system

Abstract

The system according to this embodiment aims to provide immediate feedback during handwriting practice and maintain motivation. [Solution] The system according to the embodiment comprises an analysis unit, a provision unit, an adjustment unit, a display unit, and a gamification unit. The analysis unit analyzes handwritten characters. The provision unit provides feedback based on the results analyzed by the analysis unit. The adjustment unit adjusts the practice tasks based on the feedback provided by the provision unit. The display unit visually displays the practice tasks adjusted by the adjustment unit using AR technology. The gamification unit provides gamification elements to maintain user motivation.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to provide immediate feedback and maintain motivation in the practice of handwritten characters.

[0005] The system according to the embodiment aims to provide immediate feedback and maintain motivation in the practice of handwritten characters.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a provision unit, an adjustment unit, a display unit, and a gamification unit. The analysis unit analyzes handwritten characters. The provision unit provides feedback based on the results analyzed by the analysis unit. The adjustment unit adjusts the practice tasks based on the feedback provided by the provision unit. The display unit visually displays the practice tasks adjusted by the adjustment unit using AR technology. The gamification unit provides gamification elements to maintain user motivation. [Effects of the Invention]

[0007] The system according to this embodiment can provide immediate feedback during handwriting practice and maintain motivation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The gamified autonomous feedback AI agent system according to an embodiment of the present invention is a system that not only analyzes handwritten characters in real time and provides immediate feedback, but also autonomously adjusts practice tasks according to the user's progress and maintains motivation. This system utilizes AR technology to visually display areas for improvement in handwritten characters, supporting intuitive understanding. Furthermore, gamification elements are incorporated to allow users to continue practicing handwritten characters while having fun. For example, when a user writes handwritten characters, the system analyzes those characters in real time. Based on the analysis results, immediate feedback is provided. For example, areas for improvement such as character shape, balance, and pen pressure are pointed out. This feedback is displayed visually using AR technology, so users can understand it intuitively. Next, the system autonomously adjusts practice tasks according to the user's progress. For example, if the user has difficulty with a particular character or writing style, tasks that focus on practicing that part are presented. In addition, gamification elements are incorporated to maintain user motivation. Specifically, these include leveling up, badge acquisition, and ranking functions, allowing users to continue practicing handwritten characters while having fun. This system targets students, business professionals, and anyone aiming to improve their handwriting. It can solve communication problems caused by illegible or messy handwriting, as well as the reduced reliability of handwritten documents. It also addresses the difficulty of maintaining motivation due to consistent practice. Thus, the system improves the quality of handwriting, facilitating smoother communication and increasing the reliability of handwritten documents. Furthermore, by utilizing AR technology, it provides an intuitive and effective learning experience, creating an environment where learning is enjoyable through gamification elements. This helps to make handwriting practice a habit. In summary, this gamified autonomous feedback AI agent system can improve the quality of handwriting, facilitating smoother communication and increasing the reliability of handwritten documents.

[0029] The gamification autonomous feedback AI agent system according to this embodiment comprises an analysis unit, a provision unit, an adjustment unit, a display unit, and a gamification unit. The analysis unit analyzes handwritten characters. The analysis unit analyzes, for example, the shape, balance, and pressure of the handwritten characters. To analyze the shape of handwritten characters, the analysis unit can evaluate the outline and line thickness of the characters. To analyze the balance of handwritten characters, the analysis unit can evaluate the placement and inclination of the characters. To analyze the pressure of handwritten characters, the analysis unit can evaluate the strength and changes in the pressure. The provision unit provides feedback based on the results analyzed by the analysis unit. For example, the provision unit points out areas for improvement in the characters based on the analysis results. The provision unit can point out areas for improvement in the shape, balance, and pressure of the characters. The provision unit can provide feedback in the form of text, audio, visuals, etc. The adjustment unit adjusts the practice tasks based on the feedback provided by the provision unit. For example, the adjustment unit adjusts the practice tasks according to the user's progress. The adjustment unit can present tasks that focus on specific characters or writing styles if the user has difficulty with those areas. The adjustment unit can consider the number of practice sessions and achievement level to evaluate the user's progress. The display unit visually displays the practice tasks adjusted by the adjustment unit using AR technology. The display unit visually displays, for example, areas for improvement in handwriting. The display unit can visually display information using methods such as 3D display or animation. The gamification unit provides gamification elements to maintain user motivation. The gamification unit provides features such as leveling up, badge acquisition, and ranking functions. The gamification unit can provide leveling up based on a point system or achievement conditions. The gamification unit can provide badges based on the completion of specific tasks or a certain number of practice sessions. The gamification unit can provide ranking functions such as point rankings and achievement rankings.As a result, the gamified autonomous feedback AI agent system according to this embodiment can analyze handwritten characters, provide feedback, adjust practice tasks, provide visual display, and maintain motivation.

[0030] The analysis unit analyzes handwritten characters. For example, it analyzes the shape, balance, and pressure of the handwritten characters. Specifically, to analyze the shape of handwritten characters, it can evaluate the outline of the characters and the thickness of the lines. This involves a process that uses image processing technology to extract the outline of the characters and quantifies the thickness of the lines and the smoothness of the curves. Furthermore, to analyze the balance of the characters, it can evaluate the placement and inclination of the characters. For example, it calculates the center point of the characters and evaluates how the center of gravity of the entire character is positioned. Regarding the inclination of the characters, it measures the angle of each part of the character and evaluates it by comparing it with a standard way of writing. For pen pressure analysis, a pen pressure sensor can be used to evaluate the strength and changes in pen pressure. The pen pressure sensor measures the pressure applied while writing in real time and transmits this data to the analysis unit. The analysis unit integrates this data to perform a comprehensive evaluation of the handwritten characters. For example, it can identify specific problems such as distorted character shapes, poor balance, or uneven pen pressure. This allows the analysis unit to perform a detailed analysis of handwritten characters, clearly identifying the characteristics of the user's writing style and areas for improvement.

[0031] The service provider provides feedback based on the results analyzed by the analysis unit. For example, the service provider can point out areas for improvement in handwriting based on the analysis results. Specifically, it can point out areas for improvement in the shape and balance of characters, as well as pen pressure. The service provider can provide feedback in various formats, such as text, audio, and visual. For example, if the shape of the characters is distorted, it can explain specific correction methods in text and give instructions in audio. As visual feedback, it can display the correct character shape alongside the character written by the user, visually showing which parts are different. Furthermore, the service provider can adjust the content of the feedback according to the user's progress. For example, it can provide basic feedback to first-time users and more detailed and advanced feedback to experienced users. The service provider can also adjust the frequency and timing of feedback. For example, it can support effective learning by providing feedback after the user has completed a certain amount of practice. In this way, the service provider can provide users with appropriate and effective feedback and help them improve their handwriting.

[0032] The adjustment unit adjusts practice tasks based on feedback provided by the provision unit. For example, the adjustment unit adjusts practice tasks according to the user's progress. Specifically, if a user has difficulty with a particular character or writing style, it can present tasks that focus on practicing that area. The adjustment unit can consider the number of practice sessions and the level of achievement to evaluate the user's progress. For example, if a user practices a particular character many times but shows no improvement, the adjustment unit can suggest a new way to practice that character. Also, if a user reaches a certain level of achievement, it can present tasks at the next level. The adjustment unit analyzes the user's practice data and creates an optimal practice plan for each individual user. For example, if a user's pen pressure is uneven, it can provide special practice tasks to make the pen pressure even. Also, if a user's handwriting is unbalanced, it can provide practice tasks to improve the balance. In this way, the adjustment unit can provide practice tasks that meet the individual needs of the user and support effective learning.

