System

Abstract

An object of a system according to an embodiment is to provide feedback for a user to write a beautiful character in real time.SOLUTION: A system according to an embodiment includes a character presetting unit, a handwriting analysis unit, and a feedback providing unit. The character presetting unit sets a character that the user wants to write. The handwriting analysis unit analyzes handwriting of the character set by the character presetting unit. The feedback providing unit provides feedback based on a result of the analysis by the handwriting analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not provide users with real-time feedback to help them write neatly, leaving room for improvement.

[0005] The system according to the embodiment aims to provide real-time feedback to help users write neat characters. [Means for solving the problem]

[0006] The system according to the embodiment includes a character preset unit, a handwriting analysis unit, and a feedback providing unit. The character preset unit sets a character that a user wants to write. The handwriting analysis unit analyzes the handwriting of the character set by the character preset unit. The feedback providing unit provides feedback based on the analysis result by the handwriting analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide real-time feedback to help the user write neat characters. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The character writing assistance system according to the embodiment of the present invention is a system that uses a generation AI to assist a user in writing beautiful characters while referring to characters displayed on a tablet screen. This allows the character writing assistance system to efficiently assist a user in writing beautiful characters.

[0029] The character writing support system according to the embodiment includes a character pre - setting unit, a handwriting analysis unit, and a feedback providing unit. The character pre - setting unit sets the characters that the user wants to write. For example, the user selects "the Chinese character 'wood'" on the app's setting screen. Also, the character pre - setting unit can set the characters that the user wants to write using voice input. For example, when the user inputs the voice "wood", the generation AI recognizes the voice and makes the setting. Also, the character pre - setting unit can analyze and set the characters input by the user in handwriting. For example, when the user inputs the character 'wood' in handwriting, the generation AI analyzes the character and makes the setting. The handwriting analysis unit analyzes the handwriting of the characters set by the character pre - setting unit. For example, the handwriting analysis unit analyzes the pen pressure, speed, and angle of the characters written by the user. Also, the handwriting analysis unit can analyze the user's handwriting as a 3D model. For example, it analyzes the three - dimensional shape of the characters written by the user and points out areas for improvement. Also, the handwriting analysis unit can analyze the user's handwriting along the time axis. For example, it analyzes the speed and rhythm when the user writes characters and proposes an optimal writing method. The feedback providing unit provides feedback based on the results analyzed by the handwriting analysis unit. For example, when the vertical line of the character written by the user is curved, the feedback providing unit provides feedback such as "It would be better to write the vertical line straighter". Also, the feedback providing unit can provide feedback according to the user's emotional state. For example, when the user is nervous, it provides advice for relaxation. Also, the feedback providing unit can reproduce the user's handwriting as an animation and provide feedback that is easy to understand visually. For example, it reproduces the movement of the characters written by the user as an animation and visually shows the areas for improvement. Thereby, the character writing support system according to the embodiment can efficiently assist the user in writing beautiful characters.

[0030] The character presetting unit can learn a user's past handwriting data and suggest a character writing style optimized for each individual user. In the character presetting unit, for example, the generation AI collects a user's past handwriting data and suggests a character writing style optimized for each individual user. For example, based on data of characters written by a user in the past, the generation AI analyzes specific habits and tendencies and provides feedback accordingly. In addition, the character presetting unit can learn data of characters written by a user in the past and suggest a character writing style optimized for each individual user. For example, the generation AI can analyze patterns when a user repeatedly writes a specific character and point out areas for improvement. In addition, the character presetting unit can suggest a character writing style optimized for each individual user based on the user's past handwriting data. For example, the generation AI can analyze in detail how a user writes a specific character and suggest the optimal writing style. In this way, the generation AI can learn a user's past handwriting data and suggest a character writing style optimized for each individual user, thereby providing more effective feedback.

[0031] The character presetting unit can learn different font styles and perform evaluation based on the style selected by the user. In the character presetting unit, for example, the generation AI learns different font styles and performs evaluation based on the style selected by the user. For example, the character presetting unit learns styles such as regular script, running script, and cursive script, and provides feedback according to the style selected by the user. In addition, the generation AI performs evaluation based on the font style selected by the user. For example, if the user selects regular script, the character is evaluated according to the standards for regular script and feedback is provided. In addition, the character presetting unit learns different font styles and performs evaluation based on the style selected by the user. For example, if the user selects cursive script, the character is evaluated based on the characteristics of cursive script and points out areas for improvement. In this way, by learning different font styles and performing evaluation based on the style selected by the user, feedback according to the user's needs can be provided.

[0032] The character pre-setting unit can learn characters in different languages ​​and provide multilingual character writing support. For example, the character pre-setting unit allows the generation AI to learn characters in different languages ​​and provide multilingual character writing support. For example, the character pre-setting unit learns characters for English, French, Arabic, etc., and provides feedback based on the language selected by the user. The character pre-setting unit also allows the generation AI to provide character writing support based on the language selected by the user. For example, if the user selects English, the character pre-setting unit evaluates and provides feedback based on how the English characters are written. The character pre-setting unit also allows the generation AI to learn characters in different languages ​​and provide multilingual character writing support. For example, if the user selects French, the character pre-setting unit evaluates and provides feedback based on the characteristics of French characters and points out areas for improvement. In this way, by learning characters in different languages ​​and providing multilingual character writing support, it is possible to support users in writing beautiful characters in multiple languages.

[0033] The character preset unit can analyze the user's hand movements and writing pressure in real time and suggest the optimal writing style. In the character preset unit, for example, the generation AI analyzes the user's hand movements and writing pressure in real time and suggests the optimal writing style. For example, the generation AI analyzes the user's hand movements and writing pressure when writing characters and points out areas for improvement. The character preset unit also analyzes the user's hand movements and writing pressure in real time and the generation AI suggests the optimal writing style. For example, if the user's writing pressure is too strong, advice is provided on how to maintain appropriate writing pressure. The character preset unit also analyzes the user's hand movements and writing pressure in real time and suggests the optimal writing style. For example, if the user's hand movements are unstable when writing characters, advice is provided on how to maintain stable hand movements. In this way, by analyzing the user's hand movements and writing pressure in real time and suggesting the optimal writing style, it is possible to support the user in writing beautiful characters more effectively.

[0034] The pre - setting part of the text enables the user to set the text they want to write through voice input, allowing the generative AI to perform voice recognition. The pre - setting part of the text, for example, enables the user to set the text they want to write through voice input, allowing the generative AI to perform voice recognition. For example, when the user inputs "tree" by voice, the generative AI recognizes the voice and makes the settings. Also, the pre - setting part of the text constructs a system for the user to set the text they want to write using voice input. For example, when the user inputs "the Chinese character 'tree'" by voice, the generative AI analyzes the voice and makes the settings. Also, the pre - setting part of the text enables the generative AI to perform voice recognition and the user to set the text they want to write through voice input. For example, when the user inputs "the hiragana character 'a'" by voice, the generative AI recognizes the voice and makes the settings. By doing so, the user can set the text they want to write through voice input and the generative AI performs voice recognition, thus improving the convenience for the user.

[0035] The pre - setting part of the text enables the user to upload the text they want to write as an image, allowing the generative AI to analyze the image and recognize the text. The pre - setting part of the text, for example, enables the user to upload the text they want to write as an image, allowing the generative AI to analyze the image and recognize the text. For example, when the user uploads an image of "tree", the generative AI analyzes the image and makes the settings. Also, the pre - setting part of the text constructs a system for the user to upload the text they want to write as an image using image recognition technology. For example, when the user uploads an image of "the Chinese character 'tree'", the generative AI analyzes the image and makes the settings. Also, the pre - setting part of the text enables the generative AI to analyze the image and recognize the text the user wants to write. For example, when the user uploads an image of "the hiragana character 'a'", the generative AI analyzes the image and makes the settings. By doing so, the user can upload the text they want to write as an image and the generative AI analyzes the image and recognizes the text, thus improving the convenience for the user.

