system

Abstract

The system according to this embodiment aims to deepen the understanding of color theory, integrate digital and physical color experiences, and be useful in art production. [Solution] The system according to the embodiment comprises a dual color mode unit, a learning interface unit, a creative color picker unit, and an art simulation tool unit. The dual color mode unit is switchable between RGB and CMY modes and visualizes different color mixtures. The learning interface unit provides educational modules on color theory provided by the dual color mode unit, allowing users to learn through hands-on experience. The creative color picker unit generates unique colors using a new color picker that combines digital and physical colors provided by the learning interface unit. The art simulation tool unit displays a real-time prediction of the finished artwork using an art simulation with the colors generated by the creative color picker unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to understand the difference between the three primary colors of light and color, it is difficult to simultaneously experience digital and physical color mixing, and the methods of applying color theory to art production are limited.

[0005] The system according to the embodiment aims to deepen the understanding of color theory, integrate digital and physical color experiences, and apply them to art production.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a dual color mode unit, a learning interface unit, a creative color picker unit, and an art simulation tool unit. The dual color mode unit is switchable between RGB and CMY modes and visualizes different color mixtures. The learning interface unit provides educational modules on color theory provided by the dual color mode unit, allowing users to learn through hands-on experience. The creative color picker unit generates unique colors using a new color picker that combines digital and physical colors, provided by the learning interface unit. The art simulation tool unit displays a real-time prediction of the finished artwork using an art simulation with the colors generated by the creative color picker unit. [Effects of the Invention]

[0007] The system according to this embodiment can deepen the understanding of color theory, integrate digital and physical color experiences, and be useful in art production. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 color theory understanding support system according to an embodiment of the present invention is a system that supports the understanding of color theory and art production. This color theory understanding support system is switchable between RGB and CMY modes and features a dual color mode that visualizes different color mixing. It provides educational modules on color theory and features a learning interface that allows users to learn through hands-on experience. It features a creative color picker that generates unique colors using a new color picker that combines digital and physical colors. It features an art simulation tool that displays a real-time prediction of the finished artwork using the three primary colors of light and the three primary colors of pigment. This system can deepen understanding of color and broaden the scope of creativity. It also provides a new approach that integrates digital and real-world color experiences and innovates the art production process. For example, the dual color mode is switchable between RGB and CMY modes and visualizes different color mixing. When a user mixes red and green in RGB mode, yellow is produced, and when they mix cyan and magenta in CMY mode, blue is produced. In this way, the mixing of colors in different color modes can be visually understood. The learning interface provides educational modules on color theory and allows users to learn through hands-on experience. For example, by learning about the color wheel and complementary colors, and then actually mixing colors, users can gain a deeper understanding of color theory. The Creative Color Picker uses a new color picker that combines digital and physical colors to generate unique colors. For instance, users can combine a digitally selected color with a paint color they physically possess to create a new color. The Art Simulation Tool uses the three primary colors of light and pigment to display a real-time prediction of the finished artwork. For example, when a user inputs a digitally drawn image into the simulation tool, it can display in real-time what the resulting color scheme will look like using the three primary colors of light and pigment. This deepens understanding of color and broadens creative possibilities. It also integrates digital and real-world color experiences, offering a new approach that innovates the art creation process.This allows the color theory understanding support system to deepen understanding of color and broaden the scope of creativity.

[0029] The color theory understanding support system according to this embodiment comprises a dual color mode unit, a learning interface unit, a creative color picker unit, and an art simulation tool unit. The dual color mode unit is switchable between RGB and CMY modes and visualizes different color mixing. For example, the dual color mode unit generates yellow when red and green are mixed in RGB mode, and generates blue when cyan and magenta are mixed in CMY mode. The dual color mode unit allows for a visual understanding of color mixing in different color modes. The learning interface unit provides educational modules on color theory, allowing users to learn through hands-on experience. For example, the learning interface unit allows for a deeper understanding of color theory by learning concepts such as the color wheel and complementary colors while actually mixing colors. The creative color picker unit generates unique colors using a new color picker that combines digital and physical colors. For example, the creative color picker unit can generate new colors by combining a color selected digitally by the user with a color from paints they physically possess. The art simulation tool unit uses the three primary colors of light and the three primary colors of pigment to simulate the finished artwork and display a real-time prediction of its final form. For example, when a user inputs a digitally drawn image into the simulation tool, the art simulation tool unit can use the three primary colors of light and the three primary colors of pigment to display in real time what the resulting color scheme would look like. As a result, the color theory understanding support system according to this embodiment can deepen understanding of color and broaden the scope of creativity.

[0030] The dual color mode section can be switched between RGB and CMY modes, visualizing different color mixing processes. Specifically, RGB mode uses the three primary colors of light—red, green, and blue—to generate colors, while CMY mode uses the three primary colors of light—cyan, magenta, and yellow. For example, mixing red and green in RGB mode produces yellow, and mixing blue and red produces magenta. In CMY mode, mixing cyan and magenta produces blue, and mixing cyan and yellow produces green. The dual color mode section visualizes these color mixing processes in real time, allowing users to intuitively understand the color generation process in different color modes. Furthermore, the dual color mode section also includes a function to adjust color density and transparency when users experiment with color mixing. This allows users to observe subtle color changes and gradients in detail. In addition, the dual color mode section can save color mixing results for later reuse. This allows users to manage their color generation history and reuse it as needed. The dual-color mode section not only supports the learning of color theory but also serves as a useful tool in creative projects.