[0033] The display unit visually displays practice tasks adjusted by the adjustment unit using AR technology. For example, the display unit visually displays areas for improvement in handwriting. Specifically, it can use AR technology to overlay the correct character shape onto the character written by the user. This allows the user to practice while comparing their handwriting with the correct character. The display unit can also visually display information using methods such as 3D display and animation. For example, it can display the stroke order of characters and changes in pen pressure with animation, allowing the user to visually understand the correct way to write. Furthermore, the display unit can visually display the user's progress. For example, it can display tasks the user has completed and badges they have earned, providing visual feedback to maintain motivation. In this way, the display unit can provide users with visually easy-to-understand feedback and support effective learning.

[0034] The Gamification Department provides gamification elements to maintain user motivation. For example, it provides features such as level-ups, badge acquisition, and rankings. Specifically, it can offer level-ups based on point systems and achievement conditions. Users earn points each time they complete a specific task, and level up once a certain number of points are accumulated. It can also provide badges based on the completion of specific tasks or a certain number of practice sessions. For example, a "Beginner Badge" can be earned for completing a first task, and an "Advanced Badge" for completing a more difficult task. Furthermore, ranking features such as point rankings and achievement rankings can be provided. This allows users to compete with other users while practicing, thus maintaining motivation. The Gamification Department can analyze user practice data and provide gamification elements optimized for each individual user. For example, if a user is struggling with a particular task, it can offer special rewards for completing that task. This allows the Gamification Department to increase user motivation and support continuous learning.

[0035] The analysis unit can analyze the shape, balance, and pressure of handwritten characters. For example, to analyze the shape of handwritten characters, the analysis unit evaluates the outline and line thickness of the characters. To analyze the balance of handwritten characters, the analysis unit can also evaluate the placement and inclination of the characters. To analyze the pressure of handwritten characters, the analysis unit can also evaluate the strength and changes in pen pressure. This enables detailed analysis of handwritten characters. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input handwritten character image data into a generating AI and have the generating AI perform the analysis of the character shape, balance, and pen pressure.

[0036] The service provider can point out areas for improvement in handwriting based on the analysis results. For example, the service provider can point out areas for improvement in the shape, balance, and pressure of the characters based on the analysis results. To point out areas for improvement in the shape of the characters, the service provider can suggest corrections to the outline and line thickness of the characters. To point out areas for improvement in the balance of the characters, the service provider can also suggest corrections to the placement and tilt of the characters. To point out areas for improvement in pressure, the service provider can also suggest corrections to the strength and variation of the pressure applied to the pen. This allows for an improvement in the quality of the user's handwriting by pointing out areas for improvement in their handwriting. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI point out areas for improvement in the characters.

[0037] The adjustment unit can adjust practice tasks according to the user's progress. For example, the adjustment unit may present practice tasks that focus on specific characters or writing styles depending on the user's progress. The adjustment unit may consider the number of practice sessions and the degree of achievement in order to evaluate the user's progress. The adjustment unit can also adjust the difficulty and content of the practice tasks based on the user's progress. This makes it possible to adjust practice tasks according to the user's progress. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input user progress data into a generating AI and have the generating AI perform the adjustment of practice tasks.

[0038] The display unit can visually display areas for improvement in handwritten characters using AR technology. For example, the display unit can visually display areas for improvement in handwritten characters using methods such as 3D display or animation. The display unit can utilize AR technology to visually display areas for improvement in the shape, balance, and pressure of handwritten characters. The display unit can also visually display areas for improvement so that users can understand them intuitively. This deepens user understanding by visually displaying areas for improvement in handwritten characters. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input areas for improvement in handwritten characters into a generating AI and have the generating AI perform the visual display.

[0039] The gamification unit can provide features such as level-ups, badge acquisition, and rankings. For example, the gamification unit can provide level-ups based on a point system or achievement conditions. The gamification unit can provide badges based on the completion of specific tasks or a certain number of practice sessions. The gamification unit can also provide ranking functions such as point rankings and achievement rankings. This provides gamification elements to maintain user motivation. Some or all of the above processes in the gamification unit may be performed using AI, for example, or not using AI. For example, the gamification unit can input user achievement data into a generating AI and have the generating AI perform level-ups, badge acquisition, and provide ranking functions.

[0040] The analysis unit can correct the analysis results when analyzing handwritten characters, taking into account the user's writing speed. For example, if the user writes quickly, the analysis unit can relax its evaluation of character shape and balance, taking the writing speed into consideration. If the user writes slowly, the analysis unit can also perform a detailed analysis, taking the writing speed into consideration, and provide highly accurate feedback. If the user writes at a constant speed, the analysis unit can also correct the analysis results based on the writing speed and provide appropriate feedback. This makes it possible to correct the analysis results according to the user's writing speed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's writing speed data into a generating AI and have the generating AI perform the correction of the analysis results.

[0041] The analysis unit can perform handwritten character analysis while considering the user's writing environment (e.g., paper type and pen type). For example, if the user writes on rough paper, the analysis unit can adjust the evaluation of character shape and balance considering the paper type. If the user writes on smooth paper, the analysis unit can also perform a detailed analysis considering the paper type and provide highly accurate feedback. If the user uses a specific pen, the analysis unit can also correct the analysis results considering the pen type and provide appropriate feedback. This enables analysis tailored to the user's writing environment. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's writing environment data into a generating AI and have the generating AI perform the analysis.

[0042] The analysis unit can improve the accuracy of handwritten character analysis by referring to the user's past writing data. For example, the analysis unit can improve the accuracy of analysis based on data of characters the user has written in the past. The analysis unit can also analyze the user's past writing data, find specific patterns, and incorporate them into the analysis. The analysis unit can also refer to the user's past writing data to provide individualized feedback. This makes it possible to improve the accuracy of analysis based on the user's past writing data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past writing data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0043] The analysis unit can perform handwriting analysis while considering the user's writing style (e.g., whether they are right-handed or left-handed). For example, if the user is right-handed, the analysis unit will use an analysis method suitable for right-handed users. If the user is left-handed, the analysis unit can also use an analysis method suitable for left-handed users. If the user has a specific writing style, the analysis unit can also perform analysis according to that style. This makes it possible to perform analysis according to the user's writing style. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's writing style data into a generating AI and have the generating AI perform the analysis.

[0044] The service provider can adjust the level of detail in the feedback provided according to the user's level of understanding. For example, if the user has a low level of understanding, the service provider can provide feedback that includes detailed explanations. If the user has a high level of understanding, the service provider can also provide concise feedback. The service provider can also adjust the content of the feedback according to the user's level of understanding. This makes it possible to adjust the level of detail in the feedback according to the user's level of understanding. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input user understanding data into a generating AI and have the generating AI perform the adjustment of the level of detail in the feedback.

[0045] The service provider can provide optimal feedback by referring to the user's learning history when providing feedback. For example, the service provider can provide optimal feedback based on the user's past learning history. The service provider can also analyze the user's learning history, find specific patterns, and reflect them in the feedback. The service provider can also provide individualized feedback by referring to the user's learning history. This makes it possible to provide optimal feedback based on the user's learning history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's learning history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0046] The feedback provider can adjust the way feedback is presented according to the user's age and learning level. For example, if the user is young, the provider can provide feedback in friendly language. If the user is older, the provider can also provide feedback in polite language. The provider can also adjust the way feedback is presented according to the user's learning level. This makes it possible to adjust the way feedback is presented according to the user's age and learning level. Some or all of the above processing in the feedback provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's age and learning level data into a generating AI and have the generating AI perform the adjustment of the way feedback is presented.

