when studying and suggest a learning environment that will bring out positive emotions. For example, the learning environment optimization unit can use the emotion estimation function to analyze a child's emotions when studying and suggest a learning environment that will bring out positive emotions. For example, the learning environment optimization unit can provide advice such as, "Studying in a relaxed environment is effective." The learning environment optimization unit can also analyze a child's emotions when studying and suggest a learning environment that will bring out positive emotions. For example, the learning environment optimization unit can provide advice such as, "Studying in a well-lit place makes it easier to concentrate." The learning environment optimization unit can also use the emotion estimation function to analyze a child's emotions when studying and provide audio feedback that will bring out positive emotions. For example, the learning environment optimization unit can play an audio message such as, "Great! Keep it up!" In this way, by analyzing a child's emotions when studying and suggesting a learning environment that will bring out positive emotions, it is possible to increase motivation to learn.

[0085] The learning environment optimization unit can add a function to provide advice for optimizing the learning environment at home according to the progress of learning. The learning environment optimization unit adds a function to provide advice for optimizing the learning environment at home according to the progress of learning. For example, the learning environment optimization unit provides advice such as, "Rearranging your desk will make it easier to concentrate." The learning environment optimization unit also analyzes the child's learning progress and provides specific advice for optimizing the learning environment at home. For example, the learning environment optimization unit provides advice such as, "It is effective to set a fixed study time." The learning environment optimization unit also provides advice for optimizing the learning environment at home according to the progress of learning, and enables parents to implement the advice. For example, the learning environment optimization unit provides advice such as, "Keeping your study space tidy will make it easier to concentrate." In this way, by providing advice for optimizing the learning environment at home according to the progress of learning, it is possible to improve learning effectiveness.

[0086] The learning environment optimization unit can use the emotion estimation function to monitor a child's emotions when studying in real time and suggest an optimal learning environment. For example, the learning environment optimization unit uses the emotion estimation function to monitor a child's emotions when studying in real time and suggest an optimal learning environment. For example, it provides advice such as, "Studying in a relaxed environment is effective." The learning environment optimization unit also monitors a child's emotions when studying in real time and suggests a learning environment that will elicit positive emotions. For example, it provides advice such as, "Studying in a well-lit place makes it easier to concentrate." The learning environment optimization unit also uses the emotion estimation function to monitor a child's emotions when studying in real time and provides audio feedback to suggest an optimal learning environment. For example, it plays an audio message such as, "Great! Keep it up!" In this way, by monitoring a child's emotions when studying in real time and suggesting an optimal learning environment, it is possible to increase motivation to learn.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The interactive learning tool can further include a speech recognition unit. The speech recognition unit analyzes the sounds made by a child while writing and evaluates the accuracy and rhythm of pronunciation. For example, when a child writes "hiragana," it determines whether the pronunciation is correct and provides feedback such as "Your pronunciation is correct" or "Please pronounce it a little more slowly." The speech recognition unit also analyzes the sounds made by a child while writing and provides advice on stroke order and shape by voice. For example, it provides specific instructions by voice such as "Next, draw this line." In this way, speech recognition can be used to support learning both visually and aurally.

[0089] The interactive learning tool can further include a gamification section. The gamification section allows children to continue learning while having fun by progressing through their learning in a game format. For example, a game is provided in which a character grows when characters are written in the correct order and shape. The gamification section also provides level-ups and bonus stages according to the progress of learning. For example, a new stage is unlocked when a certain number of points are accumulated. The gamification section also provides a multiplayer mode that parents and children can enjoy together. For example, parents and children can work together to complete a specific mission and earn special rewards. In this way, incorporating game elements can increase children's motivation to learn.

[0090] The interactive learning tool can further include a virtual reality (VR) section. The VR section provides children with the experience of writing characters in a virtual space. For example, by wearing VR goggles, they can practice writing characters in a virtual calligraphy classroom. The VR section also provides real-time feedback on the results of handwriting analysis in the virtual space. For example, it displays a message in the virtual space saying, "Your stroke order is correct." The VR section also allows children to learn while having fun through learning in the virtual space. For example, they can earn rewards by completing a mission to write characters together with a virtual character. In this way, VR technology can be used to provide an environment where children can learn while having fun.

[0091] The feedback unit can use the emotion estimation function to analyze the emotions a child feels when writing and provide feedback to elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotions a child feels when writing in real time and provide feedback to elicit positive emotions. For example, a message such as "It looks like you're enjoying writing! Keep it up!" can be displayed. The feedback unit can also analyze the emotions a child feels when writing and, if negative emotions are detected, provide an encouraging message. For example, feedback such as "It might be a little difficult, but do your best!" can be provided. The feedback unit can also use the emotion estimation function to analyze the emotions a child feels when writing and provide audio feedback to elicit positive emotions. For example, an audio message such as "Great! Keep it up!" can be played. This makes it possible to analyze a child's emotions and elicit positive emotions, thereby increasing their motivation to learn.

[0092] The interactive learning tool may further include a social collaboration unit. The social collaboration unit provides a function that allows children to share their learning results with other users and compete with each other. For example, they can post their learning results on a social networking site and share them with their friends. The social collaboration unit also displays the learning results in a ranking format, allowing them to compete with other users. For example, if they rank high in the weekly rankings, they can earn special rewards. The social collaboration unit also provides a function that allows parents and children to share their learning results together and deepen communication. For example, parents and children can post their learning results together on a social networking site and interact with other families. This can increase children's motivation to learn through social collaboration.

