system

Abstract

The system according to this embodiment aims to provide opportunities to learn digital art techniques and styles, and to bring out the enjoyment of education. [Solution] The system according to the embodiment comprises a style conversion AI module, a user interface, an educational content integration unit, and an operability provision unit. The style conversion AI module performs style conversion. The user interface displays the conversion results generated by the style conversion AI module. The educational content integration unit provides educational content in cooperation with the user interface. The operability provision unit provides intuitive operability through the user interface.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] The system according to this embodiment can provide opportunities to learn digital art techniques and styles, and can enhance the enjoyment of education. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The digital art learning system according to an embodiment of the present invention is a system that utilizes generative AI to provide opportunities to learn digital art techniques and styles, and allows users to try out new techniques on their own illustrations. This digital art learning system includes a style conversion AI module, a user interface, an educational content integration unit, and an operability provision unit. The style conversion AI module uses trained AI to reproduce the techniques of famous painters such as Dali and Van Gogh and convert the user's own illustrations. The user interface works in conjunction with art apps on smartphones and tablets to easily perform the conversion process. The educational content integration unit incorporates educational content that introduces the background and significance of each style to deepen understanding. The operability provision unit provides an intuitive interface that allows users to easily select a style and convert their artwork. For example, when a user uploads their own illustration, the style conversion AI module analyzes the illustration and applies the selected painter's technique to convert it. This allows the user to try out new techniques on their own work. The user interface allows users to easily upload illustrations drawn with an art app and execute the AI-driven conversion process. The educational content integration section provides explanations of techniques such as Dali's Surrealism and Van Gogh's Post-Impressionism, allowing users to understand the historical background and significance of these techniques. The usability section allows users to simply select a painter's name, and that painter's technique will be applied. This enables users to easily change styles without complex operations. This system makes famous painting styles more accessible, bringing art closer to people. Furthermore, integration with other tools improves usability and provides new enjoyment and understanding to art education. As a result, the digital art learning system allows users to learn digital art techniques and styles and try out new techniques in their own illustrations.

[0029] The digital art learning system according to this embodiment comprises a style conversion AI module, a user interface, an educational content integration unit, and an operability provision unit. The style conversion AI module uses a trained AI to reproduce the techniques of multiple painters and convert the user's own illustrations. For example, when a user uploads their own illustration, the style conversion AI module analyzes the illustration and converts it by applying the techniques of a selected painter. The style conversion AI module can reproduce, for example, the techniques of Dali's Surrealism or Van Gogh's Post-Impressionism. The user interface can work in conjunction with art apps on smartphones and tablets to easily perform the conversion process. For example, the user interface can easily upload an illustration drawn with an art app and execute the AI-based conversion process. The user interface can, for example, work in conjunction with an art app to display the conversion results. The educational content integration unit provides educational content that introduces the background and significance of each style. For example, the educational content integration unit provides explanations of the techniques of Dali's Surrealism and Van Gogh's Post-Impressionism, allowing users to understand the historical background and significance of these techniques. The educational content integration unit provides, for example, an interface for displaying educational content. The operability provision unit provides an intuitive interface that allows users to easily select styles and convert artwork. For example, the operability provision unit allows users to apply a painter's technique simply by selecting the painter's name. The operability provision unit provides, for example, an interface that users can easily operate. This allows the digital art learning system to enable users to learn digital art techniques and styles and try out new techniques on their own illustrations. Some or all of the above-described processes in the style conversion AI module may be performed using, for example, a generative AI, or without a generative AI. For example, the style conversion AI module inputs an illustration uploaded by the user into the generative AI, which analyzes the illustration and outputs a conversion result. Some or all of the above-described processes in the user interface may be performed using, for example, an AI, or without an AI.For example, the user interface displays the conversion results using AI. Some or all of the above-described processes in the educational content integration unit may be performed using AI, or not. For example, the educational content integration unit provides educational content using AI. Some or all of the above-described processes in the operability provision unit may be performed using AI, or not. For example, the operability provision unit provides an intuitive interface using AI.

[0030] The Style Conversion AI module uses pre-trained AI to reproduce the techniques of multiple painters and transform the user's own illustrations. Specifically, when a user uploads their own illustration, the Style Conversion AI module analyzes it and applies the techniques of the selected painter to transform it. For example, the Style Conversion AI module can reproduce the techniques of Dali's Surrealism or Van Gogh's Post-Impressionism. To achieve this, the Style Conversion AI module has previously trained on a large number of painters' works and modeled the characteristic brushstrokes and color patterns of each painter. When a user uploads an illustration, the AI ​​analyzes the structure and colors of the illustration and performs the transformation based on the selected painter's technique. For example, if Dali's Surrealism is selected, the AI ​​adds the distorted shapes and fantastical elements characteristic of Surrealism to the illustration. On the other hand, if Van Gogh's Post-Impressionism is selected, the AI ​​applies Van Gogh's characteristic powerful brushstrokes and vibrant colors to the illustration. This allows users to easily transform their own illustrations into the styles of different painters. Furthermore, the Style Conversion AI module can also perform the transformation process using generative AI. The generative AI receives user-uploaded illustrations as input, analyzes them, and outputs a transformed result. Using deep learning technology, the generative AI extracts features from the illustrations and generates new illustrations based on the techniques of selected artists. This allows the style transfer AI module to provide more accurate and realistic transformation results.

[0031] The user interface integrates with art apps on smartphones and tablets, simplifying the conversion process. Specifically, the user interface allows users to easily upload illustrations created in art apps and execute an AI-powered conversion process. The user interface features an intuitive and user-friendly design, allowing users to upload illustrations and view conversion results with just a few taps. For example, a user taps the "Style Conversion" button in the art app and selects the illustration they want to convert. Then, they select the artist's name and style, and simply tap the "Start Conversion" button, and the AI ​​automatically executes the conversion process. Once the conversion is complete, the user interface displays the results, which the user can save or share. Furthermore, the user interface can also display conversion results using AI. The AI ​​displays the changes in the illustration in real time during the conversion process, allowing the user to check the progress of the conversion. This allows users to visually enjoy the conversion process and increase their anticipation for the results. The user interface also includes a function to compare conversion results, allowing users to view the original illustration and the converted illustration side by side and check the differences. This allows users to intuitively understand the effect of the conversion process.

[0032] The Educational Content Integration Department provides educational content that introduces the background and significance of each style. Specifically, it provides explanations of techniques such as Dali's Surrealism and Van Gogh's Post-Impressionism, allowing users to understand the historical background and significance of these techniques. For example, the Educational Content Integration Department explains in detail how Dali's Surrealism came into being, and the ideas and cultural background behind it. Regarding Van Gogh's Post-Impressionism, it introduces his life, the characteristics of his works, and the impact he had on the art world at the time. This allows users not only to learn techniques but also to deeply understand their meaning and value. Furthermore, the Educational Content Integration Department includes features such as quizzes and discussion forums to provide an interactive learning experience. Users can challenge themselves with quizzes to check what they have learned and exchange opinions with other users. This allows the Educational Content Integration Department to increase users' motivation to learn and promote deeper understanding. The Educational Content Integration Department can also provide educational content using AI. The AI ​​recommends the most suitable educational content based on the user's learning history and interests. For example, if a user is interested in Surrealism, the AI ​​will prioritize displaying Surrealism-related content. This allows the Educational Content Integration Department to personalize the user's learning experience and support more effective learning.

[0033] The user interface provides an intuitive interface that allows users to easily select styles and convert artwork. Specifically, the user interface allows users to simply select a painter's name, and that painter's technique will be applied. For example, the user interface displays a list of painter names and styles, allowing users to select one with a tap. After selection, the user simply taps the "Start Conversion" button, and the AI ​​automatically executes the conversion process. The user interface features a simple and intuitive design, allowing users to operate it without confusion. Furthermore, the user interface can also provide an intuitive interface using AI. The AI ​​suggests the optimal operation method based on the user's operation history and preferences. For example, it may prioritize displaying the styles of painters frequently used by the user or suggest simplifying the operation procedure. In this way, the user interface improves the user experience and makes the system more comfortable to use. The user interface also supports various operation methods such as voice control and gesture control, allowing users to choose the method that suits them best. In this way, the user interface can enhance user convenience and promote system use. Furthermore, the usability provision unit can collect user feedback and use it to improve the interface. By reflecting user opinions and requests, the usability provision unit can always provide an interface that meets the latest needs.