[0047] The service provider can customize the content of the feedback based on the user's goals when providing feedback. For example, the service provider customizes the content of the feedback according to the user's goals. If the user has a specific goal, the service provider can also provide feedback tailored to that goal. The service provider can also adjust the content of the feedback based on the user's goals. This makes it possible to customize the content of the feedback based on the user's goals. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's goal data into a generating AI and have the generating AI perform the customization of the feedback content.

[0048] The adjustment unit can select the optimal practice task by referring to the user's past practice history when adjusting practice tasks. For example, the adjustment unit selects the optimal practice task based on the user's past practice history. The adjustment unit can also analyze the user's practice history, find specific patterns, and reflect them in the practice tasks. The adjustment unit can also provide individual practice tasks by referring to the user's practice history. This makes it possible to select the optimal practice task based on the user's past practice history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's past practice history data into a generating AI and have the generating AI perform the selection of the optimal practice task.

[0049] The adjustment unit can adjust the amount of practice tasks according to the user's learning pace when adjusting practice tasks. For example, if the user's learning pace is fast, the adjustment unit can increase the amount of tasks. If the user's learning pace is slow, the adjustment unit can also decrease the amount of tasks. The adjustment unit can also adjust the amount of tasks according to the user's learning pace. This makes it possible to adjust the amount of tasks according to the user's learning pace. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's learning pace data into a generating AI and have the generating AI perform the adjustment of the amount of tasks.

[0050] The adjustment unit can customize the content of practice tasks based on the user's interests when adjusting them. For example, the adjustment unit can customize the content of tasks according to the user's interests. If the user has a specific interest, the adjustment unit can also provide tasks tailored to that interest. The adjustment unit can also adjust the content of tasks based on the user's interests. This makes it possible to customize the content of tasks based on the user's interests. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input user interest data into a generating AI and have the generating AI perform the customization of the task content.

[0051] The adjustment unit can adjust the timing of assignment submissions according to the user's daily routine when adjusting practice assignments. For example, the adjustment unit adjusts the timing of assignment submissions according to the user's daily routine. The adjustment unit can also extend the submission deadline if the user is busy. The adjustment unit can also shorten the submission deadline if the user has time. This makes it possible to adjust the timing of assignment submissions according to the user's daily routine. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's daily routine data into a generating AI and have the generating AI perform the adjustment of the assignment submission timing.

[0052] The display unit can adjust the display method when displaying information, taking into account the user's visual characteristics (e.g., color blindness). For example, if the user has color blindness, the display unit can enhance the color contrast. If the user has a visual impairment, the display unit can also display information in a larger font size. The display unit can also adjust the display method according to the user's visual characteristics. This makes it possible to adjust the display method according to the user's visual characteristics. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's visual characteristics data into a generating AI and have the generating AI perform the adjustment of the display method.

[0053] The display unit can adjust the display method when displaying information, taking into account the user's device characteristics (e.g., screen size and resolution). For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. If the user is using a tablet, the display unit can also provide a display method optimized for a larger screen. If the user is using a high-resolution device, the display unit can also provide a display method that corresponds to the resolution. This makes it possible to adjust the display method according to the user's device characteristics. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user device characteristic data into a generating AI and have the generating AI perform the adjustment of the display method.

[0054] The display unit can select the optimal display method by referring to the user's past viewing history when displaying information. For example, the display unit can select the optimal display method based on the user's past viewing history. The display unit can also analyze the user's viewing history, find specific patterns, and reflect them in the display. The display unit can also refer to the user's viewing history and provide individual display methods. This makes it possible to select the optimal display method based on the user's past viewing history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past viewing history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0055] The display unit can adjust the display method when displaying information, taking into account the user's environment (e.g., brightness and volume). For example, the display unit adjusts the brightness of the display if the user is in a bright place. The display unit can also adjust the brightness of the display if the user is in a dark place. The display unit can also adjust the display method according to the user's environment. This makes it possible to adjust the display method according to the user's environment. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user environment data into a generating AI and have the generating AI perform the adjustment of the display method.

[0056] The gamification unit can provide optimal gamification elements by referring to the user's past game history when providing gamification elements. For example, the gamification unit can provide optimal game elements based on the user's past game history. The gamification unit can also analyze the user's game history, find specific patterns, and reflect them in the game elements. The gamification unit can also provide individual game elements by referring to the user's game history. This makes it possible to provide optimal gamification elements based on the user's past game history. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without AI. For example, the gamification unit can input the user's past game history data into a generating AI and have the generating AI perform the task of providing optimal game elements.

[0057] The gamification unit can add elements that stimulate the user's competitive spirit when providing gamification elements. For example, the gamification unit can provide a ranking function to stimulate the user's competitive spirit. The gamification unit can also provide badges and trophies to stimulate the user's competitive spirit. The gamification unit can also provide a function to compete against other users to stimulate the user's competitive spirit. This makes it possible to add elements that stimulate the user's competitive spirit. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without using AI. For example, the gamification unit can input user competitive spirit data into a generating AI and have the generating AI execute the addition of elements that stimulate competitive spirit.

[0058] The gamification unit can customize the content of gamification elements based on the user's interests when providing them. For example, the gamification unit can customize the content of game elements according to the user's interests. The gamification unit can also provide game elements tailored to the user's specific interests. The gamification unit can also adjust the content of game elements based on the user's interests. This makes it possible to customize the content of gamification elements based on the user's interests. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without AI. For example, the gamification unit can input user interest data into a generating AI and have the generating AI perform the customization of the element content.

[0059] The gamification unit can add collaboration features with the user's friends and family when providing gamification elements. For example, the gamification unit can provide collaboration features to allow the user to enjoy game elements together with their friends and family. The gamification unit can also provide ranking features to allow the user to compete with their friends and family. The gamification unit can also provide collaboration features to allow the user to work together with their friends and family to clear game elements. This makes it possible to add collaboration features with the user's friends and family. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without AI. For example, the gamification unit can input collaboration data with the user's friends and family into a generating AI and have the generating AI execute the addition of collaboration features.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The analysis unit can adjust the analysis results of handwritten characters by taking into account the user's writing speed. For example, if the user writes quickly, the evaluation of character shape and balance can be relaxed to account for the writing speed. Conversely, if the user writes slowly, a detailed analysis can be performed to provide highly accurate feedback. Furthermore, if the user writes at a constant speed, the analysis results can be adjusted based on the writing speed to provide appropriate feedback. This makes it possible to adjust the analysis results according to the user's writing speed.

[0062] The adjustment unit can select the optimal task by referring to the user's past practice history. For example, it can select the optimal practice task based on the user's past practice history. It can also analyze the user's practice history, find specific patterns, and reflect them in the practice tasks. Furthermore, it can provide individual practice tasks by referring to the user's practice history. This makes it possible to select the optimal practice task based on the user's past practice history.

[0063] The gamification component can add elements that stimulate users' competitive spirit. For example, it can provide a ranking function to stimulate users' competitive spirit. It can also provide badges and trophies to stimulate users' competitive spirit. Furthermore, it can provide a function to compete against other users to stimulate users' competitive spirit. This makes it possible to add elements that stimulate users' competitive spirit.