[0036] The pre - setting part of the characters can be set by the user writing the characters they want to write by hand and the generative AI analyzing the handwritten characters. For example, the pre - setting part of the characters allows the user to input the characters they want to write by hand, and the generative AI analyzes the handwritten characters for setting. For example, when the user inputs the character "wood" by hand, the generative AI analyzes the character and makes settings. Also, the pre - setting part of the characters constructs a system for setting the characters the user wants to write using handwritten input. For example, when the user inputs the Chinese character "wood" by hand, the generative AI analyzes the character and makes settings. Additionally, the pre - setting part of the characters enables the generative AI to analyze the handwritten characters and recognize the characters the user wants to write. For example, when the user inputs the hiragana character "a" by hand, the generative AI analyzes the character and makes settings. By allowing the user to input the characters they want to write by hand and the generative AI to analyze and set the handwritten characters, the convenience of the user can be improved.

[0037] When the user selects the characters they want to write, the pre - setting part of the characters can add a function for the generative AI to propose related characters and words. For example, when the user selects the characters they want to write, the pre - setting part of the characters adds a function for the generative AI to propose related characters and words. For example, when the user selects "wood", the generative AI proposes related characters such as "forest" and "grove". Also, the pre - setting part of the characters adds a function for the generative AI to propose related characters and words to assist the user when they select the characters they want to write. For example, when the user selects the Chinese character "wood", the generative AI proposes related characters such as "tree" and "branch". Additionally, when the user selects the characters they want to write, the pre - setting part of the characters adds a function for the generative AI to propose related characters and words. For example, when the user selects the hiragana character "a", the generative AI proposes related characters such as "i" and "u". By having the generative AI propose related characters and words when the user selects the characters they want to write, the user's options can be expanded.

[0038] The handwriting analysis unit can analyze the user's handwriting as a 3D model and provide feedback from a three-dimensional perspective. For example, the handwriting analysis unit uses a generation AI to analyze the user's handwriting as a 3D model and provide feedback from a three-dimensional perspective. For example, it analyzes the three-dimensional shape of the characters written by the user and points out areas for improvement. The handwriting analysis unit also analyzes the user's handwriting as a 3D model and the generation AI provides feedback from a three-dimensional perspective. For example, it analyzes the height and depth of the characters written by the user and suggests the optimal writing style. The handwriting analysis unit also analyzes the user's handwriting as a 3D model and provides feedback from a three-dimensional perspective. For example, it analyzes the three-dimensional structure of the characters written by the user and points out specific areas for improvement. In this way, by analyzing the user's handwriting as a 3D model and providing feedback from a three-dimensional perspective, the user can more specifically understand areas for improvement.

[0039] The handwriting analysis unit can analyze a user's handwriting over time and provide feedback based on the writing speed and rhythm. For example, the handwriting analysis unit uses a generation AI to analyze a user's handwriting over time and provide feedback based on the writing speed and rhythm. For example, the handwriting analysis unit analyzes the speed and rhythm of the user's writing and suggests the optimal writing method. The handwriting analysis unit also analyzes a user's handwriting over time and provides feedback based on the writing speed and rhythm. For example, if the user's writing rhythm is unstable, the handwriting analysis unit provides advice on how to maintain a stable rhythm. The handwriting analysis unit also analyzes a user's handwriting over time and provides feedback based on the writing speed and rhythm. For example, if the user's writing speed is too fast, the handwriting analysis unit provides advice on how to maintain an appropriate speed. In this way, by analyzing a user's handwriting over time and providing feedback based on the writing speed and rhythm, it is possible to support the user in writing beautiful characters more effectively.

[0040] The handwriting analysis unit can compare a user's handwriting with that of other users and provide a relative evaluation. In the handwriting analysis unit, for example, the generation AI compares the user's handwriting with that of other users and provides a relative evaluation. For example, the handwriting analysis unit compares characters written by the user with those of other users, provides a relative evaluation, and points out areas for improvement. The handwriting analysis unit also compares the user's handwriting with that of other users and the generation AI provides a relative evaluation. For example, the handwriting analysis unit compares the characteristics of characters written by the user with those of other users and provides specific feedback. The handwriting analysis unit also compares the user's handwriting with that of other users and provides a relative evaluation. For example, the handwriting analysis unit compares the style and accuracy of the characters written by the user with those of other users and suggests the optimal way to write them. In this way, by comparing the user's handwriting with that of other users and providing a relative evaluation, the user can more specifically understand areas for improvement in their own handwriting.

[0041] The handwriting analysis unit can play back the user's handwriting as an animation and provide visually easy-to-understand feedback. For example, the handwriting analysis unit allows the generation AI to play back the user's handwriting as an animation and provide visually easy-to-understand feedback. For example, the movement of characters written by the user may be played back as an animation to visually show areas for improvement. The handwriting analysis unit also plays back the user's handwriting as an animation and provides visually easy-to-understand feedback. For example, the stroke order and movement of characters written by the user may be played back as an animation to provide specific feedback. The handwriting analysis unit also allows the generation AI to play back the user's handwriting as an animation and provide visually easy-to-understand feedback. For example, the movement of characters written by the user may be played back as an animation to visually show the optimal way to write them. In this way, by playing back the user's handwriting as an animation and providing visually easy-to-understand feedback, the user can more specifically understand areas for improvement.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The pre - setting part of the text sets the characters that the user wants to write. For example, the user selects "the Chinese character 'wood'" on the app's setting screen. Also, the pre - setting part of the text can set the characters that the user wants to write using voice input. For example, when the user inputs "wood" by voice, the generation AI recognizes the voice and makes the settings. Also, the pre - setting part of the text can analyze and set the characters input by the user in handwriting. For example, when the user inputs "wood" in handwriting, the generation AI analyzes the characters and makes the settings. The handwriting analysis part analyzes the handwriting of the characters set by the pre - setting part of the text. For example, the handwriting analysis part analyzes the pen pressure, speed, and angle of the characters written by the user. Also, the handwriting analysis part can analyze the user's handwriting as a 3D model. For example, it analyzes the three - dimensional shape of the characters written by the user and points out areas for improvement. Also, the handwriting analysis part can analyze the user's handwriting along the time axis. For example, it analyzes the speed and rhythm when the user writes characters and proposes the optimal way of writing. The feedback providing part provides feedback based on the results analyzed by the handwriting analysis part. For example, when the vertical line of the characters written by the user is curved, the feedback providing part provides feedback such as "It would be better to write the vertical line straighter". Also, the feedback providing part can provide feedback according to the user's emotional state. For example, when the user is nervous, it provides advice for relaxation. Also, the feedback providing part can play the user's handwriting as an animation and provide feedback that is easy to understand visually. For example, it plays the movement of the characters written by the user as an animation and visually shows the areas for improvement. Thus, the character writing support system according to the embodiment can efficiently support the user in writing beautiful characters.

[0044] The character pre-setting unit can learn a user's past handwriting data and suggest a character writing style optimized for each individual user. For example, the generation AI collects a user's past handwriting data and suggests a character writing style optimized for each individual user. For example, based on the data of characters written by a user in the past, it analyzes specific habits and tendencies and provides feedback accordingly. The character pre-setting unit also learns data of characters written by a user in the past and suggests a character writing style optimized for each individual user. For example, it analyzes patterns when a user repeatedly writes a specific character and points out areas for improvement. The character pre-setting unit also learns data of characters written by a user in the past and suggests a character writing style optimized for each individual user. For example, it analyzes in detail how a user writes a specific character and suggests the optimal writing style. This allows the generation AI to learn a user's past handwriting data and suggest a character writing style optimized for each individual user, thereby providing more effective feedback.

