system

Abstract

The system according to this embodiment aims to efficiently support children's improvement in handwriting. [Solution] The system according to this embodiment comprises a selection unit, a shooting unit, an analysis unit, an editing unit, and a provision unit. The selection unit allows the parent to select the font they want their child to master. The shooting unit photographs the characters written by the child. The analysis unit analyzes the captured image and compares it with the selected font to determine which parts of the child's writing need improvement. The editing unit makes corrections based on the analysis results. The provision unit provides the editing results to the parent and child.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a method for efficiently supporting the improvement of children's writing skills has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently support the improvement of children's writing skills.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a selection unit, a shooting unit, an analysis unit, an editing unit, and a provision unit. The selection unit allows the parent to select a font that they want their child to master. The shooting unit photographs the characters written by the child. The analysis unit analyzes the captured image and compares it with the selected font to determine which parts of the child's writing need improvement. The editing unit makes corrections based on the analysis results. The provision unit provides the editing results to the parent and child. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently support children's improvement in handwriting. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The handwriting improvement support system according to an embodiment of the present invention is a system that uses a generating AI to support the improvement of a child's handwriting. In this handwriting improvement support system, a parent selects a font that they want their child to master, the child writes characters according to that font, and the written characters are photographed with a smartphone or tablet camera. The photographed image is sent to the generating AI, which analyzes which parts of the child's handwriting need improvement by comparing them with the selected font. Based on the analysis results, the generating AI corrects the handwriting like a teacher, adding red lines and comments to the parts that need improvement and showing how to correct them. The corrected results are provided to the parent and child, and the child corrects the characters according to the generating AI's advice and rewrites them. By repeating this process, the child's handwriting gradually improves. This system allows parents to support their children's handwriting improvement, and allows children to practice handwriting in a fun and effective way. For example, a parent selects a font such as block script or semi-cursive script through a smartphone or tablet app. The selected font is sent to the generating AI and serves as the basis for analysis. Next, based on the photographed image, the generating AI evaluates the balance of the characters, the thickness of the lines, the angles, etc. For example, it analyzes how much the center line of a character is off-center and whether the line thickness is uniform. Specific examples of corrections include comments like, "Make this line a little thicker," or "Make this angle a little sharper." This makes it easier for children to understand specific correction methods. The AI's learning method utilizes past data to improve the accuracy of its feedback. For example, it learns what corrections were effective based on previously corrected data and incorporates this into future feedback. Furthermore, it collects feedback from parents and children, gathering opinions such as, "This advice was helpful," or "This part was difficult to understand," which is used to improve the AI. This allows the AI ​​to continuously learn and improve the accuracy of its feedback. Parents and children can receive more effective advice and support their children's handwriting improvement. In this way, the handwriting improvement support system allows parents to support their children's handwriting improvement, and children to practice handwriting in a fun and effective way.

[0029] The handwriting improvement support system according to this embodiment comprises a selection unit, a shooting unit, an analysis unit, a correction unit, and a provision unit. The selection unit selects a handwriting style that the parent wants the child to master. Examples of handwriting styles that the parent wants the child to master include, but are not limited to, regular script, semi-cursive script, cursive script, etc. The selection unit allows, for example, the parent to select the handwriting style through a smartphone or tablet app. The parent can select their desired handwriting style from multiple styles through the app's interface. The shooting unit photographs the characters written by the child. For example, a smartphone or tablet camera is used for shooting, but is not limited to this example. For example, the shooting unit photographs characters written by the child on paper using the smartphone or tablet camera. The captured image is transmitted to the analysis unit. The analysis unit analyzes the captured image and compares it with the selected handwriting style to determine which parts of the child's handwriting need improvement. The analysis unit evaluates the balance of the characters, the thickness of the lines, angles, etc., using, for example, a generation AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generating AI, and it improves the accuracy of feedback by utilizing past data. For example, the generating AI learns what kind of corrections were effective based on data that has been corrected in the past and reflects this in the next feedback. The correction unit corrects the work like a teacher with a red pen based on the analysis results. The correction unit generates comments such as, "Let's make this line a little thicker" or "Let's make this angle a little sharper." The generating AI generates comments that show specific correction methods based on the analysis results. The delivery unit provides the corrected results to the parents and children. The delivery unit provides the corrected results in the form of, for example, email, app notification, or printed materials. The delivery unit collects feedback from parents and children and uses it to improve the generating AI. For example, it collects opinions from parents and children such as, "This advice was helpful" or "This part was difficult to understand" and reflects this in improving the generating AI. In this way, the handwriting improvement support system according to the embodiment allows parents to support their children's handwriting improvement, and allows children to practice handwriting in a fun and effective way.

[0030] The selection function allows parents to select a font through a smartphone or tablet app. For example, a parent can select a font from multiple options through the app's interface. For instance, a parent can select fonts such as Kaisho (regular script), Gyosho (semi-cursive script), or Sosho (cursive script) from the app's menu. The selected font is sent to the generating AI to serve as the basis for analysis. This allows parents to easily select a font. Some or all of the above-described processes in the selection function may be performed using or without AI. For example, the selection function can input the font selected by the parent into the generating AI and have the generating AI perform font analysis.

[0031] The analysis unit can evaluate the balance of characters, line thickness, and angles based on the captured image. For example, the analysis unit evaluates the balance of characters, line thickness, and angles based on the captured image. For example, the analysis unit uses a generation AI to evaluate the balance of characters, line thickness, and angles. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and it improves the accuracy of feedback by utilizing past data. For example, the generation AI learns what kind of corrections were effective based on data that has been corrected in the past and reflects this in the next feedback. For example, the analysis unit analyzes how much the center line of the characters is off, whether the line thickness is uniform, etc. This makes it possible to evaluate the characters in detail. 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 captured image data into the generation AI and have the generation AI perform the evaluation of the balance of characters, line thickness, and angles.

[0032] The editing unit can generate comments such as, "Let's make this line 2mm thicker" or "Let's make this angle 5 degrees sharper." The editing unit generates comments such as, for example, "Let's make this line 2mm thicker" or "Let's make this angle 5 degrees sharper." The generating AI generates comments that show specific correction methods based on the analysis results. For example, the generating AI evaluates the balance of characters, line thickness, angles, etc., and generates comments that show specific correction methods. This allows for the provision of specific correction methods. Some or all of the above processing in the editing unit may be performed using AI, for example, or without AI. For example, the editing unit can input the analysis results into the generating AI and have the generating AI generate comments that show specific correction methods.