[0034] The Style Transfer AI module uses a pre-trained AI to reproduce the techniques of multiple painters and transform user-created illustrations. For example, when a user uploads their own illustration, the Style Transfer AI module analyzes the illustration and applies the techniques of a selected painter to transform it. The Style Transfer AI module can reproduce techniques such as Dali's Surrealism or Van Gogh's Post-Impressionism. This allows users to reproduce the techniques of multiple painters and experiment with new techniques on their own illustrations. The pre-trained AI includes, for example, an AI model that has learned the techniques of a specific painter. The pre-trained AI has learned techniques such as Dali's Surrealism or Van Gogh's Post-Impressionism. The pre-trained AI is trained using a specific dataset to reproduce the techniques of a specific painter. For example, the pre-trained AI is trained using a dataset containing Dali's works to learn Dali's Surrealism. The pre-trained AI is trained using a specific algorithm. For example, the pre-trained AI is trained using a deep learning algorithm. This allows the trained AI to reproduce the techniques of a specific painter. Some or all of the above-described processes in the style transfer AI module may be performed using a generative AI, for example, or without a generative AI. For example, the style transfer AI module inputs an illustration uploaded by the user into a generative AI, which analyzes the illustration and outputs a transformation result.

[0035] The user interface can integrate with art apps on smartphones and tablets to simplify the conversion process. For example, the user interface can easily upload illustrations drawn in an art app and execute an AI-powered conversion process. The user interface can also integrate with art apps to display the conversion results. This allows for easy integration with art apps on smartphones and tablets to simplify the conversion process. Examples of art apps on smartphones and tablets include specific art apps. Art apps on smartphones and tablets can integrate with specific art apps. Art apps on smartphones and tablets can integrate by following specific procedures. For example, the user interface is configured according to specific procedures to integrate with a specific art app. This allows the user interface to integrate with art apps on smartphones and tablets to simplify the conversion process. Some or all of the above-described processes in the user interface may be performed using AI, or not. For example, the user interface displays the conversion results using AI.

[0036] The Educational Content Integration Unit provides educational content that introduces the background and significance of each style. For example, it provides explanations of techniques such as Dali's Surrealism or Van Gogh's Post-Impressionism, enabling users to understand the historical background and significance of these techniques. The Educational Content Integration Unit also provides an interface for displaying educational content, allowing for the provision of educational content that introduces the background and significance of each style, thereby deepening understanding. The background and significance of each style include, for example, the historical background and technical characteristics of a particular style. The background and significance of each style provides specific information to clarify, for example, the historical background and technical characteristics of a particular style. For example, the Educational Content Integration Unit provides information about the historical background and technical characteristics of Dali's Surrealism, enabling users to understand the background and significance of each style. Some or all of the above processing in the Educational Content Integration Unit may be performed using AI, or not. For example, the Educational Content Integration Unit provides educational content using AI.

[0037] The operability provider provides an intuitive interface that allows users to easily select styles and convert artworks. For example, the operability provider allows users to simply select a painter's name, and that painter's technique will be applied. The operability provider provides an interface that is easy for users to operate. This provides an intuitive interface that allows users to easily select styles and convert artworks. An intuitive interface includes, for example, a specific interface design and operating method. An intuitive interface is provided based on, for example, a specific interface design and operating method. For example, the operability provider provides an interface that is easy for users to operate based on a specific interface design. This allows users to easily convert styles without complex operations. Some or all of the above-described processes in the operability provider may be performed using, for example, AI, or not. For example, the operability provider provides an intuitive interface using AI.

[0038] The style conversion AI module can select the optimal conversion method by referring to the user's past work history during style conversion. For example, the style conversion AI module may prioritize applying styles that the user has previously preferred. For example, the style conversion AI module may analyze the colors and composition of the user's past works and suggest a suitable style. For example, the style conversion AI module may suggest a style that the user has not tried before, providing an opportunity to try new techniques. This allows for more appropriate style conversion by selecting the optimal conversion method by referring to the user's past work history. The past work history may be referenced based on, for example, the format and method of reference of the stored data. For example, the past work history may be stored in a specific data format and referenced in a specific way. For example, the style conversion AI module may retrieve the user's past work history from a database and select the optimal conversion method. Some or all of the above processing in the style conversion AI module may be performed using, for example, a generative AI, or without a generative AI. For example, the style conversion AI module may input the user's past work history into a generative AI, and the generative AI may select the optimal conversion method.

[0039] The style conversion AI module can filter styles based on the user's current projects and areas of interest during the style conversion process. For example, the style conversion AI module can suggest styles that match the theme of the project the user is currently working on. For example, the style conversion AI module can prioritize the application of techniques by artists related to the user's areas of interest. For example, the style conversion AI module can suggest relevant styles based on art styles and techniques the user has recently searched for. This allows for more appropriate style conversion by filtering based on the user's current projects and areas of interest. The current projects and areas of interest are identified, for example, based on data from a project management tool or user input information. The current projects and areas of interest are obtained, for example, from a specific data source and filtered in a specific way. For example, the style conversion AI module obtains the user's current projects and areas of interest from a project management tool and suggests the optimal style. Some or all of the above processing in the style conversion AI module may be performed using, for example, a generative AI, or not using a generative AI. For example, the style conversion AI module inputs the user's current projects and areas of interest into a generative AI, and the generative AI suggests the optimal style.

[0040] The style conversion AI module can prioritize applying highly relevant styles by considering the user's geographical location during style conversion. For example, if the user is in Europe, the style conversion AI module will prioritize applying styles by European artists. For example, if the user is in Asia, the style conversion AI module will prioritize applying styles by Asian artists. For example, if the user is in a specific city, the style conversion AI module will prioritize applying styles by artists associated with that city. This enables more appropriate style conversion by prioritizing the application of highly relevant styles by considering the user's geographical location. Geographical location information is obtained, for example, based on the usage of GPS data and relevance evaluation criteria. Geographical location information is obtained, for example, in a specific way and its relevance is evaluated according to specific criteria. For example, the style conversion AI module obtains the user's geographical location information from GPS data and prioritizes applying highly relevant styles. Some or all of the above processing in the style conversion AI module may be performed using, for example, generative AI, or without using generative AI. For example, the style conversion AI module inputs the user's geographical location information into the generating AI, which then prioritizes applying the most relevant styles.

[0041] The style conversion AI module can analyze a user's social media activity and apply relevant styles during style conversion. For example, the style conversion AI module may prioritize applying the styles of artists the user follows on social media. For example, the style conversion AI module may suggest relevant styles based on the styles of artwork the user has shared on social media. For example, the style conversion AI module may prioritize applying the styles of artwork the user has "liked" on social media. This allows for more appropriate style conversion by analyzing the user's social media activity and applying relevant styles. Social media activity is analyzed based on, for example, methods for analyzing post content and criteria for evaluating relevance. Social media activity is analyzed in, for example, a specific way and its relevance is evaluated according to specific criteria. For example, the style conversion AI module analyzes the user's social media activity and suggests relevant styles. Some or all of the above processing in the style conversion AI module may be performed using, for example, a generative AI, or without a generative AI. For example, the style conversion AI module inputs the user's social media activity into a generative AI, and the generative AI suggests relevant styles.

[0042] The user interface can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the user interface may prioritize displaying interface designs that the user has previously preferred. For example, the user interface may place the most frequently used functions in a prominent position based on the user's past operation history. For example, the user interface may automatically apply settings that the user has customized in the past. This allows for a more appropriate display by selecting the optimal display method by referring to the user's past operation history. Past operation history is referenced based on, for example, the method of saving and analyzing operation logs. Past operation history is saved in a specific way and analyzed in a specific way. For example, the user interface retrieves the user's past operation history from a database and selects the optimal display method. Some or all of the above processing in the user interface may be performed using, for example, AI, or not using AI. For example, the user interface inputs the user's past operation history into a generating AI, and the generating AI selects the optimal display method.

[0043] The user interface can be customized based on the user's current device usage when the interface is displayed. For example, if the user is using a smartphone, the user interface provides an interface optimized for the screen size. For example, if the user is using a tablet, the user interface provides an interface optimized for a larger screen. For example, if the user is using a desktop, the user interface provides an interface optimized for mouse operation. This allows for a more appropriate display by customizing based on the user's current device usage. Current device usage is identified, for example, based on the type of device and the method of monitoring usage. Current device usage is identified, for example, in a specific way and customized according to specific criteria. For example, the user interface monitors the user's current device usage and provides the optimal display method. Some or all of the above processing in the user interface may be performed using AI, for example, or without AI. For example, the user interface inputs the user's device information into a generating AI, and the generating AI provides the optimal display method.

[0044] The user interface can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the user interface provides a display method optimized for the screen size. For example, if the user is using a tablet, the user interface provides a display method optimized for a larger screen. For example, if the user is using a desktop, the user interface provides a display method optimized for mouse operation. This allows for a more appropriate display by selecting the optimal display method considering the user's device information. Device information is acquired, for example, based on device specifications or usage monitoring methods. Device information is acquired, for example, by a specific method, and the optimal display method is selected according to specific criteria. For example, the user interface monitors the user's device information and provides the optimal display method. Some or all of the above processing in the user interface may be performed using AI, for example, or without AI. For example, the user interface inputs the user's device information into a generating AI, and the generating AI provides the optimal display method.

