System

Abstract

An object of a system according to an embodiment is to provide specific feedback and a training menu in order to efficiently support improvement of illustration.SOLUTION: A system includes a picture input unit, a comparison unit, a review generation unit, a training generation unit, a feedback unit, and a report generation unit. The picture input unit inputs an ideal picture and a current picture. A comparison part compares the ideal picture inputted by the picture input part with the present picture. The comment generation unit generates a comment based on a result of the comparison by the comparison unit. The training generation unit generates a training menu and a teaching material based on a result of the comparison by the comparison unit. The feedback unit feeds back the contents generated by the review generation unit and the training generation unit to the user. The report generation unit records a daily practice history of the user and generates a report.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 technologies do not provide sufficient specific feedback or training menus to efficiently support illustration improvement, and there is room for improvement.

[0005] The system according to the embodiment aims to provide specific feedback and training menus to efficiently support improvement in illustration skills. [Means for solving the problem]

[0006] The system according to the embodiment includes a picture input unit, a comparison unit, a commentary generation unit, a training generation unit, a feedback unit, and a report generation unit. The picture input unit inputs an ideal picture and a current picture. The comparison unit compares the ideal picture input by the picture input unit with the current picture. The commentary generation unit generates a commentary based on the comparison result by the comparison unit. The training generation unit generates a training menu and teaching materials based on the comparison result by the comparison unit. The feedback unit feeds back the content generated by the commentary generation unit and the training generation unit to the user. The report generation unit records the user's daily practice history and generates a report. [Effects of the Invention]

[0007] The system according to the embodiment can provide specific feedback and training menus to efficiently support improvement in illustration skills. [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) In the illustration improvement support system according to an embodiment of the present invention, a user inputs their ideal drawing and their current drawing, and a generation AI compares them to automatically generate a critique, training menu, and teaching materials, which are then fed back to the user. This helps the user improve their illustration skills and allows them to visually confirm their progress.

[0029] An illustration improvement support system according to an embodiment includes a picture input unit, a comparison unit, a commentary generation unit, a training generation unit, a feedback unit, and a report generation unit. The picture input unit allows a user to input an ideal picture and a current picture. For example, the user inputs a work that has the desired style or technique as the ideal picture and inputs a work currently drawn as the current picture. The comparison unit compares the ideal picture input by the picture input unit with the current picture. For example, a generation AI analyzes elements such as line drawing, color use, and composition in detail to identify technical differences and areas for improvement. The commentary generation unit generates a commentary based on the comparison results by the comparison unit. For example, the generation AI generates a commentary that includes specific technical advice and areas for improvement for the user. The training generation unit generates a training menu and teaching materials based on the comparison results by the comparison unit. For example, the generation AI suggests specific training content, such as line practice, color gradation practice, and composition practice, and provides helpful videos, articles, practice templates, etc. The feedback unit provides the content generated by the commentary generation unit and the training generation unit as feedback to the user. For example, the generation AI provides the user with generated critiques, training menus, and teaching materials to support daily practice. The report generation unit records the user's daily practice history and generates a report. For example, the generation AI creates a report including graphs and statistical data showing the user's growth, allowing the user to visually check their progress. In this way, the illustration improvement support system according to the embodiment helps the user improve their illustration skills and visually check their growth.

[0030] The picture input unit can analyze a user's past works and preferences to suggest the optimal ideal picture. For example, the picture input unit stores the user's past works in a database, and the generation AI analyzes them to understand the user's preferences and style. For example, the unit analyzes the user's preferred color usage and line drawing style and suggests the ideal picture based on that. The picture input unit also references works by other artists that the user has previously rated, and the generation AI uses that information to suggest the ideal picture. For example, it presents an ideal picture that incorporates the style and techniques of works that the user has highly rated. The picture input unit also analyzes data from art contests and exhibitions the user has participated in in the past, and the generation AI suggests the ideal picture based on the results. For example, it selects an ideal picture that reflects the characteristics of the user's award-winning work. This broadens the user's perspective by analyzing the user's past works and preferences and suggesting the optimal ideal picture.

[0031] The picture input unit can correct the resolution and color tone of the ideal picture and the current picture to make them easier to compare. For example, the picture input unit provides a function to automatically unify the resolution of the ideal picture uploaded by the user and the current picture. For example, it converts low-resolution images to high resolution to make them easier to compare. The picture input unit also automatically corrects the color tone of the ideal picture and the current picture so that the generation AI can compare them in the same color space. For example, it converts images with different color tones to the same color tone. The picture input unit also automatically adjusts the brightness and contrast of images uploaded by the user to make them easier to visually compare. For example, it brightens dark images to make details easier to see. This allows the resolution and color tone of the ideal picture and the current picture to be corrected to make them easier to compare, enabling more accurate comparison.

[0032] The picture input unit can suggest different art styles and techniques to broaden the user's horizons. For example, the generation AI analyzes the user's past works and suggests different art styles and techniques. For example, if the user prefers realism, it can suggest abstract or impressionist works. Furthermore, when the user is selecting their ideal painting, the generation AI displays samples of different art styles, allowing the user to try new styles. For example, it presents samples of digital art and watercolor paintings. Furthermore, the generation AI suggests works using different techniques based on the user's preferences. For example, if the user prefers line drawings, it can present works incorporating painting and collage techniques. This allows the generation AI to suggest different art styles and techniques, broadening the user's horizons and providing an opportunity to try new techniques.

[0033] The picture input unit can use 3D models and animations to enable a more multifaceted comparison of the ideal picture and the current picture. For example, the picture input unit converts the ideal picture uploaded by the user and the current picture into a 3D model, providing a function for comparing them in three dimensions. For example, the composition and perspective of the picture can be checked in 3D. The picture input unit also allows the generation AI to animate the ideal picture and the current picture, allowing them to be compared in motion. For example, the character's movements and facial expressions can be displayed in animation. Furthermore, when the user inputs the ideal picture and the current picture, the generation AI uses 3D models and animations to enable comparison from different angles. For example, the details and texture of the picture can be checked from multiple angles. This allows a more detailed analysis to be performed by comparing the ideal picture and the current picture from multiple angles using 3D models and animations.