[0033] The analysis unit can improve the accuracy of feedback by utilizing past data. For example, the analysis unit can improve the accuracy of feedback by utilizing past data. The generation AI learns what kind of corrections were effective based on previously edited data and reflects this in the next feedback. For example, the generation AI applies an algorithm to improve the accuracy of feedback based on past analysis results and feedback history. This improves the accuracy of feedback. 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 past data into the generation AI and have the generation AI perform the task of improving the accuracy of feedback.

[0034] The service provider can collect feedback from parents and children and improve the algorithm of the generative AI. For example, the service provider collects feedback from parents and children and improves the algorithm of the generative AI. The service provider collects opinions from parents and children such as "This advice was helpful" or "This part was difficult to understand" and reflects them in improving the generative AI. For example, the service provider collects feedback and uses it as data to improve the algorithm of the generative AI. This allows the accuracy of the generative AI to continuously improve. 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 collected feedback data into the generative AI and have the generative AI perform algorithm improvements.

[0035] The selection unit can analyze past selection history and prioritize displaying fonts preferred by parents. For example, the selection unit can analyze past selection history and prioritize displaying fonts preferred by parents. For example, the selection unit can prioritize displaying fonts that parents have frequently selected in the past. The selection unit can also analyze fonts selected by parents during specific time periods and suggest fonts suitable for those times. Furthermore, the selection unit can suggest fonts suitable for specific events or seasons based on the parents' past selection history. This allows for the priority selection of fonts preferred by parents. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input past selection history data into a generating AI and have the generating AI perform the priority display of fonts.

[0036] The selection unit can suggest an appropriate font based on the child's learning progress when selecting a font. For example, when a child is writing for the first time, the selection unit suggests a basic block script font. If the child has some ability to write, the selection unit can also suggest semi-cursive or cursive script fonts. Furthermore, if the child shows interest in a particular font, the selection unit can prioritize suggesting that font. This makes it possible to select a font that is appropriate for the child's learning progress. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the child's learning progress data into a generating AI and have the generating AI suggest fonts.

[0037] The selection unit can suggest region-specific fonts when selecting a font, taking into account the parent's geographical location information. For example, when selecting a font, the selection unit suggests region-specific fonts considering the parent's geographical location information. For example, if the parent is in Japan, the selection unit suggests a Japanese-style font. If the parent is in China, the selection unit can also suggest a Chinese-style font. Furthermore, if the parent is in Europe or America, the selection unit can also suggest a European-style font. This allows for the suggestion of region-specific fonts. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the parent's geographical location data into a generating AI and have the generating AI suggest region-specific fonts.

[0038] The selection unit can analyze the parent's social media activity when selecting a font and suggest relevant fonts. For example, the selection unit can analyze the parent's social media activity when selecting a font and suggest relevant fonts. For example, the selection unit can suggest fonts that the parent frequently uses on social media. The selection unit can also analyze the fonts of accounts that the parent follows and suggest relevant fonts. Furthermore, the selection unit can analyze the fonts of images that the parent has shared on social media and suggest relevant fonts. This makes it possible to suggest fonts based on social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the parent's social media activity data into a generating AI and have the generating AI perform font suggestions.

[0039] The camera unit can automatically adjust the lighting and angle during shooting to acquire an appropriate image. For example, the camera unit can automatically adjust the light intensity during shooting to ensure optimal brightness. It can also automatically adjust the camera angle to ensure the optimal viewpoint. Furthermore, the camera unit can automatically adjust the background brightness to make the subject stand out. This allows for the acquisition of an optimal image. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can have a generating AI perform the adjustment of lighting and angle.

[0040] The camera unit can detect the child's hand movements during shooting and display a guide to prevent blurring. For example, the camera unit can detect the child's hand movements during shooting and display guidelines to prevent blurring. The camera unit can also detect the child's hand movements and provide audio guidance to prevent blurring. Furthermore, the camera unit can detect the child's hand movements and provide vibration feedback to prevent blurring. This will enable the display of a guide to prevent blurring. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input the child's hand movement data into a generating AI and have the generating AI perform the display of the blur prevention guide.

[0041] The shooting unit can propose an appropriate shooting environment while considering the child's geographical location information. For example, if the child is outdoors, the shooting unit can propose a shooting environment that utilizes natural light. If the child is indoors, the shooting unit can also propose a shooting environment that utilizes artificial lighting. Furthermore, if the child is in a specific location, the shooting unit can propose a shooting environment suitable for that location. This ensures that the optimal shooting environment is proposed. Some or all of the above processing in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input the child's geographical location information data into a generating AI and have the generating AI propose a shooting environment.

[0042] The photography unit can analyze a child's social media activity during photography and suggest relevant photography methods. For example, the photography unit can analyze a child's social media activity during photography and suggest relevant photography methods. For example, the photography unit can analyze how images shared by a child on social media are photographed and suggest relevant photography methods. The photography unit can also analyze how accounts followed by a child are photographed and suggest relevant photography methods. Furthermore, the photography unit can analyze filters frequently used by a child on social media and suggest relevant photography methods. This results in the suggestion of photography methods based on social media activity. Some or all of the above processing in the photography unit may be performed using AI, for example, or without AI. For example, the photography unit can input the child's social media activity data into a generating AI and have the generating AI suggest photography methods.

[0043] The analysis unit can apply algorithms to analyze the details of characters during the analysis process. For example, the analysis unit can apply algorithms to analyze the details of characters. For example, the analysis unit can apply algorithms to analyze the thickness and angle of character lines in detail. The analysis unit can also apply algorithms to analyze the balance and placement of characters in detail. Furthermore, the analysis unit can apply algorithms to analyze the pen pressure and stroke order of characters in detail. This allows for detailed analysis of the details of 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 detailed character data into a generating AI and have the generating AI perform a detailed analysis.