[0045] The user interface can analyze the user's social media activity and display relevant information when the interface is displayed. For example, the user interface can display the latest works of artists the user follows on social media. For example, the user interface can display information related to artwork the user has shared on social media. For example, the user interface can display information related to artwork the user has "liked" on social media. This allows for more appropriate display by analyzing the user's social media activity and displaying relevant information. Social media activity is analyzed, for example, based on methods for analyzing post content and criteria for evaluating relevance. Social media activity is analyzed, for example, in a specific way, and relevant information is displayed according to specific criteria. For example, the user interface analyzes the user's social media activity and displays relevant information. Some or all of the above processing in the user interface may be performed using AI, for example, or without AI. For example, the user interface inputs the user's social media activity into a generating AI, and the generating AI displays relevant information.

[0046] The educational content integration unit can select the most suitable content by referring to the user's past learning history when displaying educational content. For example, the educational content integration unit prioritizes displaying content related to what the user has previously learned. For example, the educational content integration unit suggests what the user should learn next based on their past learning history. For example, the educational content integration unit displays content related to topics the user has previously shown interest in. This enables more appropriate education by selecting the most suitable content by referring to the user's past learning history. Past learning history is referenced, for example, based on the method of saving and analyzing learning logs. Past learning history is saved in a specific way and analyzed in a specific way. For example, the educational content integration unit retrieves the user's past learning history from a database and selects the most suitable content. Some or all of the above processing in the educational content integration unit may be performed using AI, for example, or without AI. For example, the educational content integration unit inputs the user's past learning history into a generating AI, and the generating AI selects the most suitable content.

[0047] The educational content integration unit can customize educational content based on the user's current learning status when displaying it. For example, the educational content integration unit displays content related to what the user is currently learning. For example, the educational content integration unit suggests what the user should learn next according to their learning progress. For example, the educational content integration unit suggests the optimal learning method based on the user's current learning status. This makes it possible to provide more appropriate education by customizing based on the user's current learning status. The current learning status is identified, for example, based on a method for monitoring learning progress or a criterion for customization. The current learning status is identified, for example, in a specific way and customized according to specific criteria. For example, the educational content integration unit monitors the user's current learning status and provides the optimal content. Some or all of the above processes in the educational content integration unit may be performed using AI, for example, or without AI. For example, the educational content integration unit inputs the user's learning status into a generating AI, and the generating AI provides the optimal content.

[0048] The educational content integration unit can prioritize displaying highly relevant content when displaying educational content, taking into account the user's geographical location information. For example, if the user is in Europe, the educational content integration unit will prioritize displaying educational content related to European art. For example, if the user is in Asia, the educational content integration unit will prioritize displaying educational content related to Asian art. For example, if the user is in a specific city, the educational content integration unit will prioritize displaying educational content related to art in that city. This enables more appropriate education by prioritizing the display of highly relevant content, taking into account the user's geographical location information. Geographical location information is obtained, for example, based on the usage of GPS data and relevance evaluation criteria. Geographical location information is obtained, for example, in a specific way and its relevance is evaluated according to specific criteria. For example, the educational content integration unit obtains the user's geographical location information from GPS data and prioritizes the display of highly relevant content. Some or all of the above processing in the educational content integration unit may be performed using AI, for example, or without AI. For example, the educational content integration unit inputs the user's geographical location information into a generating AI, and the generating AI prioritizes the display of highly relevant content.

[0049] The educational content integration unit can analyze a user's social media activity and display relevant content when displaying educational content. For example, the educational content integration unit can display educational content related to artists the user follows on social media. For example, the educational content integration unit can display educational content related to artwork the user has shared on social media. For example, the educational content integration unit can display educational content related to artwork the user has "liked" on social media. This enables more appropriate education by analyzing the user's social media activity and displaying relevant content. Social media activity is analyzed, for example, based on methods for analyzing posted content and criteria for evaluating relevance. Social media activity is analyzed, for example, in a specific way, and relevant content is displayed according to specific criteria. For example, the educational content integration unit analyzes the user's social media activity and displays relevant content. Some or all of the above processing in the educational content integration unit may be performed using AI, for example, or without AI. For example, the educational content integration unit inputs the user's social media activity into a generating AI, and the generating AI displays relevant content.

[0050] The operability provider can select the optimal operation method by referring to the user's past operation history when providing operability. For example, the operability provider may prioritize providing operation methods that the user has preferred to use in the past. For example, the operability provider may suggest the most frequently used operation method from the user's past operation history. For example, the operability provider may automatically apply settings that the user has customized in the past. This enables more appropriate operation by selecting the optimal operation method by referring to the user's past operation history. Past operation history is referred to, for example, based on the method of saving and analyzing operation logs. Past operation history is saved in a specific way and analyzed in a specific way. For example, the operability provider retrieves the user's past operation history from a database and selects the optimal operation method. Some or all of the above processing in the operability provider may be performed using AI, for example, or without AI. For example, the operability provider inputs the user's past operation history into a generating AI, and the generating AI selects the optimal operation method.

[0051] The operability provider can customize the operability based on the user's current device usage when providing operability. For example, if the user is using a smartphone, the operability provider provides an operation method optimized for the screen size. For example, if the user is using a tablet, the operability provider provides an operation method optimized for a large screen. For example, if the user is using a desktop, the operability provider provides an operation method optimized for mouse operation. This allows for more appropriate operation by customizing based on the user's current device usage. The current device usage is identified, for example, based on the type of device and the method of monitoring usage. The current device usage is identified, for example, in a specific way and customized according to specific criteria. For example, the operability provider monitors the user's current device usage and provides the optimal operation method. Some or all of the above processing in the operability provider may be performed using AI, for example, or without AI. For example, the operability provider inputs the user's device information into a generating AI, and the generating AI provides the optimal operation method.

[0052] The operability provider can select the optimal operating method when providing operability, taking into account the user's geographical location information. For example, if the user is in Europe, the operability provider will provide an operating method optimized for European users. For example, if the user is in Asia, the operability provider will provide an operating method optimized for Asian users. For example, if the user is in a specific city, the operability provider will provide an operating method related to that city. This allows for more appropriate operation by selecting the optimal operating method considering the user's geographical location information. Geographical location information is obtained, for example, based on the usage method of GPS data and relevance evaluation criteria. Geographical location information is obtained, for example, in a specific way and its relevance is evaluated according to specific criteria. For example, the operability provider obtains the user's geographical location information from GPS data and provides the optimal operating method. Some or all of the above processing in the operability provider may be performed using AI, for example, or without AI. For example, the operability provider inputs the user's geographical location information into a generating AI, and the generating AI provides the optimal operating method.

[0053] The operability provider can analyze the user's social media activity and suggest relevant operating methods when providing operability. For example, the operability provider may prioritize providing operating methods for artists the user follows on social media. For example, the operability provider may suggest operating methods related to artwork the user has shared on social media. For example, the operability provider may provide operating methods related to artwork the user has "liked" on social media. This allows for more appropriate operation by analyzing the user's social media activity and suggesting relevant operating methods. Social media activity is analyzed, for example, based on methods for analyzing post content and criteria for evaluating relevance. Social media activity is analyzed, for example, in a specific way, and relevant operating methods are suggested based on specific criteria. For example, the operability provider analyzes the user's social media activity and suggests relevant operating methods. Some or all of the above processing in the operability provider may be performed using AI, for example, or without AI. For example, the operability provider inputs the user's social media activity into a generating AI, and the generating AI suggests relevant operating methods.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The style conversion AI module can select the optimal conversion method by referring to the user's past work history. For example, it can prioritize applying styles that the user has previously preferred. It can analyze the colors and composition of the user's past works and suggest a suitable style. It can also suggest styles that the user has not tried before, providing an opportunity to try new techniques. By referring to the user's past work history to select the optimal conversion method, more appropriate style conversion becomes possible. The past work history is referenced based on the format and method of referencing the saved data.

[0056] The style conversion AI module can filter based on the user's current projects and areas of interest. For example, it can suggest styles that match the theme of the project the user is currently working on. It can prioritize the application of techniques by artists related to the user's areas of interest. It can also suggest relevant styles based on art styles and techniques the user has recently searched for. This allows for more appropriate style conversion by filtering based on the user's current projects and areas of interest. The current projects and areas of interest are identified based on data from project management tools and user input.

[0057] The style conversion AI module can prioritize applying highly relevant styles by considering the user's geographical location. For example, if the user is in Europe, it can prioritize the styles of European artists. If the user is in Asia, it can prioritize the styles of Asian artists. If the user is in a specific city, it can also prioritize the styles of artists associated with that city. This allows for more appropriate style conversion by prioritizing highly relevant styles while considering the user's geographical location. Geographical location information is obtained based on the usage of GPS data and relevance evaluation criteria.