[0034] The comparison unit can refer to the user's past growth history and generate a more personalized commentary. For example, the comparison unit has the generation AI store the user's past growth history in a database and refer to that data when comparing drawings to generate a personalized commentary. For example, it provides a commentary that reflects past areas for improvement and growth. The comparison unit also has the generation AI compare drawings based on the user's past growth history and generate a commentary that includes specific advice. For example, it provides a commentary that reflects past practice content and results. The comparison unit also has the generation AI analyze the user's growth history and generate a personalized commentary based on that data when comparing drawings. For example, it provides specific advice that reflects past growth patterns. In this way, by referring to the user's past growth history and generating a personalized commentary, more specific advice can be provided.

[0035] The comparison unit can refer to different art theories and techniques to generate detailed critiques that include technical background. For example, the generation AI stores different art theories and techniques in a database and references that information when comparing paintings to generate detailed critiques. For example, it provides critiques that reflect color theory and composition techniques. Furthermore, when comparing paintings, the generation AI also refers to different art theories and techniques to generate critiques that include technical background. For example, it provides specific advice on how to draw lines and apply shading. Furthermore, the generation AI analyzes different art theories and techniques and generates detailed critiques based on that information when comparing paintings. For example, it provides critiques that reflect perspective and drawing techniques. This allows the user to gain a deeper understanding by generating detailed critiques that include technical background by referring to different art theories and techniques.

[0036] The training generation unit can refer to the user's past practice data and propose an individualized training menu. For example, the generation AI stores the user's past practice data in a database, and when generating a training menu, it refers to that data to propose an individualized menu. For example, it provides an optimal training menu based on past practice content and results. Furthermore, the training generation unit generates a training menu based on the user's past practice data and proposes a menu including specific advice. For example, it provides a training menu that reflects past practice content and results. Furthermore, the training generation unit analyzes the user's practice data and proposes an individualized menu based on that data when generating a training menu. For example, it provides specific advice that reflects past practice patterns. In this way, by referring to the user's past practice data and proposing an individualized training menu, more effective practice is possible.

[0037] The training generation unit can propose optimal practice times by taking into account the user's lifestyle and schedule. For example, the generation AI stores the user's lifestyle and schedule in a database, and when generating a training menu, it refers to that data to propose optimal practice times. For example, it provides practice times that take into account the user's work or school schedule. Furthermore, the training generation unit generates a training menu based on the user's lifestyle and schedule, and proposes specific practice times. For example, it provides practice times that take into account the user's sleep patterns and meal times. Furthermore, the training generation unit analyzes the user's lifestyle and schedule, and when generating a training menu, it proposes optimal practice times based on that data. For example, it provides practice times that take into account the user's free time and break times. This allows for efficient practice by proposing optimal practice times by taking into account the user's lifestyle and schedule.

[0038] The feedback unit can refer to the user's past feedback history and generate more personalized feedback. For example, the generation AI stores the user's past feedback history in a database, and when providing feedback, the feedback unit generates personalized feedback by referring to that data. For example, feedback that reflects past areas for improvement and growth is provided. The feedback unit also provides feedback based on the user's past feedback history, generating feedback that includes specific advice. For example, feedback that reflects past practice content and results is provided. The feedback unit also analyzes the user's feedback history and generates personalized feedback based on that data when providing feedback. For example, specific advice that reflects past growth patterns is provided. In this way, more specific advice is provided by generating personalized feedback by referring to the user's past feedback history.

[0039] The feedback unit can include advice according to the user's learning style and preferences. For example, the generation AI stores the user's learning style and preferences in a database and includes advice by referring to that data when providing feedback. For example, the feedback unit provides advice tailored to the user's preferred learning method and pace. The feedback unit also generates feedback including specific advice based on the user's learning style and preferences, where the generation AI provides feedback. For example, if the user prefers visual learning, advice suited to that style is provided. The feedback unit also analyzes the user's learning style and preferences, and generates feedback including personalized advice based on that data when providing feedback. For example, if the user prefers hands-on learning, advice suited to that style is provided. This allows more effective feedback to be provided by including advice tailored to the user's learning style and preferences.

[0040] The report generation unit can analyze the user's past growth history in detail and generate an individualized growth report. For example, the generation AI stores the user's past growth history in a database, and when creating a report, analyzes the data in detail to generate an individualized growth report. For example, a report reflecting past practice content and results is provided. The report generation unit also generates a report based on the user's past growth history and generates a growth report that includes specific advice. For example, a report reflecting past practice content and results is provided. The report generation unit also analyzes the user's growth history and generates an individualized growth report based on the data when creating a report. For example, specific advice that reflects past growth patterns is provided. In this way, the user's growth can be more specifically understood by analyzing the user's past growth history in detail and generating an individualized growth report.

[0041] The report generation unit can consider the user's lifestyle and schedule to suggest optimal practice times and methods. For example, the generation AI stores the user's lifestyle and schedule in a database, and when creating a report, refers to that data to suggest optimal practice times and methods. For example, the report generation unit provides practice times that take into account the user's work or school schedule. The report generation unit also creates a report based on the user's lifestyle and schedule, and suggests specific practice times and methods. For example, the report generation unit provides practice times that take into account the user's sleep patterns and meal times. The report generation unit also analyzes the user's lifestyle and schedule, and when creating a report, suggests optimal practice times and methods based on that data. For example, the report generation unit provides practice times that take into account the user's free time and break times. This allows for efficient practice by considering the user's lifestyle and schedule to suggest optimal practice times and methods.