[0044] The analysis unit can improve analysis accuracy by referring to past analysis data during analysis. For example, the analysis unit can improve analysis accuracy by referring to past analysis data during analysis. The analysis unit can evaluate the balance and line thickness of characters based on past analysis data. The analysis unit can also evaluate the angle and placement of characters based on past analysis data. Furthermore, the analysis unit can evaluate the pen pressure and stroke order of characters based on past analysis data. As a result, analysis accuracy is improved by referring to past analysis data. 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 past analysis data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0045] The analysis unit can perform analysis while considering the geographical distribution of characters. For example, the analysis unit can perform analysis while considering the geographical distribution of characters. For example, the analysis unit can analyze region-specific typefaces based on the geographical distribution of characters. The analysis unit can also analyze region-specific stroke order based on the geographical distribution of characters. Furthermore, the analysis unit can analyze region-specific pen pressure based on the geographical distribution of characters. This makes analysis based on geographical distribution possible. 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 geographical distribution data of characters into a generating AI and have the generating AI perform the analysis.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can evaluate the balance and line thickness of characters based on relevant literature. The analysis unit can also evaluate the angle and placement of characters based on relevant literature. Furthermore, the analysis unit can evaluate the pen pressure and stroke order of characters based on relevant literature. In this way, the accuracy of the analysis is improved by referring to relevant literature. 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 relevant literature data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0047] The editing unit can adjust the level of detail in the editing process based on the importance of the characters. For example, the editing unit can adjust the level of detail in the editing process based on the importance of the characters. For example, the editing unit can provide detailed editing comments for important characters. The editing unit can also provide simplified editing comments for less important characters. Furthermore, the editing unit can provide visually emphasized editing comments for highly important parts. This adjusts the level of detail in the editing process according to the importance of the characters. Some or all of the above processing in the editing unit may be performed using AI, for example, or without AI. For example, the editing unit can input character importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the editing process.

[0048] The editing unit can apply different editing algorithms depending on the category of the characters during editing. For example, the editing unit can apply a different editing algorithm depending on the category of the characters during editing. For example, the editing unit can apply a dedicated editing algorithm for regular script to regular script. The editing unit can also apply a dedicated editing algorithm for semi-cursive script to semi-cursive script. Furthermore, the editing unit can apply a dedicated editing algorithm for cursive script to cursive script. This makes it possible to edit according to the category of characters. Some or all of the above processing in the editing unit may be performed using AI, for example, or without using AI. For example, the editing unit can input character category data into a generating AI and have the generating AI execute the application of the editing algorithm.

[0049] The editing unit can determine the priority of editing based on the submission date of the text during the editing process. For example, the editing unit can prioritize editing newly submitted text. It can also postpone editing older text. Furthermore, if the submission date is related to a specific event or season, the editing unit can tailor the editing accordingly. This determines the editing priority according to the submission date of the text. Some or all of the above processing in the editing unit may be performed using AI, for example, or without AI. For example, the editing unit can input text submission date data into a generating AI and have the generating AI determine the editing priority.

[0050] The editing unit can adjust the order of editing based on the relationships between characters during editing. For example, the editing unit can adjust the order of editing based on the relationships between characters during editing. For example, the editing unit can prioritize editing important characters. The editing unit can also postpone editing less important characters. Furthermore, the editing unit can edit parts of a text that are highly related consecutively. This adjusts the order of editing according to the relationships between characters. Some or all of the above processing in the editing unit may be performed using AI, for example, or without AI. For example, the editing unit can input character relationship data into a generating AI and have the generating AI perform the adjustment of the editing order.

[0051] The service provider can provide appropriate feedback by referring to past feedback history at the time of delivery. For example, the service provider can provide appropriate feedback by referring to past feedback history at the time of delivery. For example, the service provider can provide feedback in a format preferred by parents based on past feedback history. The service provider can also provide feedback in a format that is easy for children to understand based on past feedback history. Furthermore, the service provider can provide effective feedback content based on past feedback history. This ensures that optimal feedback is provided based on past feedback 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 past feedback history data into a generating AI and have the generating AI perform the provision of feedback.

[0052] The service provider can customize the content of the feedback based on the parent's current situation at the time of delivery. For example, the service provider can customize the content of the feedback based on the parent's current situation at the time of delivery. For example, if the parent is busy, the service provider can provide concise feedback. Alternatively, if the parent is relaxed, the service provider can provide detailed feedback. Furthermore, if the parent is involved in a specific event or season, the service provider can provide timely feedback. This ensures that the feedback is tailored to the parent's current situation. 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 parent's current situation data into a generating AI and have the generating AI perform the feedback customization.

[0053] The service provider can provide appropriate feedback at the time of delivery, taking into account the geographical location information of the guardian. For example, the service provider can provide appropriate feedback at the time of delivery, taking into account the geographical location information of the guardian. For example, if the guardian is in Japan, the service provider can provide feedback in Japanese. The service provider can also provide feedback in Chinese if the guardian is in China. Furthermore, the service provider can provide feedback in English if the guardian is in Europe or the Americas. This ensures that optimal feedback is provided based on geographical location information. Some or all of the processing described above in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the guardian's geographical location data into a generating AI and have the generating AI perform the task of providing feedback.

[0054] The service provider can analyze the parent's social media activity and provide relevant feedback at the time of delivery. For example, the service provider can analyze the parent's social media activity and provide relevant feedback at the time of delivery. The service provider can provide feedback in the language the parent frequently uses on social media. The service provider can also analyze the feedback format of accounts the parent follows and provide relevant feedback. Furthermore, the service provider can provide relevant feedback based on information the parent has shared on social media. This ensures that feedback is provided based on social media activity. 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 parent's social media activity data into a generating AI and have the generating AI perform the task of providing feedback.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The selection function can also automatically generate practice tasks tailored to the child's learning progress based on the font selected by the parent. For example, if the regular script (Kaisho) is selected, the function will generate tasks that start with basic line practice and gradually progress to more complex characters. If the semi-cursive script (Gyosho) is selected, it can generate tasks that include practice of flowing lines. Furthermore, if the cursive script (Sosho) is selected, it can generate tasks that practice freehand brushstrokes. This allows for effective practice tailored to the child's learning progress.

[0057] The analysis unit can also regularly evaluate a child's writing progress and provide feedback tailored to that progress. For example, it can evaluate weekly progress and provide feedback highlighting areas for improvement and goals achieved. It can also evaluate monthly progress and provide advice for achieving long-term goals. Furthermore, it can evaluate progress toward specific events or exams and provide feedback to support necessary preparation. This helps maintain a child's motivation to learn and supports effective learning.

[0058] The analysis unit can analyze the characteristics of a child's handwriting and propose an individualized learning plan. For example, it can analyze the balance, line thickness, and angles of a child's handwriting and suggest areas for improvement. It can also analyze a child's handwriting habits and provide specific advice for improvement. Furthermore, it can suggest practice methods tailored to the child's learning style and support effective learning. This ensures that a learning plan is provided that meets the individual needs of each child.