[0064] The analysis unit can analyze handwritten characters while considering the user's writing environment (e.g., paper type and pen type). For example, if the user writes on rough paper, the system can adjust the evaluation of character shape and balance by considering the paper type. If the user writes on smooth paper, the system can perform a detailed analysis considering the paper type and provide highly accurate feedback. Furthermore, if the user uses a specific pen, the system can correct the analysis results by considering the pen type and provide appropriate feedback. This enables analysis tailored to the user's writing environment.

[0065] The feedback system can adjust the level of detail in the feedback provided according to the user's level of understanding. For example, if the user has a low level of understanding, it can provide feedback that includes detailed explanations. If the user has a high level of understanding, it can provide concise feedback. Furthermore, it can adjust the content of the feedback according to the user's level of understanding. This makes it possible to adjust the level of detail in the feedback according to the user's level of understanding.

[0066] The display unit can adjust its display method to take into account the user's visual characteristics (e.g., color blindness). For example, if the user has color blindness, the display can be enhanced with increased color contrast. If the user has a visual impairment, the display can be shown in a larger font size. Furthermore, the display method can be adjusted according to the user's visual characteristics. This makes it possible to adjust the display method according to the user's visual characteristics.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The analysis unit analyzes the handwritten characters. The analysis unit analyzes the shape, balance, and pressure of the handwritten characters, and can evaluate the outline of the characters, the thickness of the lines, their placement and inclination, and the strength and changes in the pressure. Step 2: The service provider provides feedback based on the results analyzed by the analysis unit. The service provider can point out areas for improvement in character shape, balance, and pen pressure, and can provide feedback in various formats such as text, audio, and visuals. Step 3: The adjustment unit adjusts the practice tasks based on the feedback provided by the provision unit. The adjustment unit adjusts the practice tasks according to the user's progress and can present tasks that focus on specific characters or writing styles if the user has difficulty with those areas. Step 4: The display unit visually displays the practice exercises adjusted by the adjustment unit using AR technology. The display unit visually displays areas for improvement in handwriting, and can do so visually using methods such as 3D display or animation. Step 5: The gamification section provides gamification elements to maintain user motivation. The gamification section can provide features such as leveling up, badge acquisition, and ranking, and can offer leveling up and badges based on a point system or achievement conditions.

[0069] (Example of form 2) The gamified autonomous feedback AI agent system according to an embodiment of the present invention is a system that not only analyzes handwritten characters in real time and provides immediate feedback, but also autonomously adjusts practice tasks according to the user's progress and maintains motivation. This system utilizes AR technology to visually display areas for improvement in handwritten characters, supporting intuitive understanding. Furthermore, gamification elements are incorporated to allow users to continue practicing handwritten characters while having fun. For example, when a user writes handwritten characters, the system analyzes those characters in real time. Based on the analysis results, immediate feedback is provided. For example, areas for improvement such as character shape, balance, and pen pressure are pointed out. This feedback is displayed visually using AR technology, so users can understand it intuitively. Next, the system autonomously adjusts practice tasks according to the user's progress. For example, if the user has difficulty with a particular character or writing style, tasks that focus on practicing that part are presented. In addition, gamification elements are incorporated to maintain user motivation. Specifically, these include leveling up, badge acquisition, and ranking functions, allowing users to continue practicing handwritten characters while having fun. This system targets students, business professionals, and anyone aiming to improve their handwriting. It can solve communication problems caused by illegible or messy handwriting, as well as the reduced reliability of handwritten documents. It also addresses the difficulty of maintaining motivation due to consistent practice. Thus, the system improves the quality of handwriting, facilitating smoother communication and increasing the reliability of handwritten documents. Furthermore, by utilizing AR technology, it provides an intuitive and effective learning experience, creating an environment where learning is enjoyable through gamification elements. This helps to make handwriting practice a habit. In summary, this gamified autonomous feedback AI agent system can improve the quality of handwriting, facilitating smoother communication and increasing the reliability of handwritten documents.

[0070] The gamification autonomous feedback AI agent system according to this embodiment comprises an analysis unit, a provision unit, an adjustment unit, a display unit, and a gamification unit. The analysis unit analyzes handwritten characters. The analysis unit analyzes, for example, the shape, balance, and pressure of the handwritten characters. To analyze the shape of handwritten characters, the analysis unit can evaluate the outline and line thickness of the characters. To analyze the balance of handwritten characters, the analysis unit can evaluate the placement and inclination of the characters. To analyze the pressure of handwritten characters, the analysis unit can evaluate the strength and changes in the pressure. The provision unit provides feedback based on the results analyzed by the analysis unit. For example, the provision unit points out areas for improvement in the characters based on the analysis results. The provision unit can point out areas for improvement in the shape, balance, and pressure of the characters. The provision unit can provide feedback in the form of text, audio, visuals, etc. The adjustment unit adjusts the practice tasks based on the feedback provided by the provision unit. For example, the adjustment unit adjusts the practice tasks according to the user's progress. The adjustment unit can present tasks that focus on specific characters or writing styles if the user has difficulty with those areas. The adjustment unit can consider the number of practice sessions and achievement level to evaluate the user's progress. The display unit visually displays the practice tasks adjusted by the adjustment unit using AR technology. The display unit visually displays, for example, areas for improvement in handwriting. The display unit can visually display information using methods such as 3D display or animation. The gamification unit provides gamification elements to maintain user motivation. The gamification unit provides features such as leveling up, badge acquisition, and ranking functions. The gamification unit can provide leveling up based on a point system or achievement conditions. The gamification unit can provide badges based on the completion of specific tasks or a certain number of practice sessions. The gamification unit can provide ranking functions such as point rankings and achievement rankings.As a result, the gamified autonomous feedback AI agent system according to this embodiment can analyze handwritten characters, provide feedback, adjust practice tasks, provide visual display, and maintain motivation.

[0071] The analysis unit analyzes handwritten characters. For example, it analyzes the shape, balance, and pressure of the handwritten characters. Specifically, to analyze the shape of handwritten characters, it can evaluate the outline of the characters and the thickness of the lines. This involves a process that uses image processing technology to extract the outline of the characters and quantifies the thickness of the lines and the smoothness of the curves. Furthermore, to analyze the balance of the characters, it can evaluate the placement and inclination of the characters. For example, it calculates the center point of the characters and evaluates how the center of gravity of the entire character is positioned. Regarding the inclination of the characters, it measures the angle of each part of the character and evaluates it by comparing it with a standard way of writing. For pen pressure analysis, a pen pressure sensor can be used to evaluate the strength and changes in pen pressure. The pen pressure sensor measures the pressure applied while writing in real time and transmits this data to the analysis unit. The analysis unit integrates this data to perform a comprehensive evaluation of the handwritten characters. For example, it can identify specific problems such as distorted character shapes, poor balance, or uneven pen pressure. This allows the analysis unit to perform a detailed analysis of handwritten characters, clearly identifying the characteristics of the user's writing style and areas for improvement.