[0042] The report generation unit can provide a growth report from various perspectives that combine different art styles and techniques. For example, the generation AI stores different art styles and techniques in a database and refers to that information when creating a report to provide a growth report from various perspectives. For example, a growth report that combines realism and abstract painting is provided. Furthermore, the report generation unit provides a growth report from various perspectives that combines different art styles and techniques when creating a report. For example, a growth report that combines watercolor painting and digital art is provided. Furthermore, the report generation unit analyzes different art styles and techniques and provides a growth report from various perspectives based on that information when creating a report. For example, a growth report that combines painting and collage is provided. This allows the user's growth to be evaluated from multiple angles by providing a growth report from various perspectives that combines different art styles and techniques.

[0043] The report generation unit can provide a report that includes comparison data with other users and content that stimulates competitive spirit. For example, when the generation AI creates a report, the report generation unit stores the comparison data with other users in a database and includes content that stimulates competitive spirit by referring to that information. For example, the report generation unit displays practice results in a ranking format. Furthermore, when the report generation unit creates a report, the generation AI includes comparison data with other users and provides content that stimulates competitive spirit. For example, it compares the growth pace and results of other users. Furthermore, the report generation unit analyzes the comparison data with other users and includes content that stimulates competitive spirit based on that information when creating a report. For example, it compares the practice content and results of other users. In this way, by providing a report that includes comparison data with other users and includes content that stimulates competitive spirit, the user's motivation is improved.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The picture input unit allows the user to input their ideal picture and current picture. For example, the user can input a work that represents their desired style and technique as the ideal picture, and input their current drawing as the current picture. The comparison unit compares the ideal picture input by the picture input unit with the current picture. For example, the generation AI performs a detailed analysis of elements such as line drawing, color use, and composition to identify technical differences and areas for improvement. The commentary generation unit generates a commentary based on the comparison results by the comparison unit. For example, the generation AI generates a commentary that includes specific technical advice and areas for improvement for the user. The training generation unit generates training menus and teaching materials based on the comparison results by the comparison unit. For example, the generation AI suggests specific training content, such as line practice, color gradation practice, and composition practice, and provides helpful videos, articles, practice templates, etc. The feedback unit provides the content generated by the commentary generation unit and training generation unit as feedback to the user. For example, the generation AI provides the user with the commentary, training menu, and teaching materials to support their daily practice. The report generation unit records the user's daily practice history and generates reports. For example, the generation AI can create a report containing graphs and statistical data showing the user's growth, allowing the user to visually check their progress. In this way, the illustration improvement support system according to the embodiment helps the user improve their illustration skills and allows the user to visually check their progress.

[0046] The picture input unit can analyze a user's past works and preferences to suggest the optimal ideal picture. For example, the user's past works are stored in a database, and the generation AI analyzes them to understand the user's preferences and style. For example, the user's preferred color usage and line drawing style are analyzed and the ideal picture is suggested based on that. The picture input unit also references works by other artists that the user has previously rated, and the generation AI suggests the ideal picture based on that information. For example, it suggests an ideal picture that incorporates the style and techniques of works that the user has highly rated. The picture input unit also analyzes data from art contests and exhibitions the user has participated in in the past, and the generation AI suggests the ideal picture based on the results. For example, it selects an ideal picture that reflects the characteristics of the user's award-winning works. This broadens the user's perspective by analyzing the user's past works and preferences and suggesting the optimal ideal picture.

[0047] The picture input unit can correct the resolution and color tone of the ideal picture and the current picture to make them easier to compare. For example, it provides a function to automatically unify the resolution of the ideal picture uploaded by the user and the current picture. For example, it converts low-resolution images to high resolution to make them easier to compare. The picture input unit also automatically corrects the color tone of the ideal picture and the current picture so that the generative AI can compare them in the same color space. For example, it converts images with different color tones to the same color tone. The picture input unit also automatically adjusts the brightness and contrast of images uploaded by the user to make them easier to visually compare. For example, it brightens dark images to make details easier to see. This allows the resolution and color tone of the ideal picture and the current picture to be corrected to make them easier to compare, enabling more accurate comparison.

[0048] The picture input unit can suggest different art styles and techniques to broaden the user's horizons. For example, the generation AI can analyze the user's past works and suggest different art styles and techniques. For example, if the user prefers realism, it can suggest abstract or impressionist works. Furthermore, when the user is choosing their ideal painting, the generation AI can display samples of different art styles, allowing the user to try new styles. For example, it can present samples of digital art or watercolor paintings. Furthermore, the generation AI can suggest works using different techniques based on the user's preferences. For example, if the user prefers line art, it can present works incorporating painting and collage techniques. By suggesting different art styles and techniques, the generation AI can broaden the user's horizons and provide an opportunity to try new techniques.

[0049] The picture input unit can use 3D models and animations to enable a more multifaceted comparison of the ideal picture and the current picture. For example, it can convert the ideal picture uploaded by the user and the current picture into a 3D model, providing a function for comparing them in three dimensions. For example, the composition and perspective of the picture can be checked in 3D. The picture input unit also allows the generation AI to animate the ideal picture and the current picture, allowing them to be compared in motion. For example, it can display the character's movements and facial expressions in animation. Furthermore, when the user inputs the ideal picture and the current picture, the generation AI can use 3D models and animations to compare them from different angles. For example, it can check the details and texture of the picture from multiple angles. This allows for a more detailed analysis by comparing the ideal picture and the current picture from multiple angles using 3D models and animations.

[0050] The comparison unit can refer to the user's past growth history and generate more personalized commentary. For example, the generation AI stores the user's past growth history in a database and generates personalized commentary by referring to that data when comparing drawings. For example, it provides commentary that reflects past areas for improvement and growth. The comparison unit also has the generation AI compare drawings based on the user's past growth history and generate commentary that includes specific advice. For example, it provides commentary that reflects past practice content and results. The comparison unit also has the generation AI analyze the user's growth history and generate personalized commentary based on that data when comparing drawings. For example, it provides specific advice that reflects past growth patterns. In this way, by referring to the user's past growth history and generating personalized commentary, more specific advice can be provided.