[0045] The character pre-setting unit can learn different font styles and evaluate them based on the style selected by the user. For example, the generation AI can learn different font styles and evaluate them based on the style selected by the user. For example, it can learn styles such as regular script, running script, and cursive script, and provide feedback according to the style selected by the user. The character pre-setting unit also has the generation AI evaluate them based on the font style selected by the user. For example, if the user selects regular script, it evaluates the characters according to the standards for regular script and provides feedback. The character pre-setting unit also has the generation AI learn different font styles and evaluates them based on the style selected by the user. For example, if the user selects cursive script, it evaluates the characters based on the characteristics of cursive script and points out areas for improvement. In this way, by learning different font styles and evaluating them based on the style selected by the user, it is possible to provide feedback that meets the user's needs.

[0046] The character pre-setting unit can learn characters in different languages ​​and provide multilingual character writing support. For example, the generation AI learns characters in different languages ​​and provides multilingual character writing support. For example, it learns characters for English, French, Arabic, etc., and provides feedback based on the language selected by the user. The character pre-setting unit also allows the generation AI to provide character writing support based on the language selected by the user. For example, if the user selects English, it evaluates and provides feedback based on how the English characters are written. The character pre-setting unit also allows the generation AI to learn characters in different languages ​​and provide multilingual character writing support. For example, if the user selects French, it evaluates based on the characteristics of French characters and points out areas for improvement. In this way, by learning characters in different languages ​​and providing multilingual character writing support, it is possible to support users in writing beautiful characters in multiple languages.

[0047] The character pre-setting unit can analyze the user's hand movements and writing pressure in real time and suggest the optimal writing style. For example, the generation AI can analyze the user's hand movements and writing pressure in real time and suggest the optimal writing style. For example, the generation AI can analyze the user's hand movements and writing pressure when writing characters and point out areas for improvement. The character pre-setting unit can also analyze the user's hand movements and writing pressure in real time and suggest the optimal writing style. For example, if the user's writing pressure is too strong, the generation AI can provide advice on maintaining appropriate writing pressure. The character pre-setting unit can also analyze the user's hand movements and writing pressure in real time and suggest the optimal writing style. For example, if the user's hand movements are unstable when writing characters, the generation AI can provide advice on maintaining stable hand movements. In this way, by analyzing the user's hand movements and writing pressure in real time and suggesting the optimal writing style, it is possible to support the user in writing beautiful characters more effectively.

[0048] The pre - setting part of the text enables the user to set the characters they want to write through voice input, allowing the generative AI to perform voice recognition. For example, the user can set the characters they want to write through voice input, and the generative AI performs voice recognition. For example, when the user inputs the voice "tree", the generative AI recognizes the voice and makes the settings. Also, the pre - setting part of the text constructs a system for the user to set the characters they want to write using voice input. For example, when the user inputs the voice "the Chinese character 'tree'", the generative AI analyzes the voice and makes the settings. Also, the pre - setting part of the text enables the generative AI to perform voice recognition and the user to set the characters they want to write through voice input. For example, when the user inputs the voice "the hiragana 'a'", the generative AI recognizes the voice and makes the settings. Thus, by enabling the user to set the characters they want to write through voice input and allowing the generative AI to perform voice recognition, the convenience of the user can be improved.

[0049] The pre - setting part of the text enables the user to upload the characters they want to write as an image, allowing the generative AI to analyze the image and recognize the characters. For example, the user can upload the characters they want to write as an image, and the generative AI analyzes the image and recognizes the characters. For example, when the user uploads an image of "tree", the generative AI analyzes the image and makes the settings. Also, the pre - setting part of the text constructs a system for the user to upload the characters they want to write as an image using image recognition technology. For example, when the user uploads an image of "the Chinese character 'tree'", the generative AI analyzes the image and makes the settings. Also, the pre - setting part of the text enables the generative AI to analyze the image and recognize the characters the user wants to write. For example, when the user uploads an image of "the hiragana 'a'", the generative AI analyzes the image and makes the settings. Thus, by enabling the user to upload the characters they want to write as an image and allowing the generative AI to analyze the image and recognize the characters, the convenience of the user can be improved.

[0050] The pre - setting part of the text can input the characters that the user wants to write in handwriting, and the generative AI can analyze and set the handwriting characters. For example, the user inputs the characters that they want to write in handwriting, and the generative AI analyzes and sets the handwriting characters. For example, when the user inputs the character "wood" in handwriting, the generative AI analyzes the character and makes the settings. Also, the pre - setting part of the text constructs a system for setting the characters that the user wants to write using handwriting input. For example, when the user inputs the Chinese character "wood" in handwriting, the generative AI analyzes the character and makes the settings. Also, the pre - setting part of the text has the generative AI analyze the handwriting characters and recognize the characters that the user wants to write. For example, when the user inputs the hiragana character "a" in handwriting, the generative AI analyzes the character and makes the settings. Thus, by the user inputting the characters they want to write in handwriting and the generative AI analyzing and setting the handwriting characters, the convenience of the user can be improved.

[0051] When the pre - setting part of the text selects the characters that the user wants to write, the generative AI can add a function of proposing related characters and words. For example, when the pre - setting part of the text selects the characters that the user wants to write, the generative AI adds a function of proposing related characters and words. For example, when the user selects the character "wood", the generative AI proposes related characters such as "forest" and "grove". Also, the pre - setting part of the text adds a function of the generative AI proposing related characters and words to assist when the user selects the characters they want to write. For example, when the user selects the Chinese character "wood", the generative AI proposes related characters such as "tree" and "branch". Also, when the pre - setting part of the text selects the characters that the user wants to write, the generative AI adds a function of proposing related characters and words. For example, when the user selects the hiragana character "a", the generative AI proposes related characters such as "i" and "u". Thus, by the generative AI proposing related characters and words when the user selects the characters they want to write, the user's options can be expanded.

[0052] The handwriting analysis unit can analyze the user's handwriting as a 3D model and provide feedback from a three-dimensional perspective. For example, the generation AI can analyze the user's handwriting as a 3D model and provide feedback from a three-dimensional perspective. For example, it can analyze the three-dimensional shape of the characters written by the user and point out areas for improvement. The handwriting analysis unit can also analyze the user's handwriting as a 3D model and the generation AI can provide feedback from a three-dimensional perspective. For example, it can analyze the height and depth of the characters written by the user and suggest the optimal writing style. The handwriting analysis unit can also analyze the user's handwriting as a 3D model and provide feedback from a three-dimensional perspective. For example, it can analyze the three-dimensional structure of the characters written by the user and point out specific areas for improvement. In this way, by analyzing the user's handwriting as a 3D model and providing feedback from a three-dimensional perspective, the user can more specifically understand areas for improvement.

[0053] The handwriting analysis unit can analyze a user's handwriting over time and provide feedback based on writing speed and rhythm. For example, the generation AI can analyze a user's handwriting over time and provide feedback based on writing speed and rhythm. For example, the generation AI can analyze the speed and rhythm at which the user writes characters and suggest the optimal writing method. The handwriting analysis unit can also analyze a user's handwriting over time and provide feedback based on writing speed and rhythm. For example, if the user's writing rhythm is unstable, advice can be provided on how to maintain a stable rhythm. The handwriting analysis unit can also analyze a user's handwriting over time and provide feedback based on writing speed and rhythm. For example, if the user's writing speed is too fast, advice can be provided on how to maintain an appropriate speed. In this way, by analyzing a user's handwriting over time and providing feedback based on writing speed and rhythm, it is possible to support the user in writing beautiful characters more effectively.

[0054] The handwriting analysis unit can compare a user's handwriting with that of other users and provide a relative evaluation. For example, the generation AI compares the user's handwriting with that of other users and provides a relative evaluation. For example, it compares characters written by the user with that of other users, provides a relative evaluation, and points out areas for improvement. The handwriting analysis unit also compares the user's handwriting with that of other users and the generation AI provides a relative evaluation. For example, it compares the characteristics of characters written by the user with that of other users and provides specific feedback. The handwriting analysis unit also compares the user's handwriting with that of other users and provides a relative evaluation. For example, it compares the style and accuracy of the characters written by the user with that of other users and suggests the optimal way to write them. In this way, by comparing the user's handwriting with that of other users and providing a relative evaluation, the user can more specifically understand areas for improvement in their own handwriting.