[0059] The selection section can analyze the parent's past selection history and prioritize displaying fonts they prefer. For example, it can prioritize displaying fonts that the parent has frequently selected in the past. It can also analyze the fonts the parent has selected during specific time periods and suggest fonts suitable for those times. Furthermore, it can suggest fonts suitable for specific events or seasons based on the parent's past selection history. This allows for the prioritization of fonts preferred by the parent.

[0060] The camera unit can automatically adjust lighting and angles during shooting to acquire appropriate images. For example, it can automatically adjust the light intensity to ensure optimal brightness. It can also automatically adjust the camera angle to ensure the optimal viewpoint. Furthermore, it can automatically adjust the background brightness to make the subject stand out. This allows for the acquisition of optimal images.

[0061] The service provider can also provide appropriate feedback by considering the parent's geographical location at the time of delivery. For example, if the parent is in Japan, feedback will be provided in Japanese. If the parent is in China, feedback can be provided in Chinese. Furthermore, if the parent is in Europe or the Americas, feedback can be provided in English. This ensures that optimal feedback is provided based on geographical location information.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: In the selection section, parents choose the typeface they want their child to master. Parents select their preferred typeface from several options (e.g., regular script, semi-cursive script, cursive script, etc.) via a smartphone or tablet app. Step 2: The shooting unit photographs the writing of the child. A smartphone or tablet camera is used to photograph the writing the child has written on paper. The captured image is sent to the analysis unit. Step 3: The analysis unit analyzes the captured image and compares it to the selected font to determine which parts of the child's handwriting need improvement. Generative AI is used for the analysis, evaluating the balance of the characters, line thickness, angles, etc. The generative AI utilizes past data to improve the accuracy of the feedback. Step 4: The editing section makes corrections based on the analysis results. For example, it generates comments that indicate specific correction methods, such as "Let's make this line a little thicker" or "Let's make this angle a little sharper." Step 5: The delivery team provides the corrected work to the parents and children. Delivery methods include email, app notifications, and printed materials. In addition, feedback from parents and children is collected to help improve the generating AI.

[0064] (Example of form 2) The handwriting improvement support system according to an embodiment of the present invention is a system that uses a generating AI to support the improvement of a child's handwriting. In this handwriting improvement support system, a parent selects a font that they want their child to master, the child writes characters according to that font, and the written characters are photographed with a smartphone or tablet camera. The photographed image is sent to the generating AI, which analyzes which parts of the child's handwriting need improvement by comparing them with the selected font. Based on the analysis results, the generating AI corrects the handwriting like a teacher, adding red lines and comments to the parts that need improvement and showing how to correct them. The corrected results are provided to the parent and child, and the child corrects the characters according to the generating AI's advice and rewrites them. By repeating this process, the child's handwriting gradually improves. This system allows parents to support their children's handwriting improvement, and allows children to practice handwriting in a fun and effective way. For example, a parent selects a font such as block script or semi-cursive script through a smartphone or tablet app. The selected font is sent to the generating AI and serves as the basis for analysis. Next, based on the photographed image, the generating AI evaluates the balance of the characters, the thickness of the lines, the angles, etc. For example, it analyzes how much the center line of a character is off-center and whether the line thickness is uniform. Specific examples of corrections include comments like, "Make this line a little thicker," or "Make this angle a little sharper." This makes it easier for children to understand specific correction methods. The AI's learning method utilizes past data to improve the accuracy of its feedback. For example, it learns what corrections were effective based on previously corrected data and incorporates this into future feedback. Furthermore, it collects feedback from parents and children, gathering opinions such as, "This advice was helpful," or "This part was difficult to understand," which is used to improve the AI. This allows the AI ​​to continuously learn and improve the accuracy of its feedback. Parents and children can receive more effective advice and support their children's handwriting improvement. In this way, the handwriting improvement support system allows parents to support their children's handwriting improvement, and children to practice handwriting in a fun and effective way.

[0065] The handwriting improvement support system according to this embodiment comprises a selection unit, a shooting unit, an analysis unit, a correction unit, and a provision unit. The selection unit selects a handwriting style that the parent wants the child to master. Examples of handwriting styles that the parent wants the child to master include, but are not limited to, regular script, semi-cursive script, cursive script, etc. The selection unit allows, for example, the parent to select the handwriting style through a smartphone or tablet app. The parent can select their desired handwriting style from multiple styles through the app's interface. The shooting unit photographs the characters written by the child. For example, a smartphone or tablet camera is used for shooting, but is not limited to this example. For example, the shooting unit photographs characters written by the child on paper using the smartphone or tablet camera. The captured image is transmitted to the analysis unit. The analysis unit analyzes the captured image and compares it with the selected handwriting style to determine which parts of the child's handwriting need improvement. The analysis unit evaluates the balance of the characters, the thickness of the lines, angles, etc., using, for example, a generation AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generating AI, and it improves the accuracy of feedback by utilizing past data. For example, the generating AI learns what kind of corrections were effective based on data that has been corrected in the past and reflects this in the next feedback. The correction unit corrects the work like a teacher with a red pen based on the analysis results. The correction unit generates comments such as, "Let's make this line a little thicker" or "Let's make this angle a little sharper." The generating AI generates comments that show specific correction methods based on the analysis results. The delivery unit provides the corrected results to the parents and children. The delivery unit provides the corrected results in the form of, for example, email, app notification, or printed materials. The delivery unit collects feedback from parents and children and uses it to improve the generating AI. For example, it collects opinions from parents and children such as, "This advice was helpful" or "This part was difficult to understand" and reflects this in improving the generating AI. In this way, the handwriting improvement support system according to the embodiment allows parents to support their children's handwriting improvement, and allows children to practice handwriting in a fun and effective way.

[0066] The selection function allows parents to select a font through a smartphone or tablet app. For example, a parent can select a font from multiple options through the app's interface. For instance, a parent can select fonts such as Kaisho (regular script), Gyosho (semi-cursive script), or Sosho (cursive script) from the app's menu. The selected font is sent to the generating AI to serve as the basis for analysis. This allows parents to easily select a font. Some or all of the above-described processes in the selection function may be performed using or without AI. For example, the selection function can input the font selected by the parent into the generating AI and have the generating AI perform font analysis.

[0067] The analysis unit can evaluate the balance of characters, line thickness, and angles based on the captured image. For example, the analysis unit evaluates the balance of characters, line thickness, and angles based on the captured image. For example, the analysis unit uses a generation AI to evaluate the balance of characters, line thickness, and angles. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and it improves the accuracy of feedback by utilizing past data. For example, the generation AI learns what kind of corrections were effective based on data that has been corrected in the past and reflects this in the next feedback. For example, the analysis unit analyzes how much the center line of the characters is off, whether the line thickness is uniform, etc. This makes it possible to evaluate the characters in detail. 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 captured image data into the generation AI and have the generation AI perform the evaluation of the balance of characters, line thickness, and angles.