[0051] The comparison unit can refer to different art theories and techniques to generate detailed critiques that include technical background. For example, the generation AI stores different art theories and techniques in a database and references that information when comparing paintings to generate detailed critiques. For example, it provides critiques that reflect color theory and composition techniques. Furthermore, when comparing paintings, the generation AI also refers to different art theories and techniques to generate critiques that include technical background. For example, it provides specific advice on how to draw lines and apply shading. Furthermore, the comparison unit analyzes different art theories and techniques and generates detailed critiques based on that information when comparing paintings. For example, it provides critiques that reflect perspective and drawing techniques. By referencing different art theories and techniques and generating detailed critiques that include technical background, the generation AI provides users with a deeper understanding.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The user inputs their ideal and current drawings into the drawing input unit. For example, the user inputs a work that has the style and technique they are aiming for as their ideal drawing, and inputs their current drawing as their current drawing. Step 2: The comparison unit compares the ideal drawing input by the drawing input unit with the current drawing. For example, the generation AI analyzes elements such as line drawing, color use, and composition in detail to identify technical differences and areas for improvement. Step 3: The comment generation unit generates a commentary based on the results of the comparison by the comparison unit. For example, the generation AI generates a commentary that includes specific technical advice and points for improvement for the user. Step 4: The training generation unit generates training menus and teaching materials based on the results of the comparison unit. For example, the generation AI suggests specific training content, such as line practice, color gradation practice, and composition practice, and provides helpful videos, articles, and practice templates. Step 5: The feedback unit provides the user with feedback based on the content generated by the comment generation unit and the training generation unit. For example, it provides the user with commentary, training menus, and teaching materials generated by the AI ​​generation unit to support daily practice. Step 6: The report generator records the user's daily practice history and generates a report. For example, the AI ​​generator creates a report containing graphs and statistical data showing the user's progress, allowing the user to visually check their progress.

[0054] (Example 2) In the illustration improvement support system according to an embodiment of the present invention, a user inputs their ideal drawing and their current drawing, and a generation AI compares them to automatically generate a critique, training menu, and teaching materials, which are then fed back to the user. This helps the user improve their illustration skills and allows them to visually confirm their progress.

[0055] An illustration improvement support system according to an embodiment includes a picture input unit, a comparison unit, a commentary generation unit, a training generation unit, a feedback unit, and a report generation unit. The picture input unit allows a user to input an ideal picture and a current picture. For example, the user inputs a work that has the desired style or technique as the ideal picture and inputs a work currently drawn as the current picture. The comparison unit compares the ideal picture input by the picture input unit with the current picture. For example, a generation AI analyzes elements such as line drawing, color use, and composition in detail to identify technical differences and areas for improvement. The commentary generation unit generates a commentary based on the comparison results by the comparison unit. For example, the generation AI generates a commentary that includes specific technical advice and areas for improvement for the user. The training generation unit generates a training menu and teaching materials based on the comparison results by the comparison unit. For example, the generation AI suggests specific training content, such as line practice, color gradation practice, and composition practice, and provides helpful videos, articles, practice templates, etc. The feedback unit provides the content generated by the commentary generation unit and the training generation unit as feedback to the user. For example, the generation AI provides the user with generated critiques, training menus, and teaching materials to support daily practice. The report generation unit records the user's daily practice history and generates a report. For example, the generation AI creates a report including graphs and statistical data showing the user's growth, allowing the user to visually check their progress. In this way, the illustration improvement support system according to the embodiment helps the user improve their illustration skills and visually check their growth.

[0056] The picture input unit can analyze a user's past works and preferences to suggest the optimal ideal picture. For example, the picture input unit stores the user's past works in a database, and the generation AI analyzes them to understand the user's preferences and style. For example, the unit analyzes the user's preferred color usage and line drawing style and suggests the ideal picture based on that. The picture input unit also references works by other artists that the user has previously rated, and the generation AI uses that information to suggest the ideal picture. For example, it presents an ideal picture that incorporates the style and techniques of works that the user has highly rated. The picture input unit also analyzes data from art contests and exhibitions the user has participated in in the past, and the generation AI suggests the ideal picture based on the results. For example, it selects an ideal picture that reflects the characteristics of the user's award-winning work. This broadens the user's perspective by analyzing the user's past works and preferences and suggesting the optimal ideal picture.

[0057] The picture input unit can correct the resolution and color tone of the ideal picture and the current picture to make them easier to compare. For example, the picture input unit provides a function to automatically unify the resolution of the ideal picture uploaded by the user and the current picture. For example, it converts low-resolution images to high resolution to make them easier to compare. The picture input unit also automatically corrects the color tone of the ideal picture and the current picture so that the generation AI can compare them in the same color space. For example, it converts images with different color tones to the same color tone. The picture input unit also automatically adjusts the brightness and contrast of images uploaded by the user to make them easier to visually compare. For example, it brightens dark images to make details easier to see. This allows the resolution and color tone of the ideal picture and the current picture to be corrected to make them easier to compare, enabling more accurate comparison.

[0058] The picture input unit uses the emotion estimation function to analyze the user's emotions when selecting an ideal picture and can suggest ideal pictures that elicit positive emotions. The picture input unit provides a function for estimating emotions by analyzing facial expressions and voice when the user is selecting an ideal picture. For example, if the user is smiling, a positive picture is suggested based on that emotion. The picture input unit also uses the emotion estimation function to calculate an emotion score when the user is selecting an ideal picture and preferentially suggest pictures with a high positive emotion score. For example, if the user is excited, a picture that matches that emotion is selected. The picture input unit also provides real-time feedback based on emotion estimation data when the user is selecting an ideal picture and suggests pictures that elicit positive emotions. For example, if the user is emotionally calm, a picture that matches that state is suggested. In this way, the user's emotions are analyzed and ideal pictures that elicit positive emotions are suggested, thereby improving the user's motivation.