[0058] The user interface can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, it can prioritize displaying interface designs that the user has previously preferred. Based on the user's past operation history, the most frequently used functions can be placed in a prominent position. It can also automatically apply settings that the user has customized in the past. As a result, a more appropriate display is possible by selecting the optimal display method by referring to the user's past operation history. Past operation history is referenced based on the method of saving and analyzing operation logs.

[0059] The educational content integration unit can select the most suitable content when displaying educational content by referring to the user's past learning history. For example, it can prioritize displaying content related to what the user has previously learned. It can also suggest what the user should learn next based on their past learning history. It can also display content related to topics the user has shown interest in in the past. This enables more appropriate education by selecting the most suitable content by referring to the user's past learning history. Past learning history is referenced based on the learning log storage and analysis methods.

[0060] The user interface provision unit can analyze the user's social media activity and suggest relevant operation methods when providing user interfaces. For example, it can prioritize providing operation methods for artists the user follows on social media. It can also suggest operation methods related to artwork the user has shared on social media. It can even provide operation methods related to artwork the user has "liked" on social media. By analyzing the user's social media activity and suggesting relevant operation methods, more appropriate operation becomes possible. Social media activity is analyzed based on methods for analyzing posted content and criteria for evaluating relevance.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The Style Transfer AI module uses pre-trained AI to reproduce the techniques of multiple painters and transform the user's illustration. For example, when a user uploads their own illustration, the module analyzes it and applies the techniques of a selected painter to transform it. The Style Transfer AI module can reproduce techniques such as Dali's Surrealism and Van Gogh's Post-Impressionism. Step 2: The user interface integrates with art apps on smartphones and tablets, making the conversion process easy. For example, users can easily upload illustrations drawn in an art app, run the AI-powered conversion process, and view the results. Step 3: The Educational Content Integration Department provides educational content that introduces the background and significance of each style. For example, it provides explanations of the techniques of Dali's Surrealism and Van Gogh's Post-Impressionism, allowing users to understand the historical background and significance of these techniques. Step 4: The operability section provides an intuitive interface that allows users to easily select styles and convert artworks. For example, the user can simply select a painter's name, and that painter's technique will be applied.

[0063] (Example of form 2) The digital art learning system according to an embodiment of the present invention is a system that utilizes generative AI to provide opportunities to learn digital art techniques and styles, and allows users to try out new techniques on their own illustrations. This digital art learning system includes a style conversion AI module, a user interface, an educational content integration unit, and an operability provision unit. The style conversion AI module uses trained AI to reproduce the techniques of famous painters such as Dali and Van Gogh and convert the user's own illustrations. The user interface works in conjunction with art apps on smartphones and tablets to easily perform the conversion process. The educational content integration unit incorporates educational content that introduces the background and significance of each style to deepen understanding. The operability provision unit provides an intuitive interface that allows users to easily select a style and convert their artwork. For example, when a user uploads their own illustration, the style conversion AI module analyzes the illustration and applies the selected painter's technique to convert it. This allows the user to try out new techniques on their own work. The user interface allows users to easily upload illustrations drawn with an art app and execute the AI-driven conversion process. The educational content integration section provides explanations of techniques such as Dali's Surrealism and Van Gogh's Post-Impressionism, allowing users to understand the historical background and significance of these techniques. The usability section allows users to simply select a painter's name, and that painter's technique will be applied. This enables users to easily change styles without complex operations. This system makes famous painting styles more accessible, bringing art closer to people. Furthermore, integration with other tools improves usability and provides new enjoyment and understanding to art education. As a result, the digital art learning system allows users to learn digital art techniques and styles and try out new techniques in their own illustrations.

[0064] The digital art learning system according to this embodiment comprises a style conversion AI module, a user interface, an educational content integration unit, and an operability provision unit. The style conversion AI module uses a trained AI to reproduce the techniques of multiple painters and convert the user's own illustrations. For example, when a user uploads their own illustration, the style conversion AI module analyzes the illustration and converts it by applying the techniques of a selected painter. The style conversion AI module can reproduce, for example, the techniques of Dali's Surrealism or Van Gogh's Post-Impressionism. The user interface can work in conjunction with art apps on smartphones and tablets to easily perform the conversion process. For example, the user interface can easily upload an illustration drawn with an art app and execute the AI-based conversion process. The user interface can, for example, work in conjunction with an art app to display the conversion results. The educational content integration unit provides educational content that introduces the background and significance of each style. For example, the educational content integration unit provides explanations of the techniques of Dali's Surrealism and Van Gogh's Post-Impressionism, allowing users to understand the historical background and significance of these techniques. The educational content integration unit provides, for example, an interface for displaying educational content. The operability provision unit provides an intuitive interface that allows users to easily select styles and convert artwork. For example, the operability provision unit allows users to apply a painter's technique simply by selecting the painter's name. The operability provision unit provides, for example, an interface that users can easily operate. This allows the digital art learning system to enable users to learn digital art techniques and styles and try out new techniques on their own illustrations. Some or all of the above-described processes in the style conversion AI module may be performed using, for example, a generative AI, or without a generative AI. For example, the style conversion AI module inputs an illustration uploaded by the user into the generative AI, which analyzes the illustration and outputs a conversion result. Some or all of the above-described processes in the user interface may be performed using, for example, an AI, or without an AI.For example, the user interface displays the conversion results using AI. Some or all of the above-described processes in the educational content integration unit may be performed using AI, or not. For example, the educational content integration unit provides educational content using AI. Some or all of the above-described processes in the operability provision unit may be performed using AI, or not. For example, the operability provision unit provides an intuitive interface using AI.

[0065] The Style Conversion AI module uses pre-trained AI to reproduce the techniques of multiple painters and transform the user's own illustrations. Specifically, when a user uploads their own illustration, the Style Conversion AI module analyzes it and applies the techniques of the selected painter to transform it. For example, the Style Conversion AI module can reproduce the techniques of Dali's Surrealism or Van Gogh's Post-Impressionism. To achieve this, the Style Conversion AI module has previously trained on a large number of painters' works and modeled the characteristic brushstrokes and color patterns of each painter. When a user uploads an illustration, the AI ​​analyzes the structure and colors of the illustration and performs the transformation based on the selected painter's technique. For example, if Dali's Surrealism is selected, the AI ​​adds the distorted shapes and fantastical elements characteristic of Surrealism to the illustration. On the other hand, if Van Gogh's Post-Impressionism is selected, the AI ​​applies Van Gogh's characteristic powerful brushstrokes and vibrant colors to the illustration. This allows users to easily transform their own illustrations into the styles of different painters. Furthermore, the Style Conversion AI module can also perform the transformation process using generative AI. The generative AI receives user-uploaded illustrations as input, analyzes them, and outputs a transformed result. Using deep learning technology, the generative AI extracts features from the illustrations and generates new illustrations based on the techniques of selected artists. This allows the style transfer AI module to provide more accurate and realistic transformation results.

[0066] The user interface integrates with art apps on smartphones and tablets, simplifying the conversion process. Specifically, the user interface allows users to easily upload illustrations created in art apps and execute an AI-powered conversion process. The user interface features an intuitive and user-friendly design, allowing users to upload illustrations and view conversion results with just a few taps. For example, a user taps the "Style Conversion" button in the art app and selects the illustration they want to convert. Then, they select the artist's name and style, and simply tap the "Start Conversion" button, and the AI ​​automatically executes the conversion process. Once the conversion is complete, the user interface displays the results, which the user can save or share. Furthermore, the user interface can also display conversion results using AI. The AI ​​displays the changes in the illustration in real time during the conversion process, allowing the user to check the progress of the conversion. This allows users to visually enjoy the conversion process and increase their anticipation for the results. The user interface also includes a function to compare conversion results, allowing users to view the original illustration and the converted illustration side by side and check the differences. This allows users to intuitively understand the effect of the conversion process.

[0067] The Educational Content Integration Department provides educational content that introduces the background and significance of each style. Specifically, it provides explanations of techniques such as Dali's Surrealism and Van Gogh's Post-Impressionism, allowing users to understand the historical background and significance of these techniques. For example, the Educational Content Integration Department explains in detail how Dali's Surrealism came into being, and the ideas and cultural background behind it. Regarding Van Gogh's Post-Impressionism, it introduces his life, the characteristics of his works, and the impact he had on the art world at the time. This allows users not only to learn techniques but also to deeply understand their meaning and value. Furthermore, the Educational Content Integration Department includes features such as quizzes and discussion forums to provide an interactive learning experience. Users can challenge themselves with quizzes to check what they have learned and exchange opinions with other users. This allows the Educational Content Integration Department to increase users' motivation to learn and promote deeper understanding. The Educational Content Integration Department can also provide educational content using AI. The AI ​​recommends the most suitable educational content based on the user's learning history and interests. For example, if a user is interested in Surrealism, the AI ​​will prioritize displaying Surrealism-related content. This allows the Educational Content Integration Department to personalize the user's learning experience and support more effective learning.