[0072] The service provider provides feedback based on the results analyzed by the analysis unit. For example, the service provider can point out areas for improvement in handwriting based on the analysis results. Specifically, it can point out areas for improvement in the shape and balance of characters, as well as pen pressure. The service provider can provide feedback in various formats, such as text, audio, and visual. For example, if the shape of the characters is distorted, it can explain specific correction methods in text and give instructions in audio. As visual feedback, it can display the correct character shape alongside the character written by the user, visually showing which parts are different. Furthermore, the service provider can adjust the content of the feedback according to the user's progress. For example, it can provide basic feedback to first-time users and more detailed and advanced feedback to experienced users. The service provider can also adjust the frequency and timing of feedback. For example, it can support effective learning by providing feedback after the user has completed a certain amount of practice. In this way, the service provider can provide users with appropriate and effective feedback and help them improve their handwriting.

[0073] The adjustment unit adjusts practice tasks based on feedback provided by the provision unit. For example, the adjustment unit adjusts practice tasks according to the user's progress. Specifically, if a user has difficulty with a particular character or writing style, it can present tasks that focus on practicing that area. The adjustment unit can consider the number of practice sessions and the level of achievement to evaluate the user's progress. For example, if a user practices a particular character many times but shows no improvement, the adjustment unit can suggest a new way to practice that character. Also, if a user reaches a certain level of achievement, it can present tasks at the next level. The adjustment unit analyzes the user's practice data and creates an optimal practice plan for each individual user. For example, if a user's pen pressure is uneven, it can provide special practice tasks to make the pen pressure even. Also, if a user's handwriting is unbalanced, it can provide practice tasks to improve the balance. In this way, the adjustment unit can provide practice tasks that meet the individual needs of the user and support effective learning.

[0074] The display unit visually displays practice tasks adjusted by the adjustment unit using AR technology. For example, the display unit visually displays areas for improvement in handwriting. Specifically, it can use AR technology to overlay the correct character shape onto the character written by the user. This allows the user to practice while comparing their handwriting with the correct character. The display unit can also visually display information using methods such as 3D display and animation. For example, it can display the stroke order of characters and changes in pen pressure with animation, allowing the user to visually understand the correct way to write. Furthermore, the display unit can visually display the user's progress. For example, it can display tasks the user has completed and badges they have earned, providing visual feedback to maintain motivation. In this way, the display unit can provide users with visually easy-to-understand feedback and support effective learning.

[0075] The Gamification Department provides gamification elements to maintain user motivation. For example, it provides features such as level-ups, badge acquisition, and rankings. Specifically, it can offer level-ups based on point systems and achievement conditions. Users earn points each time they complete a specific task, and level up once a certain number of points are accumulated. It can also provide badges based on the completion of specific tasks or a certain number of practice sessions. For example, a "Beginner Badge" can be earned for completing a first task, and an "Advanced Badge" for completing a more difficult task. Furthermore, ranking features such as point rankings and achievement rankings can be provided. This allows users to compete with other users while practicing, thus maintaining motivation. The Gamification Department can analyze user practice data and provide gamification elements optimized for each individual user. For example, if a user is struggling with a particular task, it can offer special rewards for completing that task. This allows the Gamification Department to increase user motivation and support continuous learning.

[0076] The analysis unit can analyze the shape, balance, and pressure of handwritten characters. For example, to analyze the shape of handwritten characters, the analysis unit evaluates the outline and line thickness of the characters. To analyze the balance of handwritten characters, the analysis unit can also evaluate the placement and inclination of the characters. To analyze the pressure of handwritten characters, the analysis unit can also evaluate the strength and changes in pen pressure. This enables detailed analysis of handwritten characters. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input handwritten character image data into a generating AI and have the generating AI perform the analysis of the character shape, balance, and pen pressure.

[0077] The service provider can point out areas for improvement in handwriting based on the analysis results. For example, the service provider can point out areas for improvement in the shape, balance, and pressure of the characters based on the analysis results. To point out areas for improvement in the shape of the characters, the service provider can suggest corrections to the outline and line thickness of the characters. To point out areas for improvement in the balance of the characters, the service provider can also suggest corrections to the placement and tilt of the characters. To point out areas for improvement in pressure, the service provider can also suggest corrections to the strength and variation of the pressure applied to the pen. This allows for an improvement in the quality of the user's handwriting by pointing out areas for improvement in their handwriting. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI point out areas for improvement in the characters.

[0078] The adjustment unit can adjust practice tasks according to the user's progress. For example, the adjustment unit may present practice tasks that focus on specific characters or writing styles depending on the user's progress. The adjustment unit may consider the number of practice sessions and the degree of achievement in order to evaluate the user's progress. The adjustment unit can also adjust the difficulty and content of the practice tasks based on the user's progress. This makes it possible to adjust practice tasks according to the user's progress. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input user progress data into a generating AI and have the generating AI perform the adjustment of practice tasks.

[0079] The display unit can visually display areas for improvement in handwritten characters using AR technology. For example, the display unit can visually display areas for improvement in handwritten characters using methods such as 3D display or animation. The display unit can utilize AR technology to visually display areas for improvement in the shape, balance, and pressure of handwritten characters. The display unit can also visually display areas for improvement so that users can understand them intuitively. This deepens user understanding by visually displaying areas for improvement in handwritten characters. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input areas for improvement in handwritten characters into a generating AI and have the generating AI perform the visual display.

[0080] The gamification unit can provide features such as level-ups, badge acquisition, and rankings. For example, the gamification unit can provide level-ups based on a point system or achievement conditions. The gamification unit can provide badges based on the completion of specific tasks or a certain number of practice sessions. The gamification unit can also provide ranking functions such as point rankings and achievement rankings. This provides gamification elements to maintain user motivation. Some or all of the above processes in the gamification unit may be performed using AI, for example, or not using AI. For example, the gamification unit can input user achievement data into a generating AI and have the generating AI perform level-ups, badge acquisition, and provide ranking functions.

[0081] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can loosen the accuracy of the analysis and provide gentler feedback. If the user is relaxed, the analysis unit can also increase the accuracy of the analysis and provide more detailed feedback. If the user is in a hurry, the analysis unit can adjust the accuracy of the analysis to provide quicker feedback. This makes it possible to adjust the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis accuracy.

[0082] The analysis unit can correct the analysis results when analyzing handwritten characters, taking into account the user's writing speed. For example, if the user writes quickly, the analysis unit can relax its evaluation of character shape and balance, taking the writing speed into consideration. If the user writes slowly, the analysis unit can also perform a detailed analysis, taking the writing speed into consideration, and provide highly accurate feedback. If the user writes at a constant speed, the analysis unit can also correct the analysis results based on the writing speed and provide appropriate feedback. This makes it possible to correct the analysis results according to the user's writing speed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's writing speed data into a generating AI and have the generating AI perform the correction of the analysis results.

[0083] The analysis unit can perform handwritten character analysis while considering the user's writing environment (e.g., paper type and pen type). For example, if the user writes on rough paper, the analysis unit can adjust the evaluation of character shape and balance considering the paper type. If the user writes on smooth paper, the analysis unit can also perform a detailed analysis considering the paper type and provide highly accurate feedback. If the user uses a specific pen, the analysis unit can also correct the analysis results considering the pen type and provide appropriate feedback. This enables analysis tailored to the user's writing environment. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's writing environment data into a generating AI and have the generating AI perform the analysis.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. This makes it possible to adjust the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0085] The analysis unit can improve the accuracy of handwritten character analysis by referring to the user's past writing data. For example, the analysis unit can improve the accuracy of analysis based on data of characters the user has written in the past. The analysis unit can also analyze the user's past writing data, find specific patterns, and incorporate them into the analysis. The analysis unit can also refer to the user's past writing data to provide individualized feedback. This makes it possible to improve the accuracy of analysis based on the user's past writing data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past writing data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0086] The analysis unit can perform handwriting analysis while considering the user's writing style (e.g., whether they are right-handed or left-handed). For example, if the user is right-handed, the analysis unit will use an analysis method suitable for right-handed users. If the user is left-handed, the analysis unit can also use an analysis method suitable for left-handed users. If the user has a specific writing style, the analysis unit can also perform analysis according to that style. This makes it possible to perform analysis according to the user's writing style. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's writing style data into a generating AI and have the generating AI perform the analysis.