[0059] The picture input unit can suggest different art styles and techniques to broaden the user's horizons. For example, the generation AI analyzes the user's past works and suggests different art styles and techniques. For example, if the user prefers realism, it can suggest abstract or impressionist works. Furthermore, when the user is selecting their ideal painting, the generation AI displays samples of different art styles, allowing the user to try new styles. For example, it presents samples of digital art and watercolor paintings. Furthermore, the generation AI suggests works using different techniques based on the user's preferences. For example, if the user prefers line drawings, it can present works incorporating painting and collage techniques. This allows the generation AI to suggest different art styles and techniques, broadening the user's horizons and providing an opportunity to try new techniques.

[0060] The picture input unit can use 3D models and animations to enable a more multifaceted comparison of the ideal picture and the current picture. For example, the picture input unit converts the ideal picture uploaded by the user and the current picture into a 3D model, providing a function for comparing them in three dimensions. For example, the composition and perspective of the picture can be checked in 3D. The picture input unit also allows the generation AI to animate the ideal picture and the current picture, allowing them to be compared in motion. For example, the character's movements and facial expressions can be displayed in animation. Furthermore, when the user inputs the ideal picture and the current picture, the generation AI uses 3D models and animations to enable comparison from different angles. For example, the details and texture of the picture can be checked from multiple angles. This allows a more detailed analysis to be performed by comparing the ideal picture and the current picture from multiple angles using 3D models and animations.

[0061] The picture input unit can use the emotion estimation function to monitor the user's emotion in real time when selecting an ideal picture and dynamically adjust the options. For example, when the user is selecting an ideal picture, the picture input unit uses the emotion estimation function to monitor the user's emotion in real time and dynamically adjust the options. For example, if the user is excited, pictures that match that emotion are preferentially displayed. The picture input unit also uses the emotion estimation function to analyze the emotion score in real time when the user is selecting an ideal picture and dynamically adjust the options. For example, if the user is relaxed, pictures that match that state are presented. The picture input unit also provides feedback in real time based on the emotion estimation data when the user is selecting an ideal picture and dynamically adjusts the options. For example, if the user is emotionally calm, pictures that match that state are presented. In this way, the user's emotion is monitored in real time and the options are dynamically adjusted to suggest the optimal ideal picture according to the user's emotion.

[0062] The comparison unit can refer to the user's past growth history and generate a more personalized commentary. For example, the comparison unit has the generation AI store the user's past growth history in a database and refer to that data when comparing drawings to generate a personalized commentary. For example, it provides a commentary that reflects past areas for improvement and growth. The comparison unit also has the generation AI compare drawings based on the user's past growth history and generate a commentary that includes specific advice. For example, it provides a commentary that reflects past practice content and results. The comparison unit also has the generation AI analyze the user's growth history and generate a personalized commentary based on that data when comparing drawings. For example, it provides specific advice that reflects past growth patterns. In this way, by referring to the user's past growth history and generating a personalized commentary, more specific advice can be provided.

[0063] The comparison unit can refer to different art theories and techniques to generate detailed critiques that include technical background. For example, the generation AI stores different art theories and techniques in a database and references that information when comparing paintings to generate detailed critiques. For example, it provides critiques that reflect color theory and composition techniques. Furthermore, when comparing paintings, the generation AI also refers to different art theories and techniques to generate critiques that include technical background. For example, it provides specific advice on how to draw lines and apply shading. Furthermore, the generation AI analyzes different art theories and techniques and generates detailed critiques based on that information when comparing paintings. For example, it provides critiques that reflect perspective and drawing techniques. This allows the user to gain a deeper understanding by generating detailed critiques that include technical background by referring to different art theories and techniques.

[0064] The comparison unit uses the emotion estimation function to analyze the emotions of the user when receiving a review and can emphasize positive feedback. For example, when the generation AI generates a review, the comparison unit uses the emotion estimation function to analyze the user's emotions and emphasize positive feedback. For example, if the user is happy, the comparison unit provides a review that reflects that emotion. The comparison unit also uses the emotion estimation function to calculate an emotion score when the user receives a review and emphasize positive feedback. For example, if the user is satisfied, the comparison unit provides a review that matches that emotion. The comparison unit also provides feedback in real time based on the emotion estimation data when the user receives a review and provides a review that elicits positive emotions. For example, if the user is emotionally calm, the comparison unit presents a review that matches that state. In this way, the user's emotions are analyzed and positive feedback is emphasized, thereby improving the user's motivation.

[0065] The training generation unit can refer to the user's past practice data and propose an individualized training menu. For example, the generation AI stores the user's past practice data in a database, and when generating a training menu, it refers to that data to propose an individualized menu. For example, it provides an optimal training menu based on past practice content and results. Furthermore, the training generation unit generates a training menu based on the user's past practice data and proposes a menu including specific advice. For example, it provides a training menu that reflects past practice content and results. Furthermore, the training generation unit analyzes the user's practice data and proposes an individualized menu based on that data when generating a training menu. For example, it provides specific advice that reflects past practice patterns. In this way, by referring to the user's past practice data and proposing an individualized training menu, more effective practice is possible.

[0066] The training generation unit can propose optimal practice times by taking into account the user's lifestyle and schedule. For example, the generation AI stores the user's lifestyle and schedule in a database, and when generating a training menu, it refers to that data to propose optimal practice times. For example, it provides practice times that take into account the user's work or school schedule. Furthermore, the training generation unit generates a training menu based on the user's lifestyle and schedule, and proposes specific practice times. For example, it provides practice times that take into account the user's sleep patterns and meal times. Furthermore, the training generation unit analyzes the user's lifestyle and schedule, and when generating a training menu, it proposes optimal practice times based on that data. For example, it provides practice times that take into account the user's free time and break times. This allows for efficient practice by proposing optimal practice times by taking into account the user's lifestyle and schedule.