[0068] The user interface provides an intuitive interface that allows users to easily select styles and convert artwork. Specifically, the user interface allows users to simply select a painter's name, and that painter's technique will be applied. For example, the user interface displays a list of painter names and styles, allowing users to select one with a tap. After selection, the user simply taps the "Start Conversion" button, and the AI ​​automatically executes the conversion process. The user interface features a simple and intuitive design, allowing users to operate it without confusion. Furthermore, the user interface can also provide an intuitive interface using AI. The AI ​​suggests the optimal operation method based on the user's operation history and preferences. For example, it may prioritize displaying the styles of painters frequently used by the user or suggest simplifying the operation procedure. In this way, the user interface improves the user experience and makes the system more comfortable to use. The user interface also supports various operation methods such as voice control and gesture control, allowing users to choose the method that suits them best. In this way, the user interface can enhance user convenience and promote system use. Furthermore, the usability provision unit can collect user feedback and use it to improve the interface. By reflecting user opinions and requests, the usability provision unit can always provide an interface that meets the latest needs.

[0069] The Style Transfer AI module uses a pre-trained AI to reproduce the techniques of multiple painters and transform user-created illustrations. For example, when a user uploads their own illustration, the Style Transfer AI module analyzes the illustration and applies the techniques of a selected painter to transform it. The Style Transfer AI module can reproduce techniques such as Dali's Surrealism or Van Gogh's Post-Impressionism. This allows users to reproduce the techniques of multiple painters and experiment with new techniques on their own illustrations. The pre-trained AI includes, for example, an AI model that has learned the techniques of a specific painter. The pre-trained AI has learned techniques such as Dali's Surrealism or Van Gogh's Post-Impressionism. The pre-trained AI is trained using a specific dataset to reproduce the techniques of a specific painter. For example, the pre-trained AI is trained using a dataset containing Dali's works to learn Dali's Surrealism. The pre-trained AI is trained using a specific algorithm. For example, the pre-trained AI is trained using a deep learning algorithm. This allows the trained AI to reproduce the techniques of a specific painter. Some or all of the above-described processes in the style transfer AI module may be performed using a generative AI, for example, or without a generative AI. For example, the style transfer AI module inputs an illustration uploaded by the user into a generative AI, which analyzes the illustration and outputs a transformation result.

[0070] The user interface can integrate with art apps on smartphones and tablets to simplify the conversion process. For example, the user interface can easily upload illustrations drawn in an art app and execute an AI-powered conversion process. The user interface can also integrate with art apps to display the conversion results. This allows for easy integration with art apps on smartphones and tablets to simplify the conversion process. Examples of art apps on smartphones and tablets include specific art apps. Art apps on smartphones and tablets can integrate with specific art apps. Art apps on smartphones and tablets can integrate by following specific procedures. For example, the user interface is configured according to specific procedures to integrate with a specific art app. This allows the user interface to integrate with art apps on smartphones and tablets to simplify the conversion process. Some or all of the above-described processes in the user interface may be performed using AI, or not. For example, the user interface displays the conversion results using AI.

[0071] The Educational Content Integration Unit provides educational content that introduces the background and significance of each style. For example, it provides explanations of techniques such as Dali's Surrealism or Van Gogh's Post-Impressionism, enabling users to understand the historical background and significance of these techniques. The Educational Content Integration Unit also provides an interface for displaying educational content, allowing for the provision of educational content that introduces the background and significance of each style, thereby deepening understanding. The background and significance of each style include, for example, the historical background and technical characteristics of a particular style. The background and significance of each style provides specific information to clarify, for example, the historical background and technical characteristics of a particular style. For example, the Educational Content Integration Unit provides information about the historical background and technical characteristics of Dali's Surrealism, enabling users to understand the background and significance of each style. Some or all of the above processing in the Educational Content Integration Unit may be performed using AI, or not. For example, the Educational Content Integration Unit provides educational content using AI.

[0072] The operability provider provides an intuitive interface that allows users to easily select styles and convert artworks. For example, the operability provider allows users to simply select a painter's name, and that painter's technique will be applied. The operability provider provides an interface that is easy for users to operate. This provides an intuitive interface that allows users to easily select styles and convert artworks. An intuitive interface includes, for example, a specific interface design and operating method. An intuitive interface is provided based on, for example, a specific interface design and operating method. For example, the operability provider provides an interface that is easy for users to operate based on a specific interface design. This allows users to easily convert styles without complex operations. Some or all of the above-described processes in the operability provider may be performed using, for example, AI, or not. For example, the operability provider provides an intuitive interface using AI.

[0073] The style transformation AI module can estimate the user's emotions and adjust the degree to which style transformation is applied based on the estimated emotions. For example, if the user is relaxed, the style transformation AI module will apply a calm color scheme and a soft touch style. If the user is excited, the style transformation AI module will apply a vibrant color scheme and a bold brushstroke style. If the user is sad, the style transformation AI module will apply a calm color scheme and a delicate touch style. This allows for more appropriate style transformation by adjusting the degree to which style transformation is applied based on the user's emotions. The user's emotions are estimated by methods such as facial recognition, speech analysis, and text analysis. Emotion estimation is achieved using, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the style transformation AI module may be performed using, for example, a generative AI, or not using a generative AI. For example, the style transformation AI module inputs the user's emotional data into the generating AI, which then adjusts the degree to which the style transformation is applied based on the emotions.

[0074] The style conversion AI module can select the optimal conversion method by referring to the user's past work history during style conversion. For example, the style conversion AI module may prioritize applying styles that the user has previously preferred. For example, the style conversion AI module may analyze the colors and composition of the user's past works and suggest a suitable style. For example, the style conversion AI module may suggest a style that the user has not tried before, providing an opportunity to try new techniques. This allows for more appropriate style conversion by selecting the optimal conversion method by referring to the user's past work history. The past work history may be referenced based on, for example, the format and method of reference of the stored data. For example, the past work history may be stored in a specific data format and referenced in a specific way. For example, the style conversion AI module may retrieve the user's past work history from a database and select the optimal conversion method. Some or all of the above processing in the style conversion AI module may be performed using, for example, a generative AI, or without a generative AI. For example, the style conversion AI module may input the user's past work history into a generative AI, and the generative AI may select the optimal conversion method.

[0075] The style conversion AI module can filter styles based on the user's current projects and areas of interest during the style conversion process. For example, the style conversion AI module can suggest styles that match the theme of the project the user is currently working on. For example, the style conversion AI module can prioritize the application of techniques by artists related to the user's areas of interest. For example, the style conversion AI module can suggest relevant styles based on art styles and techniques the user has recently searched for. This allows for more appropriate style conversion by filtering based on the user's current projects and areas of interest. The current projects and areas of interest are identified, for example, based on data from a project management tool or user input information. The current projects and areas of interest are obtained, for example, from a specific data source and filtered in a specific way. For example, the style conversion AI module obtains the user's current projects and areas of interest from a project management tool and suggests the optimal style. Some or all of the above processing in the style conversion AI module may be performed using, for example, a generative AI, or not using a generative AI. For example, the style conversion AI module inputs the user's current projects and areas of interest into a generative AI, and the generative AI suggests the optimal style.

[0076] The style conversion AI module can estimate the user's emotions and determine the priority of styles to convert based on the estimated emotions. For example, if the user is relaxed, the style conversion AI module will prioritize applying calm styles. If the user is excited, the style conversion AI module will prioritize applying stimulating styles. If the user is sad, the style conversion AI module will prioritize applying calm styles. This allows for more appropriate style conversion by determining the priority of styles to convert based on the user's emotions. The emotion-based priority is determined, for example, by an algorithm based on the intensity and type of emotion. Emotion estimation is achieved, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the style conversion AI module may be performed, for example, using a generative AI or not using a generative AI. For example, the style conversion AI module inputs user emotion data into a generative AI, and the generative AI determines the priority of styles based on the emotion.

[0077] The style conversion AI module can prioritize applying highly relevant styles by considering the user's geographical location during style conversion. For example, if the user is in Europe, the style conversion AI module will prioritize applying styles by European artists. For example, if the user is in Asia, the style conversion AI module will prioritize applying styles by Asian artists. For example, if the user is in a specific city, the style conversion AI module will prioritize applying styles by artists associated with that city. This enables more appropriate style conversion by prioritizing the application of highly relevant styles by considering the user's geographical location. Geographical location information is obtained, for example, based on the usage of GPS data and relevance evaluation criteria. Geographical location information is obtained, for example, in a specific way and its relevance is evaluated according to specific criteria. For example, the style conversion AI module obtains the user's geographical location information from GPS data and prioritizes applying highly relevant styles. Some or all of the above processing in the style conversion AI module may be performed using, for example, generative AI, or without using generative AI. For example, the style conversion AI module inputs the user's geographical location information into the generating AI, which then prioritizes applying the most relevant styles.