[0093] The feedback unit can use the emotion estimation function to monitor a child's emotions in real time as they write and provide feedback according to their emotions. For example, the emotion estimation function can be used to monitor a child's emotions in real time as they write and provide feedback to elicit positive emotions. For example, a message such as "It looks like you're enjoying writing! Keep it up!" can be displayed. The feedback unit can also monitor a child's emotions in real time as they write, and if negative emotions are detected, an encouraging message can be provided. For example, feedback such as "It might be a little difficult, but do your best!" can be provided. The feedback unit can also use the emotion estimation function to monitor a child's emotions in real time as they write and provide audio feedback according to their emotions. For example, an audio message such as "Great! Keep it up!" can be played. In this way, by monitoring a child's emotions in real time and providing feedback according to their emotions, it is possible to increase their motivation to learn.

[0094] The interactive learning tool can further include a customization unit. The customization unit provides a function for customizing learning content and feedback according to a child's learning style and preferences. For example, the learning content can be customized based on a child's favorite character or theme. The customization unit also customizes feedback according to a child's learning progress and strengths. For example, it provides specific advice such as, "You're good at the hiragana character 'a,' so next you should practice 'i'." The customization unit also customizes the format of feedback to suit a child's learning style. For example, if visual feedback is effective, it can provide feedback that makes extensive use of diagrams and illustrations. In this way, learning effectiveness can be improved by customizing learning content and feedback according to a child's learning style and preferences.

[0095] The interactive learning tool can further include a reward section. The reward section provides a function that provides actual rewards according to children's learning achievements. For example, once a certain number of points have been accumulated, they can be exchanged for stationery or calligraphy tools. The reward section also provides the right to participate in special events or workshops according to learning achievements. For example, with 1,000 points, you can earn the right to participate in a calligraphy class. The reward section also provides special rewards that parents and children can enjoy together according to learning achievements. For example, you can earn the right to participate in a beautiful handwriting contest that parents and children can participate in together. This can increase children's motivation to learn by providing actual rewards according to learning achievements.

[0096] The point management unit can use the emotion estimation function to analyze the emotions a child has when selecting a reward and suggest the reward that will be most appreciated. For example, the emotion estimation function can be used to analyze the emotions a child has when selecting a reward and suggest the reward that will be most appreciated. For example, a message such as "This reward is very popular. Why not try it?" can be displayed. The point management unit can also analyze the emotions a child has when selecting a reward and suggest a reward that will elicit positive emotions. For example, the point management unit can provide advice such as "Many children enjoy this reward." The point management unit can also use the emotion estimation function to analyze the emotions a child has when selecting a reward and suggest the reward that will be most appreciated. For example, a personalized message such as "This reward is perfect for you" can be displayed. In this way, by analyzing the emotions a child has when selecting a reward and suggesting the reward that will be most appreciated, it is possible to increase a child's motivation to learn.

[0097] The interactive learning tool can further include an analytics section. The analytics section analyzes a child's learning data and provides a function for understanding learning trends and patterns. For example, it identifies which characters a child is good at and which characters they are weak at based on the child's learning data. The analytics section also analyzes the learning data and visualizes the child's learning progress. For example, it displays the learning progress using graphs and charts. The analytics section also analyzes the learning data and makes suggestions to maximize the child's learning effectiveness. For example, it provides advice such as, "It would be more effective if you continued practicing this character a little longer." In this way, it is possible to analyze the learning data and understand learning trends and patterns, thereby improving learning effectiveness.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The handwriting analysis unit analyzes the child's handwriting using a smartphone camera. For example, it analyzes handwriting images captured by the smartphone camera and determines the stroke order and shape. Step 2: The feedback unit provides real-time feedback based on the handwriting analyzed by the handwriting analysis unit. For example, it determines whether the stroke order is correct and displays feedback such as "The stroke order is correct" or "Please write with a more rounded stroke." Step 3: The point management unit awards points when characters are written in the correct order and shape. For example, if you collect 100 points, a new character will be unlocked. Step 4: The Parent-Child Communication Department notifies parents of their child's learning status. For example, parents can check their child's progress and accumulated points through an app.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] 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]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A handwriting analysis unit that analyzes the child's handwriting using a smartphone camera; a feedback unit that provides real-time feedback based on the handwriting analyzed by the handwriting analysis unit; A point management section that awards points when characters are written in the correct stroke order and shape, A parent-child communication section that notifies parents of their children's learning status. A system characterized by:

2. The feedback unit Analyzes the subtle movements and pressure of the handwriting to provide detailed feedback 2. The system of claim 1.

3. The feedback unit It has a personalized feedback function that suggests the best practice method for each child based on the results of handwriting analysis.

2. The system of claim 1.

4. The feedback unit Analyze the child's emotions when writing and provide feedback to elicit positive emotions.

2. The system of claim 1.

5. The handwriting analysis unit Supports other languages ​​and scripts 2. The system of claim 1.

6. The feedback unit Provides audio feedback based on handwriting analysis results, supporting learning through both visual and auditory means 2. The system of claim 1.

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