[0067] The training generation unit can use the emotion estimation function to analyze the emotions of the user when receiving the training menu and generate a training menu including content that will increase motivation. For example, when the generation AI generates a training menu, the training generation unit uses the emotion estimation function to analyze the user's emotions and generate a menu including content that will increase motivation. For example, if the user is happy, the training generation unit provides a training menu that reflects that emotion. The training generation unit also uses the emotion estimation function to calculate an emotion score when the user receives the training menu and generate a menu including content that will increase motivation. For example, if the user is satisfied, the training generation unit provides a training menu that matches that emotion. The training generation unit also provides real-time feedback based on the emotion estimation data when the user receives the training menu and generates a menu including content that will increase motivation. For example, if the user is emotionally calm, the training generation unit presents a training menu that matches that state. In this way, the user's emotions are analyzed and a training menu including content that will increase motivation is generated, thereby increasing the user's motivation to practice.

[0068] The feedback unit can refer to the user's past feedback history and generate more personalized feedback. For example, the generation AI stores the user's past feedback history in a database, and when providing feedback, the feedback unit generates personalized feedback by referring to that data. For example, feedback that reflects past areas for improvement and growth is provided. The feedback unit also provides feedback based on the user's past feedback history, generating feedback that includes specific advice. For example, feedback that reflects past practice content and results is provided. The feedback unit also analyzes the user's feedback history and generates personalized feedback based on that data when providing feedback. For example, specific advice that reflects past growth patterns is provided. In this way, more specific advice is provided by generating personalized feedback by referring to the user's past feedback history.

[0069] The feedback unit can include advice according to the user's learning style and preferences. For example, the generation AI stores the user's learning style and preferences in a database and includes advice by referring to that data when providing feedback. For example, the feedback unit provides advice tailored to the user's preferred learning method and pace. The feedback unit also generates feedback including specific advice based on the user's learning style and preferences, where the generation AI provides feedback. For example, if the user prefers visual learning, advice suited to that style is provided. The feedback unit also analyzes the user's learning style and preferences, and generates feedback including personalized advice based on that data when providing feedback. For example, if the user prefers hands-on learning, advice suited to that style is provided. This allows more effective feedback to be provided by including advice tailored to the user's learning style and preferences.

[0070] The feedback unit can use the emotion estimation function to analyze the emotions of the user when receiving feedback and emphasize positive feedback. For example, when the generation AI provides feedback, the feedback unit uses the emotion estimation function to analyze the user's emotions and emphasize positive feedback. For example, if the user is happy, feedback that reflects that emotion is provided. The feedback unit also uses the emotion estimation function to calculate an emotion score when the user receives feedback and emphasize positive feedback. For example, if the user is satisfied, feedback that matches that emotion is provided. The feedback unit also provides feedback in real time based on the emotion estimation data when the user receives feedback, providing feedback that elicits positive emotions. For example, if the user is emotionally calm, feedback that matches that state is presented. In this way, the user's emotions are analyzed and positive feedback is emphasized, thereby improving the user's motivation.

[0071] The report generation unit can analyze the user's past growth history in detail and generate an individualized growth report. For example, the generation AI stores the user's past growth history in a database, and when creating a report, analyzes the data in detail to generate an individualized growth report. For example, a report reflecting past practice content and results is provided. The report generation unit also generates a report based on the user's past growth history and generates a growth report that includes specific advice. For example, a report reflecting past practice content and results is provided. The report generation unit also analyzes the user's growth history and generates an individualized growth report based on the data when creating a report. For example, specific advice that reflects past growth patterns is provided. In this way, the user's growth can be more specifically understood by analyzing the user's past growth history in detail and generating an individualized growth report.

[0072] The report generation unit can consider the user's lifestyle and schedule to suggest optimal practice times and methods. For example, the generation AI stores the user's lifestyle and schedule in a database, and when creating a report, refers to that data to suggest optimal practice times and methods. For example, the report generation unit provides practice times that take into account the user's work or school schedule. The report generation unit also creates a report based on the user's lifestyle and schedule, and suggests specific practice times and methods. For example, the report generation unit provides practice times that take into account the user's sleep patterns and meal times. The report generation unit also analyzes the user's lifestyle and schedule, and when creating a report, suggests optimal practice times and methods based on that data. For example, the report generation unit provides practice times that take into account the user's free time and break times. This allows for efficient practice by considering the user's lifestyle and schedule to suggest optimal practice times and methods.

[0073] The report generation unit can use the emotion estimation function to analyze the emotions of the user when receiving a report and generate a report including content that will increase motivation. For example, when the generation AI creates a report, the report generation unit uses the emotion estimation function to analyze the user's emotions and generate a report including content that will increase motivation. For example, if the user is happy, the report generation unit provides a report that reflects that emotion. The report generation unit also uses the emotion estimation function to calculate an emotion score when the user receives a report and generate a report including content that will increase motivation. For example, if the user is satisfied, the report generation unit provides a report that matches that emotion. The report generation unit also provides real-time feedback based on emotion estimation data when the user receives a report and generates a report including content that will increase motivation. For example, if the user is emotionally calm, the report generation unit presents a report that matches that state. In this way, by analyzing the user's emotions and generating a report including content that will increase motivation, the user's motivation to grow is increased.

[0074] The report generation unit can provide a growth report from various perspectives that combine different art styles and techniques. For example, the generation AI stores different art styles and techniques in a database and refers to that information when creating a report to provide a growth report from various perspectives. For example, a growth report that combines realism and abstract painting is provided. Furthermore, the report generation unit provides a growth report from various perspectives that combines different art styles and techniques when creating a report. For example, a growth report that combines watercolor painting and digital art is provided. Furthermore, the report generation unit analyzes different art styles and techniques and provides a growth report from various perspectives based on that information when creating a report. For example, a growth report that combines painting and collage is provided. This allows the user's growth to be evaluated from multiple angles by providing a growth report from various perspectives that combines different art styles and techniques.