[0055] The handwriting analysis unit can play back the user's handwriting as an animation and provide visually easy-to-understand feedback. For example, the generation AI can play back the user's handwriting as an animation and provide visually easy-to-understand feedback. For example, the movement of the characters written by the user can be played back as an animation to visually show areas for improvement. The handwriting analysis unit can also play back the user's handwriting as an animation and provide visually easy-to-understand feedback. For example, the stroke order and movement of the characters written by the user can be played back as an animation to provide specific feedback. The handwriting analysis unit can also play back the user's handwriting as an animation and provide visually easy-to-understand feedback. For example, the movement of the characters written by the user can be played back as an animation to visually show the optimal way to write them. In this way, by playing back the user's handwriting as an animation and providing visually easy-to-understand feedback, the user can more specifically understand areas for improvement.

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

[0057] Step 1: The pre - setting part of the characters sets the characters that the user wants to write. For example, the user selects the Chinese character '木' on the app's setting screen. Also, the user can set the characters to be written using voice input. For example, when the user inputs the voice "木", the generating AI recognizes the voice and makes the setting. Additionally, the characters input by the user in handwriting can be analyzed and set. For example, when the user inputs the character '木' in handwriting, the generating AI analyzes the character and makes the setting. Step 2: The handwriting analysis part analyzes the handwriting of the characters set by the pre - setting part of the characters. For example, the handwriting analysis part analyzes the pen pressure, speed, and angle of the characters written by the user. Also, the user's handwriting can be analyzed as a 3D model to analyze the three - dimensional shape. Furthermore, the user's handwriting can be analyzed on the time axis to analyze the speed and rhythm. Step 3: The feedback providing part provides feedback based on the results analyzed by the handwriting analysis part. For example, when the vertical line of the character written by the user is curved, feedback such as "It would be better to write the vertical line straighter" is provided. Also, feedback according to the user's emotional state can be provided. Furthermore, the user's handwriting can be played back as an animation to provide feedback that is easy to understand visually.

[0058] (Embodiment Example 2) The character writing support system according to the embodiment of the present invention is a system that uses a generating AI to support the user in writing beautiful characters while referring to the characters displayed on the tablet screen. Thereby, the character writing support system can efficiently support the user in writing beautiful characters.

[0059] The character writing support system according to the embodiment includes a character pre - setting unit, a handwriting analysis unit, and a feedback providing unit. The character pre - setting unit sets the characters that the user wants to write. For example, the user selects "the Chinese character 'wood'" on the app's setting screen. Also, the character pre - setting unit can set the characters that the user wants to write using voice input. For example, when the user makes a voice input of "wood", the generation AI recognizes the voice and makes the setting. Also, the character pre - setting unit can analyze and set the characters input by the user in handwriting. For example, when the user inputs "wood" in handwriting, the generation AI analyzes the character and makes the setting. The handwriting analysis unit analyzes the handwriting of the characters set by the character pre - setting unit. For example, the handwriting analysis unit analyzes the pen pressure, speed, and angle of the characters written by the user. Also, the handwriting analysis unit can analyze the user's handwriting as a 3D model. For example, it analyzes the three - dimensional shape of the characters written by the user and points out areas for improvement. Also, the handwriting analysis unit can analyze the user's handwriting along the time axis. For example, it analyzes the speed and rhythm when the user writes characters and proposes an optimal writing method. The feedback providing unit provides feedback based on the results analyzed by the handwriting analysis unit. For example, when the vertical line of the characters written by the user is curved, the feedback providing unit provides feedback such as "It would be better to write the vertical line straighter". Also, the feedback providing unit can provide feedback according to the user's emotional state. For example, when the user is tense, it provides advice for relaxation. Also, the feedback providing unit can play the user's handwriting as an animation and provide feedback that is easy to understand visually. For example, it plays the movement of the characters written by the user as an animation and visually shows the areas for improvement. Thus, the character writing support system according to the embodiment can efficiently assist the user in writing beautiful characters.

[0060] The character presetting unit can learn a user's past handwriting data and suggest a character writing style optimized for each individual user. In the character presetting unit, for example, the generation AI collects a user's past handwriting data and suggests a character writing style optimized for each individual user. For example, based on data of characters written by a user in the past, the generation AI analyzes specific habits and tendencies and provides feedback accordingly. In addition, the character presetting unit can learn data of characters written by a user in the past and suggest a character writing style optimized for each individual user. For example, the generation AI can analyze patterns when a user repeatedly writes a specific character and point out areas for improvement. In addition, the character presetting unit can suggest a character writing style optimized for each individual user based on the user's past handwriting data. For example, the generation AI can analyze in detail how a user writes a specific character and suggest the optimal writing style. In this way, the generation AI can learn a user's past handwriting data and suggest a character writing style optimized for each individual user, thereby providing more effective feedback.

[0061] The character presetting unit can learn different font styles and perform evaluation based on the style selected by the user. In the character presetting unit, for example, the generation AI learns different font styles and performs evaluation based on the style selected by the user. For example, the character presetting unit learns styles such as regular script, running script, and cursive script, and provides feedback according to the style selected by the user. In addition, the generation AI performs evaluation based on the font style selected by the user. For example, if the user selects regular script, the character is evaluated according to the standards for regular script and feedback is provided. In addition, the character presetting unit learns different font styles and performs evaluation based on the style selected by the user. For example, if the user selects cursive script, the character is evaluated based on the characteristics of cursive script and points out areas for improvement. In this way, by learning different font styles and performing evaluation based on the style selected by the user, feedback according to the user's needs can be provided.

[0062] The character preset unit uses the emotion estimation function to provide feedback according to the user's emotional state, thereby encouraging the user to write characters in a relaxed state. The character preset unit, for example, uses the emotion estimation function to provide feedback according to the user's emotional state. For example, if the user is nervous, the character preset unit provides advice to help the user relax, thereby reducing stress when writing characters. The character preset unit also analyzes the user's emotional state in real time and provides feedback according to the emotion. For example, if the user is relaxed, the character preset unit provides advice to help the user maintain that relaxed state. The character preset unit also uses the emotion estimation function to provide feedback according to the user's emotional state, thereby encouraging the user to write characters in a relaxed state. For example, if the user is feeling stressed, the character preset unit provides advice on breathing techniques and posture to help the user relax. In this way, the character preset unit uses the emotion estimation function to provide feedback according to the user's emotional state, thereby encouraging the user to write characters in a relaxed state, thereby reducing stress for the user.

[0063] The character pre-setting unit can learn characters in different languages ​​and provide multilingual character writing support. For example, the character pre-setting unit allows the generation AI to learn characters in different languages ​​and provide multilingual character writing support. For example, the character pre-setting unit learns characters for English, French, Arabic, etc., and provides feedback based on the language selected by the user. The character pre-setting unit also allows the generation AI to provide character writing support based on the language selected by the user. For example, if the user selects English, the character pre-setting unit evaluates and provides feedback based on how the English characters are written. The character pre-setting unit also allows the generation AI to learn characters in different languages ​​and provide multilingual character writing support. For example, if the user selects French, the character pre-setting unit evaluates and provides feedback based on the characteristics of French characters and points out areas for improvement. In this way, by learning characters in different languages ​​and providing multilingual character writing support, it is possible to support users in writing beautiful characters in multiple languages.

[0064] The character preset unit can analyze the user's hand movements and writing pressure in real time and suggest the optimal writing style. In the character preset unit, for example, the generation AI analyzes the user's hand movements and writing pressure in real time and suggests the optimal writing style. For example, the generation AI analyzes the user's hand movements and writing pressure when writing characters and points out areas for improvement. The character preset unit also analyzes the user's hand movements and writing pressure in real time and the generation AI suggests the optimal writing style. For example, if the user's writing pressure is too strong, advice is provided on how to maintain appropriate writing pressure. The character preset unit also analyzes the user's hand movements and writing pressure in real time and suggests the optimal writing style. For example, if the user's hand movements are unstable when writing characters, advice is provided on how to maintain stable hand movements. In this way, by analyzing the user's hand movements and writing pressure in real time and suggesting the optimal writing style, it is possible to support the user in writing beautiful characters more effectively.