[0087] The service provider can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is stressed, the service provider will provide feedback in gentle language. If the user is relaxed, the service provider may also provide detailed feedback. If the user is in a hurry, the service provider may also provide concise feedback. This allows for adjustment of the feedback content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the content of the feedback.

[0088] The service provider can adjust the level of detail in the feedback provided according to the user's level of understanding. For example, if the user has a low level of understanding, the service provider can provide feedback that includes detailed explanations. If the user has a high level of understanding, the service provider can also provide concise feedback. The service provider can also adjust the content of the feedback according to the user's level of understanding. This makes it possible to adjust the level of detail in the feedback according to the user's level of understanding. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input user understanding data into a generating AI and have the generating AI perform the adjustment of the level of detail in the feedback.

[0089] The service provider can provide optimal feedback by referring to the user's learning history when providing feedback. For example, the service provider can provide optimal feedback based on the user's past learning history. The service provider can also analyze the user's learning history, find specific patterns, and reflect them in the feedback. The service provider can also provide individualized feedback by referring to the user's learning history. This makes it possible to provide optimal feedback based on the user's learning history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's learning history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0090] The service provider can estimate the user's emotions and adjust the timing of feedback based on the estimated emotions. For example, if the user is stressed, the service provider may delay the timing of feedback. If the user is relaxed, the service provider may also speed up the timing of feedback. If the user is in a hurry, the service provider may also provide feedback quickly. This makes it possible to adjust the timing of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of feedback timing.

[0091] The feedback provider can adjust the way feedback is presented according to the user's age and learning level. For example, if the user is young, the provider can provide feedback in friendly language. If the user is older, the provider can also provide feedback in polite language. The provider can also adjust the way feedback is presented according to the user's learning level. This makes it possible to adjust the way feedback is presented according to the user's age and learning level. Some or all of the above processing in the feedback provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's age and learning level data into a generating AI and have the generating AI perform the adjustment of the way feedback is presented.

[0092] The service provider can customize the content of the feedback based on the user's goals when providing feedback. For example, the service provider customizes the content of the feedback according to the user's goals. If the user has a specific goal, the service provider can also provide feedback tailored to that goal. The service provider can also adjust the content of the feedback based on the user's goals. This makes it possible to customize the content of the feedback based on the user's goals. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's goal data into a generating AI and have the generating AI perform the customization of the feedback content.

[0093] The adjustment unit can estimate the user's emotions and adjust the difficulty level of the practice tasks based on the estimated emotions. For example, if the user is feeling stressed, the adjustment unit can lower the difficulty level of the practice tasks. If the user is relaxed, the adjustment unit can also raise the difficulty level of the practice tasks. If the user is in a hurry, the adjustment unit can adjust the difficulty level of the practice tasks to allow for quick completion. This makes it possible to adjust the difficulty level of the practice tasks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the difficulty level of the practice tasks.

[0094] The adjustment unit can select the optimal practice task by referring to the user's past practice history when adjusting practice tasks. For example, the adjustment unit selects the optimal practice task based on the user's past practice history. The adjustment unit can also analyze the user's practice history, find specific patterns, and reflect them in the practice tasks. The adjustment unit can also provide individual practice tasks by referring to the user's practice history. This makes it possible to select the optimal practice task based on the user's past practice history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's past practice history data into a generating AI and have the generating AI perform the selection of the optimal practice task.

[0095] The adjustment unit can adjust the amount of practice tasks according to the user's learning pace when adjusting practice tasks. For example, if the user's learning pace is fast, the adjustment unit can increase the amount of tasks. If the user's learning pace is slow, the adjustment unit can also decrease the amount of tasks. The adjustment unit can also adjust the amount of tasks according to the user's learning pace. This makes it possible to adjust the amount of tasks according to the user's learning pace. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's learning pace data into a generating AI and have the generating AI perform the adjustment of the amount of tasks.

[0096] The adjustment unit can estimate the user's emotions and adjust the order of practice tasks based on the estimated emotions. For example, if the user is stressed, the adjustment unit can start with easy tasks. If the user is relaxed, the adjustment unit can also start with difficult tasks. If the user is in a hurry, the adjustment unit can also start with important tasks. This makes it possible to adjust the order of practice tasks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the order of practice tasks.

[0097] The adjustment unit can customize the content of practice tasks based on the user's interests when adjusting them. For example, the adjustment unit can customize the content of tasks according to the user's interests. If the user has a specific interest, the adjustment unit can also provide tasks tailored to that interest. The adjustment unit can also adjust the content of tasks based on the user's interests. This makes it possible to customize the content of tasks based on the user's interests. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input user interest data into a generating AI and have the generating AI perform the customization of the task content.

[0098] The adjustment unit can adjust the timing of assignment submissions according to the user's daily routine when adjusting practice assignments. For example, the adjustment unit adjusts the timing of assignment submissions according to the user's daily routine. The adjustment unit can also extend the submission deadline if the user is busy. The adjustment unit can also shorten the submission deadline if the user has time. This makes it possible to adjust the timing of assignment submissions according to the user's daily routine. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's daily routine data into a generating AI and have the generating AI perform the adjustment of the assignment submission timing.

[0099] The display unit can estimate the user's emotions and adjust the displayed content based on the estimated emotions. For example, if the user is stressed, the display unit can provide simple content. If the user is relaxed, the display unit can also provide detailed content. If the user is in a hurry, the display unit can provide concise content. This makes it possible to adjust the displayed content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the displayed content.

[0100] The display unit can adjust the display method when displaying information, taking into account the user's visual characteristics (e.g., color blindness). For example, if the user has color blindness, the display unit can enhance the color contrast. If the user has a visual impairment, the display unit can also display information in a larger font size. The display unit can also adjust the display method according to the user's visual characteristics. This makes it possible to adjust the display method according to the user's visual characteristics. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's visual characteristics data into a generating AI and have the generating AI perform the adjustment of the display method.

[0101] The display unit can adjust the display method when displaying information, taking into account the user's device characteristics (e.g., screen size and resolution). For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. If the user is using a tablet, the display unit can also provide a display method optimized for a larger screen. If the user is using a high-resolution device, the display unit can also provide a display method that corresponds to the resolution. This makes it possible to adjust the display method according to the user's device characteristics. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user device characteristic data into a generating AI and have the generating AI perform the adjustment of the display method.

[0102] The display unit can estimate the user's emotions and adjust the timing of the display based on the estimated emotions. For example, if the user is stressed, the display unit may delay the display timing. If the user is relaxed, the display unit may speed up the display timing. If the user is in a hurry, the display unit may display quickly. This makes it possible to adjust the display timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display timing.