[0068] The editing unit can generate comments such as, "Let's make this line 2mm thicker" or "Let's make this angle 5 degrees sharper." The editing unit generates comments such as, for example, "Let's make this line 2mm thicker" or "Let's make this angle 5 degrees sharper." The generating AI generates comments that show specific correction methods based on the analysis results. For example, the generating AI evaluates the balance of characters, line thickness, angles, etc., and generates comments that show specific correction methods. This allows for the provision of specific correction methods. Some or all of the above processing in the editing unit may be performed using AI, for example, or without AI. For example, the editing unit can input the analysis results into the generating AI and have the generating AI generate comments that show specific correction methods.

[0069] The analysis unit can improve the accuracy of feedback by utilizing past data. For example, the analysis unit can improve the accuracy of feedback by utilizing past data. The generation AI learns what kind of corrections were effective based on previously edited data and reflects this in the next feedback. For example, the generation AI applies an algorithm to improve the accuracy of feedback based on past analysis results and feedback history. This improves the accuracy of feedback. 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 past data into the generation AI and have the generation AI perform the task of improving the accuracy of feedback.

[0070] The service provider can collect feedback from parents and children and improve the algorithm of the generative AI. For example, the service provider collects feedback from parents and children and improves the algorithm of the generative AI. The service provider collects opinions from parents and children such as "This advice was helpful" or "This part was difficult to understand" and reflects them in improving the generative AI. For example, the service provider collects feedback and uses it as data to improve the algorithm of the generative AI. This allows the accuracy of the generative AI to continuously improve. 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 collected feedback data into the generative AI and have the generative AI perform algorithm improvements.

[0071] The selection unit can estimate the parent's emotions and suggest font options based on those emotions. For example, if the parent is stressed, the selection unit will prioritize suggesting simple and intuitive fonts. If the parent is relaxed, the selection unit can also provide multiple font options with detailed explanations. Furthermore, if the parent is in a hurry, the selection unit can automatically suggest fonts that have been selected in the past. This enables font selection tailored to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input parent emotion data into a generative AI and have the generative AI suggest font options.

[0072] The selection unit can analyze past selection history and prioritize displaying fonts preferred by parents. For example, the selection unit can analyze past selection history and prioritize displaying fonts preferred by parents. For example, the selection unit can prioritize displaying fonts that parents have frequently selected in the past. The selection unit can also analyze fonts selected by parents during specific time periods and suggest fonts suitable for those times. Furthermore, the selection unit can suggest fonts suitable for specific events or seasons based on the parents' past selection history. This allows for the priority selection of fonts preferred by parents. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input past selection history data into a generating AI and have the generating AI perform the priority display of fonts.

[0073] The selection unit can suggest an appropriate font based on the child's learning progress when selecting a font. For example, when a child is writing for the first time, the selection unit suggests a basic block script font. If the child has some ability to write, the selection unit can also suggest semi-cursive or cursive script fonts. Furthermore, if the child shows interest in a particular font, the selection unit can prioritize suggesting that font. This makes it possible to select a font that is appropriate for the child's learning progress. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the child's learning progress data into a generating AI and have the generating AI suggest fonts.

[0074] The selection unit can estimate the parent's emotions and adjust the font selection order based on the estimated emotions. For example, if the parent is stressed, the selection unit may display simple and intuitive fonts first. If the parent is relaxed, the selection unit may also display multiple font options randomly. Furthermore, if the parent is in a hurry, the selection unit may display previously selected fonts first. This adjusts the font selection order according to the parent'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 selection unit may be performed using AI or not using AI. For example, the selection unit can input parent emotion data into a generative AI and have the generative AI adjust the font selection order.

[0075] The selection unit can suggest region-specific fonts when selecting a font, taking into account the parent's geographical location information. For example, when selecting a font, the selection unit suggests region-specific fonts considering the parent's geographical location information. For example, if the parent is in Japan, the selection unit suggests a Japanese-style font. If the parent is in China, the selection unit can also suggest a Chinese-style font. Furthermore, if the parent is in Europe or America, the selection unit can also suggest a European-style font. This allows for the suggestion of region-specific fonts. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the parent's geographical location data into a generating AI and have the generating AI suggest region-specific fonts.

[0076] The selection unit can analyze the parent's social media activity when selecting a font and suggest relevant fonts. For example, the selection unit can analyze the parent's social media activity when selecting a font and suggest relevant fonts. For example, the selection unit can suggest fonts that the parent frequently uses on social media. The selection unit can also analyze the fonts of accounts that the parent follows and suggest relevant fonts. Furthermore, the selection unit can analyze the fonts of images that the parent has shared on social media and suggest relevant fonts. This makes it possible to suggest fonts based on social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the parent's social media activity data into a generating AI and have the generating AI perform font suggestions.

[0077] The camera unit can estimate the child's emotions and adjust the shooting timing based on the estimated emotions. For example, the camera unit can estimate the child's emotions and adjust the shooting timing based on the estimated emotions. For example, if the child is relaxed, the camera unit can shoot at a natural timing. If the child is tense, the camera unit can wait until the child relaxes before shooting. Furthermore, if the child is excited, the camera unit can wait until the excitement subsides before shooting. This makes it possible to adjust the shooting timing according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI, for example, or not using AI. For example, the camera unit can input the child's emotion data into the generative AI and have the generative AI adjust the shooting timing.

[0078] The camera unit can automatically adjust the lighting and angle during shooting to acquire an appropriate image. For example, the camera unit can automatically adjust the light intensity during shooting to ensure optimal brightness. It can also automatically adjust the camera angle to ensure the optimal viewpoint. Furthermore, the camera unit can automatically adjust the background brightness to make the subject stand out. This allows for the acquisition of an optimal image. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can have a generating AI perform the adjustment of lighting and angle.

[0079] The camera unit can detect the child's hand movements during shooting and display a guide to prevent blurring. For example, the camera unit can detect the child's hand movements during shooting and display guidelines to prevent blurring. The camera unit can also detect the child's hand movements and provide audio guidance to prevent blurring. Furthermore, the camera unit can detect the child's hand movements and provide vibration feedback to prevent blurring. This will enable the display of a guide to prevent blurring. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input the child's hand movement data into a generating AI and have the generating AI perform the display of the blur prevention guide.