[0065] The character preset unit can use the emotion estimation function to analyze the user's emotional response to the characters they write and suggest a way of writing characters that will elicit positive emotions. For example, the character preset unit can use the emotion estimation function to analyze the user's emotional response to the characters they write and suggest a way of writing characters that will elicit positive emotions. For example, the character preset unit can analyze the user's emotional response to the characters they write and provide advice for eliciting positive emotions. The character preset unit can also analyze the user's emotional response in real time and suggest a way of writing characters based on the emotion. For example, the character preset unit can provide feedback for eliciting positive emotions from the characters they write. The character preset unit can also use the emotion estimation function to analyze the user's emotional response to the characters they write and suggest a way of writing characters that will elicit positive emotions. For example, the character preset unit can provide specific advice for eliciting positive emotions based on the user's emotional response to the characters they write. In this way, the emotion estimation function can be used to analyze the user's emotional response to the characters they write and suggest a way of writing characters that will elicit positive emotions, thereby improving the user's motivation.

[0066] The pre - setting part of the characters enables the user to set the characters they want to write through voice input, allowing the generative AI to perform voice recognition. For example, the pre - setting part of the characters enables the user to set the characters they want to write through voice input, allowing the generative AI to perform voice recognition. For example, when the user inputs "tree" by voice, the generative AI recognizes the voice and makes the settings. Also, the pre - setting part of the characters constructs a system for setting the characters the user wants to write using voice input. For example, when the user inputs "the Chinese character 'tree'" by voice, the generative AI analyzes the voice and makes the settings. Additionally, the pre - setting part of the characters enables the generative AI to perform voice recognition and the user to set the characters they want to write through voice input. For example, when the user inputs "the hiragana character 'a'" by voice, the generative AI recognizes the voice and makes the settings. By enabling the user to set the characters they want to write through voice input and allowing the generative AI to perform voice recognition, the convenience of the user can be improved.

[0067] The pre - setting part of the characters allows the user to upload the characters they want to write as an image, enabling the generative AI to analyze the image and recognize the characters. For example, the pre - setting part of the characters allows the user to upload the characters they want to write as an image, enabling the generative AI to analyze the image and recognize the characters. For example, when the user uploads an image of "tree", the generative AI analyzes the image and makes the settings. Also, the pre - setting part of the characters constructs a system for the user to upload the characters they want to write as an image using image recognition technology. For example, when the user uploads an image of "the Chinese character 'tree'", the generative AI analyzes the image and makes the settings. Additionally, the pre - setting part of the characters enables the generative AI to analyze the image and recognize the characters the user wants to write. For example, when the user uploads an image of "the hiragana character 'a'", the generative AI analyzes the image and makes the settings. By allowing the user to upload the characters they want to write as an image and enabling the generative AI to analyze the image and recognize the characters, the convenience of the user can be improved.

[0068] The pre - setting part of the characters can analyze the sentiment towards the characters the user wants to write using the sentiment estimation function and propose ways of writing the characters based on the sentiment. For example, the pre - setting part of the characters uses the sentiment estimation function to analyze the sentiment towards the characters the user wants to write and propose ways of writing the characters based on the sentiment. For example, when the user wants to write the character "tree", its sentiment is analyzed and advice on writing in a relaxed state is provided. Also, the pre - setting part of the characters analyzes the user's sentiment in real - time and proposes ways of writing the characters based on the sentiment. For example, when the user wants to write the Chinese character "tree", its sentiment is analyzed and feedback for eliciting positive sentiment is provided. Also, the pre - setting part of the characters uses the sentiment estimation function to analyze the sentiment towards the characters the user wants to write and propose ways of writing the characters based on the sentiment. For example, when the user wants to write the hiragana character "a", its sentiment is analyzed and specific advice on writing in a relaxed state is provided. Thus, by using the sentiment estimation function to analyze the sentiment towards the characters the user wants to write and propose ways of writing the characters based on the sentiment, feedback corresponding to the user's sentiment can be provided.

[0069] The pre - setting part of the characters can input the characters the user wants to write by handwriting, and the generation AI can analyze and set the handwritten characters. For example, the pre - setting part of the characters inputs the characters the user wants to write by handwriting, and the generation AI analyzes and sets the handwritten characters. For example, when the user inputs the character "tree" by handwriting, the generation AI analyzes the character and makes settings. Also, the pre - setting part of the characters constructs a system for setting the characters the user wants to write using handwriting input. For example, when the user inputs the Chinese character "tree" by handwriting, the generation AI analyzes the character and makes settings. Also, the pre - setting part of the characters enables the generation AI to analyze the handwritten characters and recognize the characters the user wants to write. For example, when the user inputs the hiragana character "a" by handwriting, the generation AI analyzes the character and makes settings. Thus, by inputting the characters the user wants to write by handwriting and having the generation AI analyze and set the handwritten characters, the convenience of the user can be improved.

[0070] The character pre-setting unit can add a function that allows the generation AI to suggest related characters and words when a user selects the characters they want to write. For example, if a user selects "tree," the generation AI would suggest related characters such as "forest" and "forest." The character pre-setting unit can also add a function that allows the generation AI to suggest related characters and words to assist the user in selecting the characters they want to write. For example, if a user selects the kanji character "tree," the generation AI would suggest related characters such as "tree" and "branch." The character pre-setting unit can also add a function that allows the generation AI to suggest related characters and words when a user selects the characters they want to write. For example, if a user selects the hiragana character "a," the generation AI would suggest related characters such as "i" and "u." This allows the generation AI to suggest related characters and words when selecting the characters they want to write, thereby expanding the user's options.

[0071] The character pre-setting unit can use the emotion estimation function to analyze the user's emotion regarding the character they want to write and propose a character practice plan based on the emotion. For example, the character pre-setting unit can use the emotion estimation function to analyze the user's emotion regarding the character they want to write and propose a character practice plan based on the emotion. For example, if the user wants to write "tree," the emotion can be analyzed and a practice plan that allows the user to practice in a relaxed state can be provided. The character pre-setting unit can also analyze the user's emotion in real time and propose a character practice plan based on the emotion. For example, if the user wants to write the kanji character "tree," the emotion can be analyzed and a practice plan that elicits positive emotions can be provided. The character pre-setting unit can also use the emotion estimation function to analyze the user's emotion regarding the character they want to write and propose a character practice plan based on the emotion. For example, if the user wants to write the hiragana character "a," the emotion can be analyzed and a specific plan that allows the user to practice in a relaxed state can be provided. In this way, the emotion estimation function can be used to analyze the user's emotion regarding the character they want to write and propose a character practice plan based on the emotion, thereby providing a practice plan that suits the user's emotion.

[0072] The handwriting analysis unit can analyze the user's handwriting as a 3D model and provide feedback from a three-dimensional perspective. For example, the handwriting analysis unit uses a generation AI to analyze the user's handwriting as a 3D model and provide feedback from a three-dimensional perspective. For example, it analyzes the three-dimensional shape of the characters written by the user and points out areas for improvement. The handwriting analysis unit also analyzes the user's handwriting as a 3D model and the generation AI provides feedback from a three-dimensional perspective. For example, it analyzes the height and depth of the characters written by the user and suggests the optimal writing style. The handwriting analysis unit also analyzes the user's handwriting as a 3D model and provides feedback from a three-dimensional perspective. For example, it analyzes the three-dimensional structure of the characters written by the user and points out specific areas for improvement. In this way, by analyzing the user's handwriting as a 3D model and providing feedback from a three-dimensional perspective, the user can more specifically understand areas for improvement.