[0103] The display unit can select the optimal display method by referring to the user's past viewing history when displaying information. For example, the display unit can select the optimal display method based on the user's past viewing history. The display unit can also analyze the user's viewing history, find specific patterns, and reflect them in the display. The display unit can also refer to the user's viewing history and provide individual display methods. This makes it possible to select the optimal display method based on the user's past viewing history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past viewing history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0104] The display unit can adjust the display method when displaying information, taking into account the user's environment (e.g., brightness and volume). For example, the display unit adjusts the brightness of the display if the user is in a bright place. The display unit can also adjust the brightness of the display if the user is in a dark place. The display unit can also adjust the display method according to the user's environment. This makes it possible to adjust the display method according to the user's environment. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user environment data into a generating AI and have the generating AI perform the adjustment of the display method.

[0105] The gamification unit can estimate the user's emotions and adjust the content of gamification elements based on the estimated user emotions. For example, if the user is stressed, the gamification unit can provide simple game elements. If the user is relaxed, the gamification unit can also provide complex game elements. If the user is in a hurry, the gamification unit can also provide game elements that can be completed quickly. This makes it possible to adjust the content of gamification elements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the gamification unit may be performed using AI, for example, or not using AI. For example, the gamification unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the gamification elements.

[0106] The gamification unit can provide optimal gamification elements by referring to the user's past game history when providing gamification elements. For example, the gamification unit can provide optimal game elements based on the user's past game history. The gamification unit can also analyze the user's game history, find specific patterns, and reflect them in the game elements. The gamification unit can also provide individual game elements by referring to the user's game history. This makes it possible to provide optimal gamification elements based on the user's past game history. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without AI. For example, the gamification unit can input the user's past game history data into a generating AI and have the generating AI perform the task of providing optimal game elements.

[0107] The gamification unit can add elements that stimulate the user's competitive spirit when providing gamification elements. For example, the gamification unit can provide a ranking function to stimulate the user's competitive spirit. The gamification unit can also provide badges and trophies to stimulate the user's competitive spirit. The gamification unit can also provide a function to compete against other users to stimulate the user's competitive spirit. This makes it possible to add elements that stimulate the user's competitive spirit. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without using AI. For example, the gamification unit can input user competitive spirit data into a generating AI and have the generating AI execute the addition of elements that stimulate competitive spirit.

[0108] The gamification unit can estimate the user's emotions and adjust the display method of gamification elements based on the estimated user emotions. For example, if the user is stressed, the gamification unit can provide a simple display method. If the user is relaxed, the gamification unit can also provide a detailed display method. If the user is in a hurry, the gamification unit can also provide a concise display method. This makes it possible to adjust the display method of gamification elements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the gamification unit may be performed using AI, for example, or not using AI. For example, the gamification unit can input user emotion data into a generative AI and have the generative AI adjust the display method of gamification elements.

[0109] The gamification unit can customize the content of gamification elements based on the user's interests when providing them. For example, the gamification unit can customize the content of game elements according to the user's interests. The gamification unit can also provide game elements tailored to the user's specific interests. The gamification unit can also adjust the content of game elements based on the user's interests. This makes it possible to customize the content of gamification elements based on the user's interests. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without AI. For example, the gamification unit can input user interest data into a generating AI and have the generating AI perform the customization of the element content.

[0110] The gamification unit can add collaboration features with the user's friends and family when providing gamification elements. For example, the gamification unit can provide collaboration features to allow the user to enjoy game elements together with their friends and family. The gamification unit can also provide ranking features to allow the user to compete with their friends and family. The gamification unit can also provide collaboration features to allow the user to work together with their friends and family to clear game elements. This makes it possible to add collaboration features with the user's friends and family. Some or all of the above processing in the gamification unit may be performed using AI, for example, or without AI. For example, the gamification unit can input collaboration data with the user's friends and family into a generating AI and have the generating AI execute the addition of collaboration features.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The analysis unit can adjust the analysis results of handwritten characters by taking into account the user's writing speed. For example, if the user writes quickly, the evaluation of character shape and balance can be relaxed to account for the writing speed. Conversely, if the user writes slowly, a detailed analysis can be performed to provide highly accurate feedback. Furthermore, if the user writes at a constant speed, the analysis results can be adjusted based on the writing speed to provide appropriate feedback. This makes it possible to adjust the analysis results according to the user's writing speed.

[0113] The system can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is stressed, it can provide feedback in gentle language. If the user is relaxed, it can provide detailed feedback. Furthermore, if the user is in a hurry, it can provide concise feedback. This allows for the adjustment of feedback content according to the user's emotions.

[0114] The adjustment unit can select the optimal task by referring to the user's past practice history. For example, it can select the optimal practice task based on the user's past practice history. It can also analyze the user's practice history, find specific patterns, and reflect them in the practice tasks. Furthermore, it can provide individual practice tasks by referring to the user's practice history. This makes it possible to select the optimal practice task based on the user's past practice history.

[0115] The display unit can estimate the user's emotions and adjust the displayed content based on those emotions. For example, if the user is stressed, it can provide simple content. If the user is relaxed, it can provide detailed content. Furthermore, if the user is in a hurry, it can provide concise content. This allows for adjustment of the displayed content according to the user's emotions.

[0116] The gamification component can add elements that stimulate users' competitive spirit. For example, it can provide a ranking function to stimulate users' competitive spirit. It can also provide badges and trophies to stimulate users' competitive spirit. Furthermore, it can provide a function to compete against other users to stimulate users' competitive spirit. This makes it possible to add elements that stimulate users' competitive spirit.

[0117] The analysis unit can analyze handwritten characters while considering the user's writing environment (e.g., paper type and pen type). For example, if the user writes on rough paper, the system can adjust the evaluation of character shape and balance by considering the paper type. If the user writes on smooth paper, the system can perform a detailed analysis considering the paper type and provide highly accurate feedback. Furthermore, if the user uses a specific pen, the system can correct the analysis results by considering the pen type and provide appropriate feedback. This enables analysis tailored to the user's writing environment.

[0118] The feedback system can adjust the level of detail in the feedback provided according to the user's level of understanding. For example, if the user has a low level of understanding, it can provide feedback that includes detailed explanations. If the user has a high level of understanding, it can provide concise feedback. Furthermore, it can adjust the content of the feedback according to the user's level of understanding. This makes it possible to adjust the level of detail in the feedback according to the user's level of understanding.

[0119] The adjustment unit can estimate the user's emotions and adjust the difficulty level of the practice tasks based on those emotions. For example, if the user is feeling stressed, the difficulty level of the practice tasks can be lowered. If the user is relaxed, the difficulty level of the practice tasks can be increased. Furthermore, if the user is in a hurry, the difficulty level of the practice tasks can be adjusted to allow for quick completion. This makes it possible to adjust the difficulty level of practice tasks in accordance with the user's emotions.

[0120] The display unit can adjust its display method to take into account the user's visual characteristics (e.g., color blindness). For example, if the user has color blindness, the display can be enhanced with increased color contrast. If the user has a visual impairment, the display can be shown in a larger font size. Furthermore, the display method can be adjusted according to the user's visual characteristics. This makes it possible to adjust the display method according to the user's visual characteristics.