[0075] The report generation unit can provide a report that includes comparison data with other users and content that stimulates competitive spirit. For example, when the generation AI creates a report, the report generation unit stores the comparison data with other users in a database and includes content that stimulates competitive spirit by referring to that information. For example, the report generation unit displays practice results in a ranking format. Furthermore, when the report generation unit creates a report, the generation AI includes comparison data with other users and provides content that stimulates competitive spirit. For example, it compares the growth pace and results of other users. Furthermore, the report generation unit analyzes the comparison data with other users and includes content that stimulates competitive spirit based on that information when creating a report. For example, it compares the practice content and results of other users. In this way, by providing a report that includes comparison data with other users and includes content that stimulates competitive spirit, the user's motivation is improved.

[0076] The report generation unit can use the emotion estimation function to monitor the user's emotions in real time when receiving a report and dynamically adjust the content of the report. For example, when the generation AI creates a report, the report generation unit uses the emotion estimation function to monitor the user's emotions in real time and dynamically adjust the content of the report. For example, if the user is feeling anxious, the report generation unit provides a report that alleviates those emotions. The report generation unit also uses the emotion estimation function to analyze the emotion score in real time when the user receives a report and dynamically adjust the content of the report. For example, if the user is happy, the report generation unit provides a report that further enhances those emotions. The report generation unit also provides feedback in real time based on the emotion estimation data when the user receives a report and provides a report that elicits positive emotions. For example, if the user is emotionally calm, the report generation unit presents a report that matches that state. In this way, the report generation unit monitors the user's emotions in real time and dynamically adjusts the content of the report to provide an optimal report according to the user's emotions.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The picture input unit allows the user to input their ideal picture and current picture. For example, the user can input a work that represents their desired style and technique as the ideal picture, and input their current drawing as the current picture. The comparison unit compares the ideal picture input by the picture input unit with the current picture. For example, the generation AI performs a detailed analysis of elements such as line drawing, color use, and composition to identify technical differences and areas for improvement. The commentary generation unit generates a commentary based on the comparison results by the comparison unit. For example, the generation AI generates a commentary that includes specific technical advice and areas for improvement for the user. The training generation unit generates training menus and teaching materials based on the comparison results by the comparison unit. For example, the generation AI suggests specific training content, such as line practice, color gradation practice, and composition practice, and provides helpful videos, articles, practice templates, etc. The feedback unit provides the content generated by the commentary generation unit and training generation unit as feedback to the user. For example, the generation AI provides the user with the commentary, training menu, and teaching materials to support their daily practice. The report generation unit records the user's daily practice history and generates reports. For example, the generation AI can create a report containing graphs and statistical data showing the user's growth, allowing the user to visually check their progress. In this way, the illustration improvement support system according to the embodiment helps the user improve their illustration skills and allows the user to visually check their progress.

[0079] The picture input unit can analyze a user's past works and preferences to suggest the optimal ideal picture. For example, the user's past works are stored in a database, and the generation AI analyzes them to understand the user's preferences and style. For example, the user's preferred color usage and line drawing style are analyzed and the ideal picture is suggested based on that. The picture input unit also references works by other artists that the user has previously rated, and the generation AI suggests the ideal picture based on that information. For example, it suggests an ideal picture that incorporates the style and techniques of works that the user has highly rated. The picture input unit also analyzes data from art contests and exhibitions the user has participated in in the past, and the generation AI suggests the ideal picture based on the results. For example, it selects an ideal picture that reflects the characteristics of the user's award-winning works. This broadens the user's perspective by analyzing the user's past works and preferences and suggesting the optimal ideal picture.

[0080] The picture input unit can correct the resolution and color tone of the ideal picture and the current picture to make them easier to compare. For example, it provides a function to automatically unify the resolution of the ideal picture uploaded by the user and the current picture. For example, it converts low-resolution images to high resolution to make them easier to compare. The picture input unit also automatically corrects the color tone of the ideal picture and the current picture so that the generative AI can compare them in the same color space. For example, it converts images with different color tones to the same color tone. The picture input unit also automatically adjusts the brightness and contrast of images uploaded by the user to make them easier to visually compare. For example, it brightens dark images to make details easier to see. This allows the resolution and color tone of the ideal picture and the current picture to be corrected to make them easier to compare, enabling more accurate comparison.

[0081] The picture input unit uses the emotion estimation function to analyze the user's emotions when selecting an ideal picture and can suggest ideal pictures that elicit positive emotions. For example, a function is provided that estimates emotions by analyzing facial expressions and voice when the user is selecting an ideal picture. For example, if the user is smiling, a positive picture is suggested based on that emotion. The picture input unit also uses the emotion estimation function to calculate an emotion score when the user is selecting an ideal picture and preferentially suggests pictures with a high positive emotion score. For example, if the user is excited, a picture that matches that emotion is selected. The picture input unit also provides real-time feedback based on emotion estimation data when the user is selecting an ideal picture, suggesting pictures that elicit positive emotions. For example, if the user is emotionally calm, a picture that matches that state is suggested. In this way, the user's emotions are analyzed and ideal pictures that elicit positive emotions are suggested, thereby improving the user's motivation.

[0082] The picture input unit can suggest different art styles and techniques to broaden the user's horizons. For example, the generation AI can analyze the user's past works and suggest different art styles and techniques. For example, if the user prefers realism, it can suggest abstract or impressionist works. Furthermore, when the user is choosing their ideal painting, the generation AI can display samples of different art styles, allowing the user to try new styles. For example, it can present samples of digital art or watercolor paintings. Furthermore, the generation AI can suggest works using different techniques based on the user's preferences. For example, if the user prefers line art, it can present works incorporating painting and collage techniques. By suggesting different art styles and techniques, the generation AI can broaden the user's horizons and provide an opportunity to try new techniques.