[0073] The handwriting analysis unit can analyze a user's handwriting over time and provide feedback based on the writing speed and rhythm. For example, the handwriting analysis unit uses a generation AI to analyze a user's handwriting over time and provide feedback based on the writing speed and rhythm. For example, the handwriting analysis unit analyzes the speed and rhythm of the user's writing and suggests the optimal writing method. The handwriting analysis unit also analyzes a user's handwriting over time and provides feedback based on the writing speed and rhythm. For example, if the user's writing rhythm is unstable, the handwriting analysis unit provides advice on how to maintain a stable rhythm. The handwriting analysis unit also analyzes a user's handwriting over time and provides feedback based on the writing speed and rhythm. For example, if the user's writing speed is too fast, the handwriting analysis unit provides advice on how to maintain an appropriate speed. In this way, by analyzing a user's handwriting over time and providing feedback based on the writing speed and rhythm, it is possible to support the user in writing beautiful characters more effectively.

[0074] The handwriting analysis unit can use the emotion estimation function to analyze the user's emotion regarding their handwriting and provide feedback based on the emotion. The handwriting analysis unit, for example, uses the emotion estimation function to analyze the user's emotion regarding their handwriting and provide feedback based on the emotion. For example, the handwriting analysis unit analyzes the user's emotional response to the characters they have written and provides advice to elicit positive emotions. The handwriting analysis unit also analyzes the user's emotion in real time and provides feedback based on the emotion. For example, it provides feedback to reduce negative emotions the user has regarding the characters they have written. The handwriting analysis unit also uses the emotion estimation function to analyze the user's emotion regarding their handwriting and provide feedback based on the emotion. For example, it provides specific advice to write in a relaxed state based on the user's emotional response to the characters they have written. In this way, by using the emotion estimation function to analyze the user's emotion regarding their handwriting and provide feedback based on the emotion, it is possible to provide feedback according to the user's emotion.

[0075] The handwriting analysis unit can compare a user's handwriting with that of other users and provide a relative evaluation. In the handwriting analysis unit, for example, the generation AI compares the user's handwriting with that of other users and provides a relative evaluation. For example, the handwriting analysis unit compares characters written by the user with those of other users, provides a relative evaluation, and points out areas for improvement. The handwriting analysis unit also compares the user's handwriting with that of other users and the generation AI provides a relative evaluation. For example, the handwriting analysis unit compares the characteristics of characters written by the user with those of other users and provides specific feedback. The handwriting analysis unit also compares the user's handwriting with that of other users and provides a relative evaluation. For example, the handwriting analysis unit compares the style and accuracy of the characters written by the user with those of other users and suggests the optimal way to write them. In this way, by comparing the user's handwriting with that of other users and providing a relative evaluation, the user can more specifically understand areas for improvement in their own handwriting.

[0076] The handwriting analysis unit can play back the user's handwriting as an animation and provide visually easy-to-understand feedback. For example, the handwriting analysis unit allows the generation AI to play back the user's handwriting as an animation and provide visually easy-to-understand feedback. For example, the movement of characters written by the user may be played back as an animation to visually show areas for improvement. The handwriting analysis unit also plays back the user's handwriting as an animation and provides visually easy-to-understand feedback. For example, the stroke order and movement of characters written by the user may be played back as an animation to provide specific feedback. The handwriting analysis unit also allows the generation AI to play back the user's handwriting as an animation and provide visually easy-to-understand feedback. For example, the movement of characters written by the user may be played back as an animation to visually show the optimal way to write them. In this way, by playing back the user's handwriting as an animation and providing visually easy-to-understand feedback, the user can more specifically understand areas for improvement.

[0077] The handwriting analysis unit can use the emotion estimation function to analyze the user's emotion regarding their handwriting and provide emotion-based feedback for improving motivation. The handwriting analysis unit, for example, uses the emotion estimation function to analyze the user's emotion regarding their handwriting and provide emotion-based feedback for improving motivation. For example, the handwriting analysis unit analyzes the user's emotional response to the characters they have written and provides advice for eliciting positive emotions. The handwriting analysis unit also analyzes the user's emotion in real time and provides emotion-based feedback for improving motivation. For example, it provides feedback for reducing negative emotions toward the characters they have written. The handwriting analysis unit also uses the emotion estimation function to analyze the user's emotion regarding their handwriting and provide emotion-based feedback for improving motivation. For example, it provides specific advice for writing in a relaxed state based on the user's emotional response to the characters they have written. In this way, the emotion estimation function can be used to analyze the user's emotion regarding their handwriting and provide emotion-based feedback for improving motivation, thereby improving the user's motivation.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The pre - setting part of the text sets the characters that the user wants to write. For example, the user selects "the Chinese character 'wood'" on the app's settings screen. Also, the pre - setting part of the text can set the characters that the user wants to write using voice input. For example, when the user inputs the voice "wood", the generation AI recognizes the voice and makes the settings. Also, the pre - setting part of the text can analyze and set the characters input by the user in handwriting. For example, when the user inputs the character 'wood' in handwriting, the generation AI analyzes the character and makes the settings. The handwriting analysis part analyzes the handwriting of the characters set by the pre - setting part of the text. For example, the handwriting analysis part analyzes the pen pressure, speed, and angle of the characters written by the user. Also, the handwriting analysis part can analyze the user's handwriting as a 3D model. For example, it analyzes the three - dimensional shape of the characters written by the user and points out areas for improvement. Also, the handwriting analysis part can analyze the user's handwriting along the time axis. For example, it analyzes the speed and rhythm when the user writes characters and proposes the optimal writing method. The feedback providing part provides feedback based on the results analyzed by the handwriting analysis part. For example, when the vertical line of the characters written by the user is curved, the feedback providing part provides feedback such as "It would be better to write the vertical line straighter". Also, the feedback providing part can provide feedback according to the user's emotional state. For example, when the user is nervous, it provides advice for relaxation. Also, the feedback providing part can play the user's handwriting as an animation and provide feedback that is easy to understand visually. For example, it plays the movement of the characters written by the user as an animation and visually shows the areas for improvement. Thereby, the character writing support system according to the embodiment can efficiently support the user in writing beautiful characters.

[0080] The character pre-setting unit can learn a user's past handwriting data and suggest a character writing style optimized for each individual user. For example, the generation AI collects a user's past handwriting data and suggests a character writing style optimized for each individual user. For example, based on the data of characters written by a user in the past, it analyzes specific habits and tendencies and provides feedback accordingly. The character pre-setting unit also learns data of characters written by a user in the past and suggests a character writing style optimized for each individual user. For example, it analyzes patterns when a user repeatedly writes a specific character and points out areas for improvement. The character pre-setting unit also learns data of characters written by a user in the past and suggests a character writing style optimized for each individual user. For example, it analyzes in detail how a user writes a specific character and suggests the optimal writing style. This allows the generation AI to learn a user's past handwriting data and suggest a character writing style optimized for each individual user, thereby providing more effective feedback.

[0081] The character pre-setting unit can learn different font styles and evaluate them based on the style selected by the user. For example, the generation AI can learn different font styles and evaluate them based on the style selected by the user. For example, it can learn styles such as regular script, running script, and cursive script, and provide feedback according to the style selected by the user. The character pre-setting unit also has the generation AI evaluate them based on the font style selected by the user. For example, if the user selects regular script, it evaluates the characters according to the standards for regular script and provides feedback. The character pre-setting unit also has the generation AI learn different font styles and evaluates them based on the style selected by the user. For example, if the user selects cursive script, it evaluates the characters based on the characteristics of cursive script and points out areas for improvement. In this way, by learning different font styles and evaluating them based on the style selected by the user, it is possible to provide feedback that meets the user's needs.