[0080] The camera unit can estimate a child's emotions and determine the priority of filming based on the estimated emotions. For example, the camera unit can estimate a child's emotions and determine the priority of filming based on the estimated emotions. For example, if a child is relaxed, the camera unit will prioritize filming. The camera unit can also wait to film if the child is tense until they relax. Furthermore, if the child is excited, the camera unit can wait to film until they calm down. This determines the priority of filming according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI, for example, or not using AI. For example, the camera unit can input child emotion data into a generative AI and have the generative AI determine the priority of filming.

[0081] The shooting unit can propose an appropriate shooting environment while considering the child's geographical location information. For example, if the child is outdoors, the shooting unit can propose a shooting environment that utilizes natural light. If the child is indoors, the shooting unit can also propose a shooting environment that utilizes artificial lighting. Furthermore, if the child is in a specific location, the shooting unit can propose a shooting environment suitable for that location. This ensures that the optimal shooting environment is proposed. Some or all of the above processing in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input the child's geographical location information data into a generating AI and have the generating AI propose a shooting environment.

[0082] The photography unit can analyze a child's social media activity during photography and suggest relevant photography methods. For example, the photography unit can analyze a child's social media activity during photography and suggest relevant photography methods. For example, the photography unit can analyze how images shared by a child on social media are photographed and suggest relevant photography methods. The photography unit can also analyze how accounts followed by a child are photographed and suggest relevant photography methods. Furthermore, the photography unit can analyze filters frequently used by a child on social media and suggest relevant photography methods. This results in the suggestion of photography methods based on social media activity. Some or all of the above processing in the photography unit may be performed using AI, for example, or without AI. For example, the photography unit can input the child's social media activity data into a generating AI and have the generating AI suggest photography methods.

[0083] The analysis unit can estimate the child's emotions and adjust the analysis criteria based on the estimated emotions. For example, the analysis unit can estimate the child's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the child is relaxed, the analysis unit can perform a detailed analysis. The analysis unit can also perform a simplified analysis if the child is tense. Furthermore, if the child is excited, the analysis unit can perform a visually easy-to-understand analysis. This adjusts the analysis criteria according to the child'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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's emotion data into the generative AI and have the generative AI perform the adjustment of the analysis criteria.

[0084] The analysis unit can apply algorithms to analyze the details of characters during the analysis process. For example, the analysis unit can apply algorithms to analyze the details of characters. For example, the analysis unit can apply algorithms to analyze the thickness and angle of character lines in detail. The analysis unit can also apply algorithms to analyze the balance and placement of characters in detail. Furthermore, the analysis unit can apply algorithms to analyze the pen pressure and stroke order of characters in detail. This allows for detailed analysis of the details of 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 detailed character data into a generating AI and have the generating AI perform a detailed analysis.

[0085] The analysis unit can improve analysis accuracy by referring to past analysis data during analysis. For example, the analysis unit can improve analysis accuracy by referring to past analysis data during analysis. The analysis unit can evaluate the balance and line thickness of characters based on past analysis data. The analysis unit can also evaluate the angle and placement of characters based on past analysis data. Furthermore, the analysis unit can evaluate the pen pressure and stroke order of characters based on past analysis data. As a result, analysis accuracy is improved by referring to past analysis data. 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 past analysis data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0086] The analysis unit can estimate the child's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, the analysis unit can estimate the child's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the child is relaxed, the analysis unit can display detailed analysis results first. If the child is tense, the analysis unit can also display simplified analysis results first. Furthermore, if the child is excited, the analysis unit can also display visually easy-to-understand analysis results first. This adjusts the display order of the analysis results according to the child'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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's emotion data into the generative AI and have the generative AI adjust the display order of the analysis results.

[0087] The analysis unit can perform analysis while considering the geographical distribution of characters. For example, the analysis unit can perform analysis while considering the geographical distribution of characters. For example, the analysis unit can analyze region-specific typefaces based on the geographical distribution of characters. The analysis unit can also analyze region-specific stroke order based on the geographical distribution of characters. Furthermore, the analysis unit can analyze region-specific pen pressure based on the geographical distribution of characters. This makes analysis based on geographical distribution possible. 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 geographical distribution data of characters into a generating AI and have the generating AI perform the analysis.

[0088] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can evaluate the balance and line thickness of characters based on relevant literature. The analysis unit can also evaluate the angle and placement of characters based on relevant literature. Furthermore, the analysis unit can evaluate the pen pressure and stroke order of characters based on relevant literature. In this way, the accuracy of the analysis is improved by referring to relevant literature. 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 relevant literature data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0089] The editing unit can estimate a child's emotions and adjust the editing style based on the estimated emotions. For example, the editing unit can estimate a child's emotions and adjust the editing style based on the estimated emotions. For example, if the child is relaxed, the editing unit can provide detailed editing comments. If the child is tense, the editing unit can also provide simplified editing comments. Furthermore, if the child is excited, the editing unit can provide visually easy-to-understand editing comments. This adjusts the editing style according to the child'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 editing unit may be performed using AI, for example, or without AI. For example, the editing unit can input child emotion data into a generative AI and have the generative AI adjust the editing style.

[0090] The editing unit can adjust the level of detail in the editing process based on the importance of the characters. For example, the editing unit can adjust the level of detail in the editing process based on the importance of the characters. For example, the editing unit can provide detailed editing comments for important characters. The editing unit can also provide simplified editing comments for less important characters. Furthermore, the editing unit can provide visually emphasized editing comments for highly important parts. This adjusts the level of detail in the editing process according to the importance of the characters. Some or all of the above processing in the editing unit may be performed using AI, for example, or without AI. For example, the editing unit can input character importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the editing process.

[0091] The editing unit can apply different editing algorithms depending on the category of the characters during editing. For example, the editing unit can apply a different editing algorithm depending on the category of the characters during editing. For example, the editing unit can apply a dedicated editing algorithm for regular script to regular script. The editing unit can also apply a dedicated editing algorithm for semi-cursive script to semi-cursive script. Furthermore, the editing unit can apply a dedicated editing algorithm for cursive script to cursive script. This makes it possible to edit according to the category of characters. Some or all of the above processing in the editing unit may be performed using AI, for example, or without using AI. For example, the editing unit can input character category data into a generating AI and have the generating AI execute the application of the editing algorithm.