[0083] The picture input unit can use 3D models and animations to enable a more multifaceted comparison of the ideal picture and the current picture. For example, it can convert the ideal picture uploaded by the user and the current picture into a 3D model, providing a function for comparing them in three dimensions. For example, the composition and perspective of the picture can be checked in 3D. The picture input unit also allows the generation AI to animate the ideal picture and the current picture, allowing them to be compared in motion. For example, it can display the character's movements and facial expressions in animation. Furthermore, when the user inputs the ideal picture and the current picture, the generation AI can use 3D models and animations to compare them from different angles. For example, it can check the details and texture of the picture from multiple angles. This allows for a more detailed analysis by comparing the ideal picture and the current picture from multiple angles using 3D models and animations.

[0084] The picture input unit can use the emotion estimation function to monitor the user's emotion in real time when selecting an ideal picture and dynamically adjust the options. For example, when the user is selecting an ideal picture, the emotion estimation function is used to monitor the user's emotion in real time and dynamically adjust the options. For example, if the user is excited, pictures that match that emotion are preferentially displayed. The picture input unit also uses the emotion estimation function to analyze the emotion score in real time when the user is selecting an ideal picture and dynamically adjust the options. For example, if the user is relaxed, pictures that match that state are presented. The picture input unit also provides feedback in real time based on the emotion estimation data when the user is selecting an ideal picture and dynamically adjusts the options. For example, if the user is emotionally calm, pictures that match that state are presented. In this way, the user's emotion is monitored in real time and the options are dynamically adjusted to suggest the optimal ideal picture according to the user's emotion.

[0085] The comparison unit can refer to the user's past growth history and generate more personalized commentary. For example, the generation AI stores the user's past growth history in a database and generates personalized commentary by referring to that data when comparing drawings. For example, it provides commentary that reflects past areas for improvement and growth. The comparison unit also has the generation AI compare drawings based on the user's past growth history and generate commentary that includes specific advice. For example, it provides commentary that reflects past practice content and results. The comparison unit also has the generation AI analyze the user's growth history and generate personalized commentary based on that data when comparing drawings. For example, it provides specific advice that reflects past growth patterns. In this way, by referring to the user's past growth history and generating personalized commentary, more specific advice can be provided.

[0086] The comparison unit can refer to different art theories and techniques to generate detailed critiques that include technical background. For example, the generation AI stores different art theories and techniques in a database and references that information when comparing paintings to generate detailed critiques. For example, it provides critiques that reflect color theory and composition techniques. Furthermore, when comparing paintings, the generation AI also refers to different art theories and techniques to generate critiques that include technical background. For example, it provides specific advice on how to draw lines and apply shading. Furthermore, the comparison unit analyzes different art theories and techniques and generates detailed critiques based on that information when comparing paintings. For example, it provides critiques that reflect perspective and drawing techniques. By referencing different art theories and techniques and generating detailed critiques that include technical background, the generation AI provides users with a deeper understanding.

[0087] The comparison unit uses the emotion estimation function to analyze the emotions of the user when receiving a review and emphasize positive feedback. For example, when the generation AI generates a review, it uses the emotion estimation function to analyze the user's emotions and emphasize positive feedback. For example, if the user is happy, it provides a review that reflects that emotion. The comparison unit also uses the emotion estimation function to calculate an emotion score when the user receives a review and emphasize positive feedback. For example, if the user is satisfied, it provides a review that matches that emotion. The comparison unit also provides feedback in real time based on the emotion estimation data when the user receives a review, and provides a review that elicits positive emotions. For example, if the user is emotionally calm, it presents a review that matches that state. In this way, the user's emotions are analyzed and positive feedback is emphasized, thereby improving the user's motivation.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The user inputs their ideal and current drawings into the drawing input unit. For example, the user inputs a work that has the style and technique they are aiming for as their ideal drawing, and inputs their current drawing as their current drawing. Step 2: The comparison unit compares the ideal drawing input by the drawing input unit with the current drawing. For example, the generation AI analyzes elements such as line drawing, color use, and composition in detail to identify technical differences and areas for improvement. Step 3: The comment generation unit generates a commentary based on the results of the comparison by the comparison unit. For example, the generation AI generates a commentary that includes specific technical advice and points for improvement for the user. Step 4: The training generation unit generates training menus and teaching materials based on the results of the comparison unit. For example, the generation AI suggests specific training content, such as line practice, color gradation practice, and composition practice, and provides helpful videos, articles, and practice templates. Step 5: The feedback unit provides the user with feedback based on the content generated by the comment generation unit and the training generation unit. For example, it provides the user with commentary, training menus, and teaching materials generated by the AI ​​generation unit to support daily practice. Step 6: The report generator records the user's daily practice history and generates a report. For example, the AI ​​generator creates a report containing graphs and statistical data showing the user's progress, allowing the user to visually check their progress.

[0090] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0091] 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.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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]

[0157] 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 picture input section for inputting an ideal picture and a current picture; a comparison unit that compares the ideal picture input by the picture input unit with the current picture; a comment generation unit that generates a comment based on the comparison result by the comparison unit; a training generation unit that generates a training menu and teaching materials based on the results of the comparison by the comparison unit; a feedback unit that feeds back to the user the contents generated by the comment generation unit and the training generation unit; A report generation unit that records the user's daily practice history and generates a report. A system characterized by:

2. The picture input unit Analyze the user's past works and preferences to suggest the ideal picture that best suits them.

2. The system of claim 1.

3. The picture input unit Correct the resolution and color tone of the ideal picture and the current picture to make them easier to compare.

2. The system of claim 1.

4. The picture input unit Analyzing the emotions of the user when selecting the ideal picture, and suggesting the ideal picture that elicits positive emotions 2. The system of claim 1.

5. The picture input unit Offer different art styles and techniques to broaden the user's horizons 2. The system of claim 1.

6. The picture input unit Using 3D models and animations, the ideal image and the current image can be compared from multiple angles.

2. The system of claim 1.

Images