[0082] The character preset unit uses the emotion estimation function to provide feedback according to the user's emotional state, thereby encouraging the user to write characters in a relaxed state. For example, the emotion estimation function is used to provide feedback according to the user's emotional state. For example, if the user is nervous, advice to help the user relax is provided, thereby reducing stress when writing characters. The character preset unit also analyzes the user's emotional state in real time and provides feedback according to the emotion. For example, if the user is relaxed, advice to help the user maintain that state is provided. The character preset unit also uses the emotion estimation function to provide feedback according to the user's emotional state, thereby encouraging the user to write characters in a relaxed state. For example, if the user is feeling stressed, advice on breathing techniques or posture to help the user relax is provided. In this way, the emotion estimation function is used to provide feedback according to the user's emotional state, thereby encouraging the user to write characters in a relaxed state, thereby reducing stress for the user.

[0083] The character pre-setting unit can learn characters in different languages ​​and provide multilingual character writing support. For example, the generation AI learns characters in different languages ​​and provides multilingual character writing support. For example, it learns characters for English, French, Arabic, etc., and provides feedback based on the language selected by the user. The character pre-setting unit also allows the generation AI to provide character writing support based on the language selected by the user. For example, if the user selects English, it evaluates and provides feedback based on how the English characters are written. The character pre-setting unit also allows the generation AI to learn characters in different languages ​​and provide multilingual character writing support. For example, if the user selects French, it evaluates based on the characteristics of French characters and points out areas for improvement. In this way, by learning characters in different languages ​​and providing multilingual character writing support, it is possible to support users in writing beautiful characters in multiple languages.

[0084] The character pre-setting unit can analyze the user's hand movements and writing pressure in real time and suggest the optimal writing style. For example, the generation AI can analyze the user's hand movements and writing pressure in real time and suggest the optimal writing style. For example, the generation AI can analyze the user's hand movements and writing pressure when writing characters and point out areas for improvement. The character pre-setting unit can also analyze the user's hand movements and writing pressure in real time and suggest the optimal writing style. For example, if the user's writing pressure is too strong, the generation AI can provide advice on maintaining appropriate writing pressure. The character pre-setting unit can also analyze the user's hand movements and writing pressure in real time and suggest the optimal writing style. For example, if the user's hand movements are unstable when writing characters, the generation AI can provide advice on maintaining stable hand movements. In this way, by analyzing the user's hand movements and writing pressure in real time and suggesting the optimal writing style, it is possible to support the user in writing beautiful characters more effectively.

[0085] The character preset unit can use the emotion estimation function to analyze the user's emotional response to the characters they write and suggest a way of writing characters that will elicit positive emotions. For example, the emotion estimation function can be used ... The character preset unit can also analyze the user's emotional response in real time and suggest a way of writing characters based on the emotion. For example, the character preset unit can provide feedback to elicit positive emotions from the characters they write. The character preset unit can also use the emotion estimation function to analyze the user's emotional response to the characters they write and suggest a way of writing characters that will elicit positive emotions. For example, the character preset unit can provide specific advice to elicit positive emotions based on the user's emotional response to the characters they write. In this way, the emotion estimation function can be used to analyze the user's emotional response to the characters they write and suggest a way of writing characters that will elicit positive emotions, thereby improving the user's motivation.

[0086] The pre - setting part of the text enables the user to set the characters they want to write through voice input, allowing the generative AI to perform voice recognition. For example, it enables the user to set the characters they want to write through voice input, and the generative AI performs voice recognition. For example, when the user inputs "tree" by voice, the generative AI recognizes the voice and makes the settings. Also, the pre - setting part of the text constructs a system for the user to set the characters they want to write using voice input. For example, when the user inputs "the Chinese character 'tree'" by voice, the generative AI analyzes the voice and makes the settings. Also, the pre - setting part of the text allows the generative AI to perform voice recognition and the user to set the characters they want to write through voice input. For example, when the user inputs "the hiragana character 'a'" by voice, the generative AI recognizes the voice and makes the settings. Thus, by enabling the user to set the characters they want to write through voice input and allowing the generative AI to perform voice recognition, the convenience of the user can be improved.

[0087] The pre - setting part of the text enables the user to upload the characters they want to write as an image, allowing the generative AI to analyze the image and recognize the characters. For example, it enables the user to upload the characters they want to write as an image, and the generative AI analyzes the image and recognizes the characters. For example, when the user uploads an image of "tree", the generative AI analyzes the image and makes the settings. Also, the pre - setting part of the text constructs a system for the user to upload the characters they want to write as an image using image recognition technology. For example, when the user uploads an image of "the Chinese character 'tree'", the generative AI analyzes the image and makes the settings. Also, the pre - setting part of the text allows the generative AI to analyze the image and recognize the characters the user wants to write. For example, when the user uploads an image of "the hiragana character 'a'", the generative AI analyzes the image and makes the settings. Thus, by enabling the user to upload the characters they want to write as an image and allowing the generative AI to analyze the image and recognize the characters, the convenience of the user can be improved.

[0088] The pre - setting part of the text can analyze the sentiment towards the text the user wants to write using the sentiment estimation function and propose a way of writing the text based on the sentiment. For example, using the sentiment estimation function, it analyzes the sentiment towards the text the user wants to write and proposes a way of writing the text based on the sentiment. For example, when the user wants to write the character 'wood', it analyzes the sentiment and provides advice on writing it in a relaxed state. Also, the pre - setting part of the text analyzes the user's sentiment in real - time and proposes a way of writing the text based on the sentiment. For example, when the user wants to write the Chinese character 'wood', it analyzes the sentiment and provides feedback to bring out a positive sentiment. Also, the pre - setting part of the text uses the sentiment estimation function to analyze the sentiment towards the text the user wants to write and proposes a way of writing the text based on the sentiment. For example, when the user wants to write the hiragana character 'a', it analyzes the sentiment and provides specific advice on writing it in a relaxed state. Thus, by using the sentiment estimation function to analyze the sentiment towards the text the user wants to write and proposing a way of writing the text based on the sentiment, it is possible to provide feedback according to the user's sentiment.

[0089] The pre - setting part of the text can input the text the user wants to write by handwriting, and the generation AI can analyze and set the handwritten text. For example, the user inputs the text they want to write by handwriting, and the generation AI analyzes and sets the handwritten text. For example, when the user inputs the character 'wood' by handwriting, the generation AI analyzes the character and makes settings. Also, the pre - setting part of the text constructs a system for setting the text the user wants to write using handwritten input. For example, when the user inputs the Chinese character 'wood' by handwriting, the generation AI analyzes the character and makes settings. Also, the pre - setting part of the text has the generation AI analyze the handwritten text and recognize the text the user wants to write. For example, when the user inputs the hiragana character 'a' by handwriting, the generation AI analyzes the character and makes settings. Thus, by the user inputting the text they want to write by handwriting and the generation AI analyzing and setting the handwritten text, the convenience of the user can be improved.

[0090] The character pre-setting unit can add a function that allows the generation AI to suggest related characters and words when a user selects the characters they want to write. For example, when a user selects the characters they want to write, the generation AI can suggest related characters and words. For example, if a user selects "tree," the generation AI can suggest related characters such as "forest" and "forest." The character pre-setting unit can also add a function that allows the generation AI to suggest related characters and words to assist the user in selecting the characters they want to write. For example, if a user selects the kanji character "tree," the generation AI can suggest related characters such as "tree" and "branch." The character pre-setting unit can also add a function that allows the generation AI to suggest related characters and words when a user selects the characters they want to write. For example, if a user selects the hiragana character "a," the generation AI can suggest related characters such as "i" and "u." This allows the generation AI to suggest related characters and words when selecting the characters they want to write, thereby expanding the user's options.

[0091] The character pre-setting unit can use the emotion estimation function to analyze the user's emotion regarding the character they want to write and propose a character practice plan based on the emotion. For example, the emotion estimation function can be used to analyze the user's emotion regarding the character they want to write and propose a character practice plan based on the emotion. For example, if the user wants to write "tree," the emotion can be analyzed and a practice plan that allows the user to practice in a relaxed state can be provided. The character pre-setting unit can also analyze the user's emotion in real time and propose a character practice plan based on the emotion. For example, if the user wants to write the kanji character "tree," the emotion can be analyzed and a practice plan that elicits positive emotions can be provided. The character pre-setting unit can also use the emotion estimation function to analyze the user's emotion regarding the character they want to write and propose a character practice plan based on the emotion. For example, if the user wants to write the hiragana character "a," the emotion can be analyzed and a specific plan that allows the user to practice in a relaxed state can be provided. In this way, the emotion estimation function can be used to analyze the user's emotion regarding the character they want to write and propose a character practice plan based on the emotion, thereby providing a practice plan that suits the user's emotion.