[0078] The style conversion AI module can analyze a user's social media activity and apply relevant styles during style conversion. For example, the style conversion AI module may prioritize applying the styles of artists the user follows on social media. For example, the style conversion AI module may suggest relevant styles based on the styles of artwork the user has shared on social media. For example, the style conversion AI module may prioritize applying the styles of artwork the user has "liked" on social media. This allows for more appropriate style conversion by analyzing the user's social media activity and applying relevant styles. Social media activity is analyzed based on, for example, methods for analyzing post content and criteria for evaluating relevance. Social media activity is analyzed in, for example, a specific way and its relevance is evaluated according to specific criteria. For example, the style conversion AI module analyzes the user's social media activity and suggests relevant styles. Some or all of the above processing in the style conversion AI module may be performed using, for example, a generative AI, or without a generative AI. For example, the style conversion AI module inputs the user's social media activity into a generative AI, and the generative AI suggests relevant styles.

[0079] A user interface can estimate the user's emotions and adjust how the interface is displayed based on those emotions. For example, if the user is tense, the interface may provide a calm color scheme. If the user is enjoying themselves, the interface may provide a bright color scheme. If the user is tired, the interface may provide a simple and highly visible interface. This allows for a more appropriate display by adjusting how the interface is displayed based on the user's emotions. The emotion-based interface display method is performed based on adjustment criteria such as changing colors or changing the layout. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the user interface may be performed using AI, or not using AI. For example, the user interface inputs user emotion data into a generative AI, and the generative AI adjusts how the interface is displayed based on the emotion.

[0080] The user interface can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the user interface may prioritize displaying interface designs that the user has previously preferred. For example, the user interface may place the most frequently used functions in a prominent position based on the user's past operation history. For example, the user interface may automatically apply settings that the user has customized in the past. This allows for a more appropriate display by selecting the optimal display method by referring to the user's past operation history. Past operation history is referenced based on, for example, the method of saving and analyzing operation logs. Past operation history is saved in a specific way and analyzed in a specific way. For example, the user interface retrieves the user's past operation history from a database and selects the optimal display method. Some or all of the above processing in the user interface may be performed using, for example, AI, or not using AI. For example, the user interface inputs the user's past operation history into a generating AI, and the generating AI selects the optimal display method.

[0081] The user interface can be customized based on the user's current device usage when the interface is displayed. For example, if the user is using a smartphone, the user interface provides an interface optimized for the screen size. For example, if the user is using a tablet, the user interface provides an interface optimized for a larger screen. For example, if the user is using a desktop, the user interface provides an interface optimized for mouse operation. This allows for a more appropriate display by customizing based on the user's current device usage. Current device usage is identified, for example, based on the type of device and the method of monitoring usage. Current device usage is identified, for example, in a specific way and customized according to specific criteria. For example, the user interface monitors the user's current device usage and provides the optimal display method. Some or all of the above processing in the user interface may be performed using AI, for example, or without AI. For example, the user interface inputs the user's device information into a generating AI, and the generating AI provides the optimal display method.

[0082] A user interface can estimate the user's emotions and adjust the interface's operation procedures based on those emotions. For example, if the user is tense, the user interface may simplify the operation procedures to make them more intuitive. If the user is having fun, the user interface may add playful elements to the operation procedures to provide enjoyment. If the user is tired, the user interface may minimize the operation procedures to allow for quick operation. This allows for more appropriate operation by adjusting the interface's operation procedures based on the user's emotions. Emotion-based operation procedures are performed based on adjustment criteria such as simplifying the operation procedures or displaying guides. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the user interface may be performed using AI, or not using AI. For example, the user interface inputs user emotion data into a generative AI, and the generative AI adjusts the operation procedures based on the emotion.

[0083] The user interface can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the user interface provides a display method optimized for the screen size. For example, if the user is using a tablet, the user interface provides a display method optimized for a larger screen. For example, if the user is using a desktop, the user interface provides a display method optimized for mouse operation. This allows for a more appropriate display by selecting the optimal display method considering the user's device information. Device information is acquired, for example, based on device specifications or usage monitoring methods. Device information is acquired, for example, by a specific method, and the optimal display method is selected according to specific criteria. For example, the user interface monitors the user's device information and provides the optimal display method. Some or all of the above processing in the user interface may be performed using AI, for example, or without AI. For example, the user interface inputs the user's device information into a generating AI, and the generating AI provides the optimal display method.

[0084] The user interface can analyze the user's social media activity and display relevant information when the interface is displayed. For example, the user interface can display the latest works of artists the user follows on social media. For example, the user interface can display information related to artwork the user has shared on social media. For example, the user interface can display information related to artwork the user has "liked" on social media. This allows for more appropriate display by analyzing the user's social media activity and displaying relevant information. Social media activity is analyzed, for example, based on methods for analyzing post content and criteria for evaluating relevance. Social media activity is analyzed, for example, in a specific way, and relevant information is displayed according to specific criteria. For example, the user interface analyzes the user's social media activity and displays relevant information. Some or all of the above processing in the user interface may be performed using AI, for example, or without AI. For example, the user interface inputs the user's social media activity into a generating AI, and the generating AI displays relevant information.

[0085] The educational content integration unit can estimate the user's emotions and adjust how the educational content is displayed based on those emotions. For example, if the user is relaxed, the educational content integration unit will display the educational content in calming colors. If the user is excited, the educational content integration unit will display the educational content in vibrant colors. If the user is sad, the educational content integration unit will display the educational content in subdued colors. By adjusting how the educational content is displayed based on the user's emotions, a more appropriate display becomes possible. The emotion-based display of educational content is performed based on adjustment criteria such as changing the content or changing the display order. Emotion estimation is achieved using, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the educational content integration unit may be performed using AI, or not using AI. For example, the educational content integration unit inputs the user's emotion data into the generative AI, and the generative AI adjusts how the educational content is displayed based on the emotion.

[0086] The educational content integration unit can select the most suitable content by referring to the user's past learning history when displaying educational content. For example, the educational content integration unit prioritizes displaying content related to what the user has previously learned. For example, the educational content integration unit suggests what the user should learn next based on their past learning history. For example, the educational content integration unit displays content related to topics the user has previously shown interest in. This enables more appropriate education by selecting the most suitable content by referring to the user's past learning history. Past learning history is referenced, for example, based on the method of saving and analyzing learning logs. Past learning history is saved in a specific way and analyzed in a specific way. For example, the educational content integration unit retrieves the user's past learning history from a database and selects the most suitable content. Some or all of the above processing in the educational content integration unit may be performed using AI, for example, or without AI. For example, the educational content integration unit inputs the user's past learning history into a generating AI, and the generating AI selects the most suitable content.

[0087] The educational content integration unit can customize educational content based on the user's current learning status when displaying it. For example, the educational content integration unit displays content related to what the user is currently learning. For example, the educational content integration unit suggests what the user should learn next according to their learning progress. For example, the educational content integration unit suggests the optimal learning method based on the user's current learning status. This makes it possible to provide more appropriate education by customizing based on the user's current learning status. The current learning status is identified, for example, based on a method for monitoring learning progress or a criterion for customization. The current learning status is identified, for example, in a specific way and customized according to specific criteria. For example, the educational content integration unit monitors the user's current learning status and provides the optimal content. Some or all of the above processes in the educational content integration unit may be performed using AI, for example, or without AI. For example, the educational content integration unit inputs the user's learning status into a generating AI, and the generating AI provides the optimal content.

[0088] The educational content integration unit can estimate the user's emotions and determine the priority of educational content based on the estimated emotions. For example, if the user is relaxed, the educational content integration unit will prioritize displaying calming educational content. For example, if the user is excited, the educational content integration unit will prioritize displaying stimulating educational content. For example, if the user is sad, the educational content integration unit will prioritize displaying calming educational content. This allows for more appropriate education by prioritizing educational content based on the user's emotions. The priority of emotional educational content is determined, for example, by an algorithm based on the intensity and type of emotion. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the educational content integration unit may be performed using AI, for example, or without AI. For example, the educational content integration unit inputs the user's emotion data into a generative AI, and the generative AI determines the priority of educational content based on the emotion.

[0089] The educational content integration unit can prioritize displaying highly relevant content when displaying educational content, taking into account the user's geographical location information. For example, if the user is in Europe, the educational content integration unit will prioritize displaying educational content related to European art. For example, if the user is in Asia, the educational content integration unit will prioritize displaying educational content related to Asian art. For example, if the user is in a specific city, the educational content integration unit will prioritize displaying educational content related to art in that city. This enables more appropriate education by prioritizing the display of highly relevant content, taking into account the user's geographical location information. Geographical location information is obtained, for example, based on the usage of GPS data and relevance evaluation criteria. Geographical location information is obtained, for example, in a specific way and its relevance is evaluated according to specific criteria. For example, the educational content integration unit obtains the user's geographical location information from GPS data and prioritizes the display of highly relevant content. Some or all of the above processing in the educational content integration unit may be performed using AI, for example, or without AI. For example, the educational content integration unit inputs the user's geographical location information into a generating AI, and the generating AI prioritizes the display of highly relevant content.