[0121] The gamification unit can estimate the user's emotions and adjust the display method of gamification elements based on those emotions. For example, if the user is stressed, a simple display method can be provided. If the user is relaxed, a more detailed display method can be provided. Furthermore, if the user is in a hurry, a concise display method can be provided. This makes it possible to adjust the display method of gamification elements according to the user's emotions.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The analysis unit analyzes the handwritten characters. The analysis unit analyzes the shape, balance, and pressure of the handwritten characters, and can evaluate the outline of the characters, the thickness of the lines, their placement and inclination, and the strength and changes in the pressure. Step 2: The service provider provides feedback based on the results analyzed by the analysis unit. The service provider can point out areas for improvement in character shape, balance, and pen pressure, and can provide feedback in various formats such as text, audio, and visuals. Step 3: The adjustment unit adjusts the practice tasks based on the feedback provided by the provision unit. The adjustment unit adjusts the practice tasks according to the user's progress and can present tasks that focus on specific characters or writing styles if the user has difficulty with those areas. Step 4: The display unit visually displays the practice exercises adjusted by the adjustment unit using AR technology. The display unit visually displays areas for improvement in handwriting, and can do so visually using methods such as 3D display or animation. Step 5: The gamification section provides gamification elements to maintain user motivation. The gamification section can provide features such as leveling up, badge acquisition, and ranking, and can offer leveling up and badges based on a point system or achievement conditions.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the analysis unit, provision unit, adjustment unit, display unit, and gamification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit uses the camera 42 of the smart device 14 to capture handwritten characters, which are then analyzed by the specific processing unit 290 of the data processing unit 12. The provision unit generates feedback based on the analysis results and provides it to the user by the control unit 46A of the smart device 14. The adjustment unit adjusts the practice tasks based on the feedback from the provision unit and is implemented by the specific processing unit 290 of the data processing unit 12. The display unit displays the adjusted practice tasks on the display 40A of the smart device 14 using AR technology. The gamification unit provides elements to maintain user motivation and is implemented by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

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

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the analysis unit, provision unit, adjustment unit, display unit, and gamification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit uses the camera 42 of the smart glasses 214 to capture handwritten characters, which are then analyzed by the identification processing unit 290 of the data processing unit 12. The provision unit generates feedback based on the analysis results and provides it to the user by the control unit 46A of the smart glasses 214. The adjustment unit adjusts the practice tasks based on the feedback from the provision unit and is implemented by the identification processing unit 290 of the data processing unit 12. The display unit displays the adjusted practice tasks on the display of the smart glasses 214 using AR technology. The gamification unit provides elements to maintain user motivation and is implemented by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

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

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the analysis unit, provision unit, adjustment unit, display unit, and gamification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit uses the camera 42 of the headset terminal 314 to capture handwritten characters, which are then analyzed by the specific processing unit 290 of the data processing unit 12. The provision unit generates feedback based on the analysis results and provides it to the user by the control unit 46A of the headset terminal 314. The adjustment unit adjusts the practice tasks based on the feedback from the provision unit and is implemented by the specific processing unit 290 of the data processing unit 12. The display unit displays the adjusted practice tasks on the display 343 of the headset terminal 314 using AR technology. The gamification unit provides elements to maintain user motivation and is implemented by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the analysis unit, provision unit, adjustment unit, display unit, and gamification unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit uses the camera 42 of the robot 414 to photograph handwritten characters, which are then analyzed by the specific processing unit 290 of the data processing unit 12. The provision unit generates feedback based on the analysis results and provides it to the user by the control unit 46A of the robot 414. The adjustment unit adjusts the practice tasks based on the feedback from the provision unit and is implemented by the specific processing unit 290 of the data processing unit 12. The display unit displays the adjusted practice tasks on the display of the robot 414 using AR technology. The gamification unit provides elements to maintain user motivation and is implemented by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0177] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) An analysis unit that analyzes handwritten characters, A providing unit that provides feedback based on the results of the analysis performed by the aforementioned analysis unit, An adjustment unit that adjusts practice tasks based on the feedback provided by the aforementioned provisioning unit, A display unit visually displays the practice tasks adjusted by the adjustment unit using AR technology, It includes a gamification section that provides gamification elements to maintain user motivation. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyzes the shape, balance, and pressure of handwritten characters. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Based on the analysis results, we will point out areas for improvement in the typography. The system described in Appendix 1, characterized by the features described herein. (Note 4) The adjustment unit is, Adjust practice tasks according to the user's progress. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is Using AR technology, we visually display areas for improvement in handwritten text. The system described in Appendix 1, characterized by the features described herein. (Note 6) The gamification unit is, It offers features such as leveling up, earning badges, and rankings. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing handwritten characters, the analysis results are corrected to take into account the user's writing speed. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing handwritten characters, the analysis takes into account the user's writing environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing handwritten characters, the system improves analysis accuracy by referencing the user's past handwriting data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing handwritten text, the analysis takes into account the user's writing style. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, When providing feedback, adjust the level of detail in the feedback according to the user's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing feedback, we refer to the user's learning history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, It estimates the user's emotions and adjusts the timing of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing feedback, adjust the way the feedback is presented according to the user's age and learning level. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing feedback, customize the content of the feedback based on the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 19) The adjustment unit is, The system estimates the user's emotions and adjusts the difficulty level of the practice tasks based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The adjustment unit is, When adjusting practice tasks, the system selects the most suitable task by referring to the user's past practice history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The adjustment unit is, When adjusting practice assignments, the amount of assignments is adjusted according to the user's learning pace. The system described in Appendix 1, characterized by the features described herein. (Note 22) The adjustment unit is, The system estimates the user's emotions and adjusts the order of practice tasks based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The adjustment unit is, When adjusting practice tasks, customize the content of the tasks based on the user's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 24) The adjustment unit is, When adjusting practice assignments, we adjust the submission timing of assignments according to the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is It estimates the user's emotions and adjusts the displayed content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is When displaying content, the display method is adjusted to take into account the user's visual characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is When displaying content, the display method is adjusted to take into account the user's device characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is It estimates the user's emotions and adjusts the timing of displays based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is When displaying content, the system selects the optimal display method by referring to the user's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is When displaying content, the display method is adjusted to take the user's environment into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The gamification unit is, The system estimates the user's emotions and adjusts the content of gamification elements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The gamification unit is, When providing gamification elements, refer to the user's past game history to provide the most suitable elements. The system described in Appendix 1, characterized by the features described herein. (Note 33) The gamification unit is, When providing gamification elements, add elements that stimulate users' competitive spirit. The system described in Appendix 1, characterized by the features described herein. (Note 34) The gamification unit is, It estimates the user's emotions and adjusts how gamification elements are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The gamification unit is, When providing gamification elements, customize the content of those elements based on the user's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 36) The gamification unit is, When providing gamification elements, add a feature that allows users to connect with their friends and family. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes handwritten characters, A providing unit that provides feedback based on the results of the analysis performed by the aforementioned analysis unit, An adjustment unit that adjusts practice tasks based on the feedback provided by the aforementioned provisioning unit, A display unit visually displays the practice tasks adjusted by the adjustment unit using AR technology, It includes a gamification section that provides gamification elements to maintain user motivation. A system characterized by the following features.

2. The aforementioned analysis unit, Analyzes the shape, balance, and pressure of handwritten characters. The system according to feature 1.

3. The aforementioned supply unit is, Based on the analysis results, we will point out areas for improvement in the typography. The system according to feature 1.

4. The adjustment unit is, Adjust practice tasks according to the user's progress. The system according to feature 1.

5. The aforementioned display unit is Using AR technology, we visually display areas for improvement in handwritten text. The system according to feature 1.

6. The gamification unit is, It offers features such as leveling up, earning badges, and rankings. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing handwritten characters, the analysis results are corrected to take into account the user's writing speed. The system according to feature 1.

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