[0092] The editing unit can estimate a child's emotions and adjust the length of the edits based on the estimated emotions. For example, the editing unit estimates a child's emotions and adjusts the length of the edits based on the estimated emotions. For example, if the child is relaxed, the editing unit provides detailed edits. If the child is tense, the editing unit can also provide simplified, short edits. Furthermore, if the child is excited, the editing unit can provide visually clear, short edits. This adjusts the length of the edits according to the child'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 editing unit may be performed using AI, for example, or not using AI. For example, the editing unit can input child emotion data into a generative AI and have the generative AI adjust the length of the edits.

[0093] The editing unit can determine the priority of editing based on the submission date of the text during the editing process. For example, the editing unit can prioritize editing newly submitted text. It can also postpone editing older text. Furthermore, if the submission date is related to a specific event or season, the editing unit can tailor the editing accordingly. This determines the editing priority according to the submission date of the text. Some or all of the above processing in the editing unit may be performed using AI, for example, or without AI. For example, the editing unit can input text submission date data into a generating AI and have the generating AI determine the editing priority.

[0094] The editing unit can adjust the order of editing based on the relationships between characters during editing. For example, the editing unit can adjust the order of editing based on the relationships between characters during editing. For example, the editing unit can prioritize editing important characters. The editing unit can also postpone editing less important characters. Furthermore, the editing unit can edit parts of a text that are highly related consecutively. This adjusts the order of editing according to the relationships between characters. Some or all of the above processing in the editing unit may be performed using AI, for example, or without AI. For example, the editing unit can input character relationship data into a generating AI and have the generating AI perform the adjustment of the editing order.

[0095] The service provider can estimate the parent's emotions and adjust the way it expresses the feedback it provides based on the estimated emotions. For example, the service provider can estimate the parent's emotions and adjust the way it expresses the feedback it provides based on the estimated emotions. For example, if the parent is relaxed, the service provider can provide detailed feedback. If the parent is stressed, the service provider can also provide simplified feedback. Furthermore, if the parent is in a hurry, the service provider can provide concise feedback. This adjusts the way the feedback is expressed according to the parent'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 service provider may be performed using AI or not using AI. For example, the service provider can input parent emotion data into a generative AI and have the generative AI adjust the way the feedback is expressed.

[0096] The service provider can provide appropriate feedback by referring to past feedback history at the time of delivery. For example, the service provider can provide appropriate feedback by referring to past feedback history at the time of delivery. For example, the service provider can provide feedback in a format preferred by parents based on past feedback history. The service provider can also provide feedback in a format that is easy for children to understand based on past feedback history. Furthermore, the service provider can provide effective feedback content based on past feedback history. This ensures that optimal feedback is provided based on past feedback 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 past feedback history data into a generating AI and have the generating AI perform the provision of feedback.

[0097] The service provider can customize the content of the feedback based on the parent's current situation at the time of delivery. For example, the service provider can customize the content of the feedback based on the parent's current situation at the time of delivery. For example, if the parent is busy, the service provider can provide concise feedback. Alternatively, if the parent is relaxed, the service provider can provide detailed feedback. Furthermore, if the parent is involved in a specific event or season, the service provider can provide timely feedback. This ensures that the feedback is tailored to the parent's current situation. 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 parent's current situation data into a generating AI and have the generating AI perform the feedback customization.

[0098] The service provider can estimate the parent's emotions and determine the priority of feedback based on the estimated emotions. For example, the service provider can estimate the parent's emotions and determine the priority of feedback based on the estimated emotions. For example, if the parent is relaxed, the service provider may prioritize providing detailed feedback. If the parent is stressed, the service provider may also prioritize providing simplified feedback. Furthermore, if the parent is in a hurry, the service provider may also prioritize providing concise feedback. This determines the priority of feedback according to the parent'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 or not using AI. For example, the service provider can input parent emotion data into a generative AI and have the generative AI determine the priority of feedback.

[0099] The service provider can provide appropriate feedback at the time of delivery, taking into account the geographical location information of the guardian. For example, the service provider can provide appropriate feedback at the time of delivery, taking into account the geographical location information of the guardian. For example, if the guardian is in Japan, the service provider can provide feedback in Japanese. The service provider can also provide feedback in Chinese if the guardian is in China. Furthermore, the service provider can provide feedback in English if the guardian is in Europe or the Americas. This ensures that optimal feedback is provided based on geographical location information. Some or all of the processing described above in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the guardian's geographical location data into a generating AI and have the generating AI perform the task of providing feedback.

[0100] The service provider can analyze the parent's social media activity and provide relevant feedback at the time of delivery. For example, the service provider can analyze the parent's social media activity and provide relevant feedback at the time of delivery. The service provider can provide feedback in the language the parent frequently uses on social media. The service provider can also analyze the feedback format of accounts the parent follows and provide relevant feedback. Furthermore, the service provider can provide relevant feedback based on information the parent has shared on social media. This ensures that feedback is provided based on social media activity. 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 parent's social media activity data into a generating AI and have the generating AI perform the task of providing feedback. === Hard Collateral 1-1 === Each of the multiple elements described above, including the selection unit, shooting unit, analysis unit, correction unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the selection unit is implemented as a function that allows a parent to select a font through an app on the smart device 14. The shooting unit is implemented as a function that uses the camera on the smart device 14 to photograph the characters written by the child. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the captured image and compares it with the selected font to determine which parts of the child's characters need improvement. The correction unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates comments indicating specific correction methods based on the analysis results. The provision unit is implemented as a function that provides the correction results to the parent and child through app notifications on the smart device 14 or via email. === Hard Collateral 1-2 === Each of the multiple elements described above, including the selection unit, shooting unit, analysis unit, correction unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the selection unit is implemented as a function that allows parents to select a font through an app on the smart glasses 214. The shooting unit is implemented as a function that uses the camera on the smart glasses 214 to photograph the characters written by the child. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the captured image and compares it with the selected font to determine which parts of the child's writing need improvement. The correction unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates comments indicating specific correction methods based on the analysis results. The provision unit is implemented as a function that provides the correction results to the parent and child through app notifications on the smart glasses 214 or via email. === Hard Collateral 1-3 === Each of the multiple elements described above, including the selection unit, shooting unit, analysis unit, correction unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the selection unit is implemented as a function that allows a parent to select a font through an app on the headset terminal 314. The shooting unit is implemented as a function that uses the camera on the headset terminal 314 to photograph the characters written by the child. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the captured image and compares it with the selected font to determine which parts of the child's writing need improvement. The correction unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates comments indicating specific correction methods based on the analysis results. The provision unit is implemented as a function that provides the correction results to the parent and child through app notifications on the headset terminal 314 or via email. === Hard Collateral 1-4 === Each of the multiple elements described above, including the selection unit, shooting unit, analysis unit, correction unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the selection unit is implemented as a function that allows a parent to select a font through the interface of the robot 414. The shooting unit is implemented as a function that uses the camera of the robot 414 to photograph the characters written by the child. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the captured image and compares it with the selected font to determine which parts of the child's writing need improvement. The correction unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates comments indicating specific correction methods based on the analysis results. The provision unit is implemented as a function that provides the correction results to the parent and child through the display or voice output of the robot 414.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The selection function can also automatically generate practice tasks tailored to the child's learning progress based on the font selected by the parent. For example, if the regular script (Kaisho) is selected, the function will generate tasks that start with basic line practice and gradually progress to more complex characters. If the semi-cursive script (Gyosho) is selected, it can generate tasks that include practice of flowing lines. Furthermore, if the cursive script (Sosho) is selected, it can generate tasks that practice freehand brushstrokes. This allows for effective practice tailored to the child's learning progress.