[0090] The educational content integration unit can analyze a user's social media activity and display relevant content when displaying educational content. For example, the educational content integration unit can display educational content related to artists the user follows on social media. For example, the educational content integration unit can display educational content related to artwork the user has shared on social media. For example, the educational content integration unit can display educational content related to artwork the user has "liked" on social media. This enables more appropriate education by analyzing the user's social media activity and displaying relevant content. Social media activity is analyzed, for example, based on methods for analyzing posted content and criteria for evaluating relevance. Social media activity is analyzed, for example, in a specific way, and relevant content is displayed according to specific criteria. For example, the educational content integration unit analyzes the user's social media activity and displays relevant content. Some or all of the above processing in the educational content integration unit may be performed using AI, for example, or without AI. For example, the educational content integration unit inputs the user's social media activity into a generating AI, and the generating AI displays relevant content.

[0091] The operability provider can estimate the user's emotions and adjust the operability delivery method based on the estimated user emotions. For example, if the user is tense, the operability provider can simplify the operation procedure to make it intuitive. For example, if the user is having fun, the operability provider can add playful elements to the operation procedure to provide enjoyment. For example, if the user is tired, the operability provider can minimize the operation procedure to make it quick. In this way, by adjusting the operability delivery method based on the user's emotions, more appropriate operation becomes possible. The emotion-based operability delivery method is performed based on adjustment criteria such as simplifying the operation procedure or displaying guides. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the operability provider may be performed using, for example, AI, or not using AI. For example, the operability provision unit inputs user emotion data into a generating AI, which then adjusts the method of providing operability based on the emotion.

[0092] The operability provider can select the optimal operation method by referring to the user's past operation history when providing operability. For example, the operability provider may prioritize providing operation methods that the user has preferred to use in the past. For example, the operability provider may suggest the most frequently used operation method from the user's past operation history. For example, the operability provider may automatically apply settings that the user has customized in the past. This enables more appropriate operation by selecting the optimal operation method by referring to the user's past operation history. Past operation history is referred to, for example, based on the method of saving and analyzing operation logs. Past operation history is saved in a specific way and analyzed in a specific way. For example, the operability provider retrieves the user's past operation history from a database and selects the optimal operation method. Some or all of the above processing in the operability provider may be performed using AI, for example, or without AI. For example, the operability provider inputs the user's past operation history into a generating AI, and the generating AI selects the optimal operation method.

[0093] The operability provider can customize the operability based on the user's current device usage when providing operability. For example, if the user is using a smartphone, the operability provider provides an operation method optimized for the screen size. For example, if the user is using a tablet, the operability provider provides an operation method optimized for a large screen. For example, if the user is using a desktop, the operability provider provides an operation method optimized for mouse operation. This allows for more appropriate operation by customizing based on the user's current device usage. The current device usage is identified, for example, based on the type of device and the method of monitoring usage. The current device usage is identified, for example, in a specific way and customized according to specific criteria. For example, the operability provider monitors the user's current device usage and provides the optimal operation method. Some or all of the above processing in the operability provider may be performed using AI, for example, or without AI. For example, the operability provider inputs the user's device information into a generating AI, and the generating AI provides the optimal operation method.

[0094] The operability provider can estimate the user's emotions and determine the priority of operability based on the estimated user emotions. For example, if the user is relaxed, the operability provider will prioritize providing gentle operability methods. For example, if the user is excited, the operability provider will prioritize providing stimulating operability methods. For example, if the user is sad, the operability provider will prioritize providing calm operability methods. This allows for more appropriate operation by determining the priority of operability based on the user's emotions. The priority of operability based on emotions is determined, for example, by an algorithm based on the intensity and type of emotion. Emotion estimation is achieved, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the operability provider may be performed using AI, for example, or not using AI. For example, the operability provider inputs the user's emotion data into the generative AI, and the generative AI determines the priority of operability based on the emotion.

[0095] The operability provider can select the optimal operating method when providing operability, taking into account the user's geographical location information. For example, if the user is in Europe, the operability provider will provide an operating method optimized for European users. For example, if the user is in Asia, the operability provider will provide an operating method optimized for Asian users. For example, if the user is in a specific city, the operability provider will provide an operating method related to that city. This allows for more appropriate operation by selecting the optimal operating method considering the user's geographical location information. Geographical location information is obtained, for example, based on the usage method of GPS data and relevance evaluation criteria. Geographical location information is obtained, for example, in a specific way and its relevance is evaluated according to specific criteria. For example, the operability provider obtains the user's geographical location information from GPS data and provides the optimal operating method. Some or all of the above processing in the operability provider may be performed using AI, for example, or without AI. For example, the operability provider inputs the user's geographical location information into a generating AI, and the generating AI provides the optimal operating method.

[0096] The operability provider can analyze the user's social media activity and suggest relevant operating methods when providing operability. For example, the operability provider may prioritize providing operating methods for artists the user follows on social media. For example, the operability provider may suggest operating methods related to artwork the user has shared on social media. For example, the operability provider may provide operating methods related to artwork the user has "liked" on social media. This allows for more appropriate operation by analyzing the user's social media activity and suggesting relevant operating methods. Social media activity is analyzed, for example, based on methods for analyzing post content and criteria for evaluating relevance. Social media activity is analyzed, for example, in a specific way, and relevant operating methods are suggested based on specific criteria. For example, the operability provider analyzes the user's social media activity and suggests relevant operating methods. Some or all of the above processing in the operability provider may be performed using AI, for example, or without AI. For example, the operability provider inputs the user's social media activity into a generating AI, and the generating AI suggests relevant operating methods.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The style transformation AI module can estimate the user's emotions and adjust the degree to which style transformation is applied based on those emotions. For example, if the user is relaxed, the generating AI can apply calm tones and a soft touch style. If the user is excited, the generating AI can apply vibrant colors and a bold brushstroke style. If the user is sad, the generating AI can apply calm colors and a delicate touch style. This allows for more appropriate style transformation by adjusting the degree of style transformation application based on the user's emotions. The user's emotions are estimated using methods such as facial recognition, voice analysis, and text analysis. Emotion estimation is achieved using an emotion engine or generating AI.

[0099] The style conversion AI module can select the optimal conversion method by referring to the user's past work history. For example, it can prioritize applying styles that the user has previously preferred. It can analyze the colors and composition of the user's past works and suggest a suitable style. It can also suggest styles that the user has not tried before, providing an opportunity to try new techniques. By referring to the user's past work history to select the optimal conversion method, more appropriate style conversion becomes possible. The past work history is referenced based on the format and method of referencing the saved data.

[0100] A user interface can estimate the user's emotions and adjust how it is displayed based on those emotions. For example, if the user is stressed, a calming color scheme can be used for the interface. If the user is enjoying themselves, a bright color scheme can be used. If the user is tired, a simple and highly visible interface can be provided. By adjusting how the interface is displayed based on the user's emotions, a more appropriate display becomes possible. The emotion-based display of the interface is done based on adjustment criteria such as changes in color and layout.

[0101] The educational content integration unit can estimate the user's emotions and adjust how educational content is displayed based on those emotions. For example, if the user is relaxed, the educational content can be displayed in calming colors. If the user is excited, the educational content can be displayed in vibrant colors. If the user is sad, the educational content can be displayed in subdued colors. By adjusting how educational content is displayed based on the user's emotions, a more appropriate display becomes possible. The display method of educational content based on emotions is carried out based on adjustment criteria such as changes in content and changes in display order.

[0102] The user experience provision unit can estimate the user's emotions and adjust the method of providing user experience based on those emotions. For example, if the user is tense, the operation procedure can be simplified to make it more intuitive. If the user is having fun, playful elements can be added to the operation procedure to provide enjoyment. If the user is tired, the operation procedure can be minimized to allow for quick operation. In this way, by adjusting the method of providing user experience based on the user's emotions, more appropriate operation becomes possible. The method of providing user experience based on emotions is carried out based on adjustment criteria such as simplifying the operation procedure and displaying guides.

[0103] The style conversion AI module can filter based on the user's current projects and areas of interest. For example, it can suggest styles that match the theme of the project the user is currently working on. It can prioritize the application of techniques by artists related to the user's areas of interest. It can also suggest relevant styles based on art styles and techniques the user has recently searched for. This allows for more appropriate style conversion by filtering based on the user's current projects and areas of interest. The current projects and areas of interest are identified based on data from project management tools and user input.