[0092] The handwriting analysis unit can analyze the user's handwriting as a 3D model and provide feedback from a three-dimensional perspective. For example, the generation AI can analyze the user's handwriting as a 3D model and provide feedback from a three-dimensional perspective. For example, it can analyze the three-dimensional shape of the characters written by the user and point out areas for improvement. The handwriting analysis unit can also analyze the user's handwriting as a 3D model and the generation AI can provide feedback from a three-dimensional perspective. For example, it can analyze the height and depth of the characters written by the user and suggest the optimal writing style. The handwriting analysis unit can also analyze the user's handwriting as a 3D model and provide feedback from a three-dimensional perspective. For example, it can analyze the three-dimensional structure of the characters written by the user and point out specific areas for improvement. In this way, by analyzing the user's handwriting as a 3D model and providing feedback from a three-dimensional perspective, the user can more specifically understand areas for improvement.

[0093] The handwriting analysis unit can analyze a user's handwriting over time and provide feedback based on writing speed and rhythm. For example, the generation AI can analyze a user's handwriting over time and provide feedback based on writing speed and rhythm. For example, the generation AI can analyze the speed and rhythm at which the user writes characters and suggest the optimal writing method. The handwriting analysis unit can also analyze a user's handwriting over time and provide feedback based on writing speed and rhythm. For example, if the user's writing rhythm is unstable, advice can be provided on how to maintain a stable rhythm. The handwriting analysis unit can also analyze a user's handwriting over time and provide feedback based on writing speed and rhythm. For example, if the user's writing speed is too fast, advice can be provided on how to maintain an appropriate speed. In this way, by analyzing a user's handwriting over time and providing feedback based on writing speed and rhythm, it is possible to support the user in writing beautiful characters more effectively.

[0094] The handwriting analysis unit can use the emotion estimation function to analyze the user's emotion regarding their handwriting and provide feedback based on the emotion. For example, the emotion estimation function can be used to analyze the user's emotion regarding their handwriting and provide feedback based on the emotion. For example, the emotion estimation function can be used to analyze the user's emotion regarding their handwriting and provide feedback based on the emotion. For example, the emotion estimation function can be used to analyze the user's emotional reaction to the characters they have written and provide advice to elicit positive emotions. The handwriting analysis unit can also analyze the user's emotion in real time and provide feedback based on the emotion. For example, the handwriting analysis unit can provide feedback to reduce negative emotions regarding the characters they have written. The handwriting analysis unit can also use the emotion estimation function to analyze the user's emotion regarding their handwriting and provide feedback based on the emotion. For example, the handwriting analysis unit can provide specific advice to write in a relaxed state based on the user's emotional reaction to the characters they have written. In this way, the emotion estimation function can be used to analyze the user's emotion regarding their handwriting and provide feedback based on the emotion, thereby providing feedback according to the user's emotion.

[0095] The handwriting analysis unit can compare a user's handwriting with that of other users and provide a relative evaluation. For example, the generation AI compares the user's handwriting with that of other users and provides a relative evaluation. For example, it compares characters written by the user with that of other users, provides a relative evaluation, and points out areas for improvement. The handwriting analysis unit also compares the user's handwriting with that of other users and the generation AI provides a relative evaluation. For example, it compares the characteristics of characters written by the user with that of other users and provides specific feedback. The handwriting analysis unit also compares the user's handwriting with that of other users and provides a relative evaluation. For example, it compares the style and accuracy of the characters written by the user with that of other users and suggests the optimal way to write them. In this way, by comparing the user's handwriting with that of other users and providing a relative evaluation, the user can more specifically understand areas for improvement in their own handwriting.

[0096] The handwriting analysis unit can play back the user's handwriting as an animation and provide visually easy-to-understand feedback. For example, the generation AI can play back the user's handwriting as an animation and provide visually easy-to-understand feedback. For example, the movement of the characters written by the user can be played back as an animation to visually show areas for improvement. The handwriting analysis unit can also play back the user's handwriting as an animation and provide visually easy-to-understand feedback. For example, the stroke order and movement of the characters written by the user can be played back as an animation to provide specific feedback. The handwriting analysis unit can also play back the user's handwriting as an animation and provide visually easy-to-understand feedback. For example, the movement of the characters written by the user can be played back as an animation to visually show the optimal way to write them. In this way, by playing back the user's handwriting as an animation and providing visually easy-to-understand feedback, the user can more specifically understand areas for improvement.

[0097] The handwriting analysis unit can use the emotion estimation function to analyze the user's emotion regarding their handwriting and provide emotion-based feedback for improving motivation. For example, the emotion estimation function can be used to analyze the user's emotion regarding their handwriting and provide emotion-based feedback for improving motivation. For example, the emotion estimation function can be used to analyze the user's emotion regarding their handwriting and provide emotion-based feedback for improving motivation. For example, the emotion estimation function can be used to analyze the user's emotional response to the characters they have written and provide advice for eliciting positive emotions. The handwriting analysis unit can also analyze the user's emotion in real time and provide emotion-based feedback for improving motivation. For example, the handwriting analysis unit can provide feedback for reducing negative emotions regarding the characters they have written. The handwriting analysis unit can also use the emotion estimation function to analyze the user's emotion regarding their handwriting and provide emotion-based feedback for improving motivation. For example, the handwriting analysis unit can provide specific advice for writing in a relaxed state based on the user's emotional response to the characters they have written. In this way, the emotion estimation function can be used to analyze the user's emotion regarding their handwriting and provide emotion-based feedback for improving motivation, thereby improving the user's motivation.

[0098] The processing flow of Form Example 2 will be briefly described below.

[0099] Step 1: The character pre - setting unit sets the characters that the user wants to write. For example, the user selects "the Chinese character 'wood'" on the app's setting screen. Also, the user can set the characters to be written using voice input. For example, when the user inputs "wood" by voice, the generation AI recognizes the voice and makes the setting. Additionally, the characters input by the user in handwriting can be analyzed and set. For example, when the user inputs "wood" in handwriting, the generation AI analyzes the characters and makes the setting. Step 2: The handwriting analysis unit analyzes the handwriting of the characters set by the character pre - setting unit. For example, the handwriting analysis unit analyzes the pen pressure, speed, and angle of the characters written by the user. Also, the user's handwriting can be analyzed as a 3D model to analyze the three - dimensional shape. Furthermore, the user's handwriting can be analyzed on the time axis to analyze the speed and rhythm. Step 3: The feedback providing unit provides feedback based on the results analyzed by the handwriting analysis unit. For example, when the vertical line of the character written by the user is curved, feedback such as "It would be better to write the vertical line straighter" is provided. Also, feedback corresponding to the user's emotional state can be provided. Furthermore, the user's handwriting can be played as an animation to provide feedback that is visually easy to understand.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires the voice indicating the user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0104] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

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

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. a character presetting section for setting the characters that the user wants to write; a handwriting analysis unit that analyzes the handwriting of the character set by the character presetting unit; a feedback providing unit that provides feedback based on the analysis result by the handwriting analysis unit. A system characterized by:

2. The character presetting unit includes: The system learns the user's past handwriting data and proposes a character writing style optimized for each individual user.

2. The system of claim 1.

3. The character presetting unit includes: Learn different font styles and rate them based on the style selected by the user 2. The system of claim 1.

4. The character presetting unit includes: Providing feedback according to the user's emotional state, encouraging them to write in a relaxed state 2. The system of claim 1.

5. The character presetting unit includes: Learning characters from different languages ​​and providing multilingual writing support 2. The system of claim 1.

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