[0103] The analysis unit can also regularly evaluate a child's writing progress and provide feedback tailored to that progress. For example, it can evaluate weekly progress and provide feedback highlighting areas for improvement and goals achieved. It can also evaluate monthly progress and provide advice for achieving long-term goals. Furthermore, it can evaluate progress toward specific events or exams and provide feedback to support necessary preparation. This helps maintain a child's motivation to learn and supports effective learning.

[0104] The analysis unit can analyze the characteristics of a child's handwriting and propose an individualized learning plan. For example, it can analyze the balance, line thickness, and angles of a child's handwriting and suggest areas for improvement. It can also analyze a child's handwriting habits and provide specific advice for improvement. Furthermore, it can suggest practice methods tailored to the child's learning style and support effective learning. This ensures that a learning plan is provided that meets the individual needs of each child.

[0105] The editing function can estimate the child's emotions and adjust the tone of the editing based on that estimation. For example, if the child is relaxed, it will provide editing comments that include many words of encouragement. If the child is tense, it can provide comments in a gentle tone that point out specific areas for improvement. Furthermore, if the child is excited, it can provide comments that emphasize positive feedback and boost motivation. This ensures that appropriate feedback is provided according to the child's emotions.

[0106] The service provider can estimate the parent's emotions and adjust the format of the feedback based on those estimates. For example, if the parent is stressed, it can provide concise and to-the-point feedback. If the parent is relaxed, it can provide detailed feedback. Furthermore, if the parent is in a hurry, it can provide feedback that emphasizes only the important points. This ensures that appropriate feedback is provided according to the parent's emotions.

[0107] The selection section can analyze the parent's past selection history and prioritize displaying fonts they prefer. For example, it can prioritize displaying fonts that the parent has frequently selected in the past. It can also analyze the fonts the parent has selected during specific time periods and suggest fonts suitable for those times. Furthermore, it can suggest fonts suitable for specific events or seasons based on the parent's past selection history. This allows for the prioritization of fonts preferred by the parent.

[0108] The analysis unit can estimate the child's emotions and adjust the analysis criteria based on those estimates. For example, if the child is relaxed, a detailed analysis can be performed. If the child is tense, a simplified analysis can be performed. Furthermore, if the child is excited, a visually easy-to-understand analysis can be performed. This adjusts the analysis criteria according to the child's emotions.

[0109] The camera unit can automatically adjust lighting and angles during shooting to acquire appropriate images. For example, it can automatically adjust the light intensity to ensure optimal brightness. It can also automatically adjust the camera angle to ensure the optimal viewpoint. Furthermore, it can automatically adjust the background brightness to make the subject stand out. This allows for the acquisition of optimal images.

[0110] The camera unit can estimate the child's emotions and adjust the shooting timing based on that estimation. For example, if the child is relaxed, shooting can be done at natural moments. If the child is tense, shooting can be done after they have relaxed. Furthermore, if the child is excited, shooting can be done after they have calmed down. This allows for adjustment of the shooting timing according to the child's emotions.

[0111] The service provider can also provide appropriate feedback by considering the parent's geographical location at the time of delivery. For example, if the parent is in Japan, feedback will be provided in Japanese. If the parent is in China, feedback can be provided in Chinese. Furthermore, if the parent is in Europe or the Americas, feedback can be provided in English. This ensures that optimal feedback is provided based on geographical location information.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: In the selection section, parents choose the typeface they want their child to master. Parents select their preferred typeface from several options (e.g., regular script, semi-cursive script, cursive script, etc.) via a smartphone or tablet app. Step 2: The shooting unit photographs the writing of the child. A smartphone or tablet camera is used to photograph the writing the child has written on paper. The captured image is sent to the analysis unit. Step 3: The analysis unit analyzes the captured image and compares it to the selected font to determine which parts of the child's handwriting need improvement. Generative AI is used for the analysis, evaluating the balance of the characters, line thickness, angles, etc. The generative AI utilizes past data to improve the accuracy of the feedback. Step 4: The editing section makes corrections based on the analysis results. For example, it generates comments that indicate specific correction methods, such as "Let's make this line a little thicker" or "Let's make this angle a little sharper." Step 5: The delivery team provides the corrected work to the parents and children. Delivery methods include email, app notifications, and printed materials. In addition, feedback from parents and children is collected to help improve the generating AI.

[0114] 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.

[0115] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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. 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.

[0132] 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.

[0133] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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. 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.

[0148] 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.

[0149] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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. 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.

[0165] 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.

[0166] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A selection section where parents can choose the font they want their children to master, A photography team that takes pictures of the children's handwriting, The analysis unit analyzes the captured image and compares it with a selected font to determine which parts of the child's handwriting need improvement. The editing section performs corrections based on the analysis results, It includes a provisioning unit that provides the corrected results to the parents and children. A system characterized by the following features.

2. The aforementioned selection unit is Parents can select fonts through a smartphone or tablet app. The system according to feature 1.

3. The aforementioned analysis unit, The balance of the characters, the thickness of the lines, and the angles are evaluated based on the captured image. The system according to feature 1.

4. The aforementioned analysis unit, Improve the accuracy of feedback by utilizing past data. The system according to feature 1.

5. The aforementioned supply unit is, We collect feedback from parents and children to improve the algorithms of our generative AI. The system according to feature 1.

6. The aforementioned selection unit is It estimates the parent's emotions and suggests font options based on those estimated emotions. The system according to feature 1.

7. The aforementioned selection unit is By analyzing past selection history, the system prioritizes displaying fonts preferred by parents. The system according to feature 1.

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