[0104] The style conversion AI module can prioritize applying highly relevant styles by considering the user's geographical location. For example, if the user is in Europe, it can prioritize the styles of European artists. If the user is in Asia, it can prioritize the styles of Asian artists. If the user is in a specific city, it can also prioritize the styles of artists associated with that city. This allows for more appropriate style conversion by prioritizing highly relevant styles while considering the user's geographical location. Geographical location information is obtained based on the usage of GPS data and relevance evaluation criteria.

[0105] The user interface can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, it can prioritize displaying interface designs that the user has previously preferred. Based on the user's past operation history, the most frequently used functions can be placed in a prominent position. It can also automatically apply settings that the user has customized in the past. As a result, a more appropriate display is possible by selecting the optimal display method by referring to the user's past operation history. Past operation history is referenced based on the method of saving and analyzing operation logs.

[0106] The educational content integration unit can select the most suitable content when displaying educational content by referring to the user's past learning history. For example, it can prioritize displaying content related to what the user has previously learned. It can also suggest what the user should learn next based on their past learning history. It can also display content related to topics the user has shown interest in in the past. This enables more appropriate education by selecting the most suitable content by referring to the user's past learning history. Past learning history is referenced based on the learning log storage and analysis methods.

[0107] The user interface provision unit can analyze the user's social media activity and suggest relevant operation methods when providing user interfaces. For example, it can prioritize providing operation methods for artists the user follows on social media. It can also suggest operation methods related to artwork the user has shared on social media. It can even provide operation methods related to artwork the user has "liked" on social media. By analyzing the user's social media activity and suggesting relevant operation methods, more appropriate operation becomes possible. Social media activity is analyzed based on methods for analyzing posted content and criteria for evaluating relevance.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The Style Transfer AI module uses pre-trained AI to reproduce the techniques of multiple painters and transform the user's illustration. For example, when a user uploads their own illustration, the module analyzes it and applies the techniques of a selected painter to transform it. The Style Transfer AI module can reproduce techniques such as Dali's Surrealism and Van Gogh's Post-Impressionism. Step 2: The user interface integrates with art apps on smartphones and tablets, making the conversion process easy. For example, users can easily upload illustrations drawn in an art app, run the AI-powered conversion process, and view the results. Step 3: The Educational Content Integration Department provides educational content that introduces the background and significance of each style. For example, it provides explanations of the techniques of Dali's Surrealism and Van Gogh's Post-Impressionism, allowing users to understand the historical background and significance of these techniques. Step 4: The operability section provides an intuitive interface that allows users to easily select styles and convert artworks. For example, the user can simply select a painter's name, and that painter's technique will be applied.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the style conversion AI module, user interface, educational content integration unit, and operability provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the style conversion AI module is implemented by the processor 46 of the smart device 14, which analyzes illustrations uploaded by the user and converts them by applying the techniques of a selected artist. The user interface is implemented by the control unit 46A of the smart device 14, which allows for easy conversion in conjunction with an art application. The educational content integration unit is implemented by the specific processing unit 290 of the data processing device 12, which provides educational content introducing the background and significance of each style. The operability provision unit is implemented by the control unit 46A of the smart device 14, which provides an intuitive interface that allows the user to easily select a style and convert their artwork. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the style conversion AI module, user interface, educational content integration unit, and operability provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the style conversion AI module is implemented by the processor 46 of the smart glasses 214, which analyzes illustrations uploaded by the user and converts them by applying the techniques of a selected artist. The user interface is implemented by the control unit 46A of the smart glasses 214, which allows for easy conversion in conjunction with an art app. The educational content integration unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides educational content introducing the background and significance of each style. The operability provision unit is implemented by the control unit 46A of the smart glasses 214, which provides an intuitive interface that allows the user to easily select a style and convert their artwork. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the style conversion AI module, user interface, educational content integration unit, and operability provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the style conversion AI module is implemented by the processor 46 of the headset terminal 314, which analyzes illustrations uploaded by the user and converts them by applying the techniques of a selected artist. The user interface is implemented by the control unit 46A of the headset terminal 314, which allows for easy conversion in conjunction with an art application. The educational content integration unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides educational content introducing the background and significance of each style. The operability provision unit is implemented by the control unit 46A of the headset terminal 314, which provides an intuitive interface that allows the user to easily select a style and convert their artwork. The correspondence between each part and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the style conversion AI module, user interface, educational content integration unit, and operability provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the style conversion AI module is implemented by the processor 46 of the robot 414, which analyzes illustrations uploaded by the user and converts them by applying the techniques of a selected artist. The user interface is implemented by the control unit 46A of the robot 414, which facilitates the conversion process in conjunction with an art application. The educational content integration unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides educational content introducing the background and significance of each style. The operability provision unit is implemented by the control unit 46A of the robot 414, which provides an intuitive interface that allows the user to easily select a style and convert their artwork. The correspondence between each part and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) Style conversion AI module and A user interface for displaying the conversion results generated by the aforementioned style conversion AI module, An educational content integration unit that provides educational content in conjunction with the aforementioned user interface, The system includes an operability providing unit that provides intuitive operability through the user interface. A system characterized by the following features. (Note 2) The aforementioned style conversion AI module is Using a pre-trained AI, the techniques of multiple painters are reproduced and transformed into the user's own illustrations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned user interface is It integrates with art apps on smartphones and tablets, making the conversion process easy. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned Educational Content Integration Unit, We provide educational content that introduces the background and significance of each style. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned operability-providing unit is, It provides an intuitive interface that allows users to easily select a style and convert their work. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned style conversion AI module is It estimates the user's emotions and adjusts the degree to which style transformations are applied based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned style conversion AI module is During style conversion, the system references the user's past work history to select the optimal conversion method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned style conversion AI module is During style conversion, filtering is performed based on the user's current project and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned style conversion AI module is It estimates the user's emotions and determines the priority of the transformation style based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned style conversion AI module is When converting styles, the system prioritizes applying styles that are more relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned style conversion AI module is During style conversion, the system analyzes the user's social media activity and applies relevant styles. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned user interface is It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned user interface is When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned user interface is When displaying the interface, it is customized based on the user's current device usage. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned user interface is It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned user interface is When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned user interface is When displaying the interface, the system analyzes the user's social media activity and displays relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned Educational Content Integration Unit, It estimates user sentiment and adjusts how educational content is displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned Educational Content Integration Unit, When displaying educational content, the system selects the most suitable content by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned Educational Content Integration Unit, When displaying educational content, customize it based on the user's current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned Educational Content Integration Unit, It estimates user sentiment and prioritizes educational content based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned Educational Content Integration Unit, When displaying educational content, the system prioritizes showing highly relevant content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned Educational Content Integration Unit, When displaying educational content, the system analyzes the user's social media activity and displays relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned operability-providing unit is, It estimates the user's emotions and adjusts the way the user experience is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned operability-providing unit is, When providing usability, the system selects the optimal operating method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned operability-providing unit is, When providing usability, customize it based on the user's current device usage. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned operability-providing unit is, It estimates the user's emotions and determines the priority of usability based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned operability-providing unit is, When providing usability, the optimal operating method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned operability-providing unit is, When providing usability features, we analyze the user's social media activity and suggest relevant methods of operation. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. Style conversion AI module, A user interface for displaying the conversion results generated by the aforementioned style conversion AI module, An educational content integration unit that provides educational content in conjunction with the aforementioned user interface, The system includes an operability providing unit that provides intuitive operability through the user interface. A system characterized by the following features.

2. The aforementioned style conversion AI module is Using a pre-trained AI, the techniques of multiple painters are reproduced and transformed into the user's own illustrations. The system according to feature 1.

3. The aforementioned user interface is It integrates with art apps on smartphones and tablets, making the conversion process easy. The system according to feature 1.

4. The aforementioned Educational Content Integration Unit, We provide educational content that introduces the background and significance of each style. The system according to feature 1.

5. The aforementioned operability-providing unit is, It provides an intuitive interface that allows users to easily select a style and convert their work. The system according to feature 1.

6. The aforementioned style conversion AI module is It estimates the user's emotions and adjusts the degree to which style transformations are applied based on the estimated user emotions. The system according to feature 1.

7. The aforementioned style conversion AI module is During style conversion, the system references the user's past work history to select the optimal conversion method. The system according to feature 1.

8. The aforementioned style conversion AI module is During style conversion, filtering is performed based on the user's current project and areas of interest. The system according to feature 1.

9. The aforementioned style conversion AI module is It estimates the user's emotions and determines the priority of the transformation style based on the estimated user emotions. The system according to feature 1.

10. The aforementioned style conversion AI module is When converting styles, the system prioritizes applying styles that are more relevant to the user's geographical location. The system according to feature 1.

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