[0031] The learning interface provides educational modules on color theory, allowing users to learn through hands-on experience. Specifically, it offers interactive learning materials that include detailed explanations of the color wheel, complementary colors, and the three attributes of color (hue, saturation, and lightness). Through these materials, users can learn the basic concepts of color theory. Furthermore, the learning interface includes features that allow users to practically understand the theory by actually mixing colors. For example, users can select different colors on a virtual palette and see what colors are produced by mixing them. The learning interface also displays the color mixing results in real time, providing an environment where users can learn through trial and error. In addition, the learning interface includes features to evaluate the user's understanding through quizzes and tests. This allows users to check their learning progress and review as needed. The learning interface functions as a tool to make learning color theory fun and effective.

[0032] The Creative Color Picker section generates unique colors using a new color picker that combines digital and physical colors. Specifically, it can generate new colors by combining a color selected digitally with the color of paint or ink the user physically possesses. The Creative Color Picker section has the ability to recognize physical colors using the camera of a digital device and capture those colors as digital data. For example, the user can photograph the color of a paint they possess with the camera and save that color as digital data. Then, the user can use the digital color picker to combine the digitally selected color with the physical color to generate a new color. The Creative Color Picker section displays the RGB and CMY values ​​of the generated color, allowing the user to check detailed color information. It is also possible to save the generated color and reuse it later. This allows users to generate and use their own unique colors in their creative projects. The Creative Color Picker section is a powerful tool for enhancing color creativity.

[0033] The Art Simulation Tool section uses the three primary colors of light and pigment to display a real-time prediction of the finished artwork. Specifically, when a user inputs a digitally drawn image into the simulation tool, it uses the three primary colors of light (red, green, blue) and the three primary colors of pigment (cyan, magenta, yellow) to display in real time what the actual colors will look like. The Art Simulation Tool section has the function to simulate a prediction of the finished artwork based on the colors and drawing style selected by the user. For example, when a user applies a specific color to a digitally drawn image, they can see in real time how that color will look. The Art Simulation Tool section also simulates the interaction of the three primary colors of light and pigment, accurately displaying the results of color mixing. This allows users to check in advance how the actual artwork will look and adjust the colors and design as needed. Furthermore, the Art Simulation Tool section allows users to save simulation results and reuse them later. This allows users to manage the progress of their artwork and work while checking the final prediction. The Art Simulation Tool section is a powerful tool for deepening understanding of color and expanding creative possibilities.

[0034] The dual color mode section can be switched between RGB and CMY modes, allowing for the visualization of different color mixtures. For example, in RGB mode, mixing red and green produces yellow, while in CMY mode, mixing cyan and magenta produces blue. The dual color mode section allows for a visual understanding of color mixing in different color modes. This visualization of different color mixtures deepens the understanding of color theory.

[0035] The learning interface provides educational modules on color theory, allowing users to learn through hands-on experience. For example, the learning interface allows users to deepen their understanding of color theory by learning about concepts such as the color wheel and complementary colors, and then actually mixing colors. This hands-on learning approach enhances understanding of color theory.

[0036] The Creative Color Picker section allows users to generate unique colors using a new color picker that combines digital and physical colors. For example, the Creative Color Picker can combine a color selected digitally with a color from a paint the user physically possesses to create a new color. This allows for the creation of unique colors by combining digital and physical elements.

[0037] The art simulation tool uses the three primary colors of light and pigment to display a real-time prediction of the finished artwork. For example, when a user inputs a digitally drawn image into the simulation tool, it uses the three primary colors of light and pigment to display in real time what the actual colors will look like. This revolutionizes the creative process by displaying a real-time prediction of the finished artwork.

[0038] The dual color mode allows users to generate yellow by mixing red and green in RGB mode, and blue by mixing cyan and magenta in CMY mode. For example, mixing red and green in RGB mode generates yellow, and mixing cyan and magenta in CMY mode generates blue. This allows for a visual understanding of color mixing in different color modes.

[0039] The learning interface allows users to deeply understand color theory by learning about concepts such as the color wheel and complementary colors, and by actually mixing colors. For example, the learning interface allows users to deeply understand color theory by learning about concepts such as the color wheel and complementary colors, and by actually mixing colors. This allows for a deeper understanding of color theory.

[0040] The Creative Color Picker section can generate new colors by combining a color digitally selected by the user with a paint color they physically possess. For example, the Creative Color Picker section can generate new colors by combining a color digitally selected by the user with a paint color they physically possess. This allows for the creation of new colors by combining digital and physical colors.

[0041] The art simulation tool allows users to input a digitally drawn image into the simulation tool and, using the three primary colors of light and pigment, display in real time what the resulting colors would actually look like. For example, when a user inputs a digitally drawn image into the simulation tool, the art simulation tool can display in real time what the resulting colors would actually look like using the three primary colors of light and pigment. This allows for real-time display of what the resulting colors would actually look like.

[0042] The dual color mode section can suggest the optimal color mixing method by referring to the user's past color mixing history when switching between RGB and CMY modes. For example, the dual color mode section can suggest color combinations that the user has previously preferred. For example, the dual color mode section can suggest avoiding color combinations that the user has previously avoided. For example, the dual color mode section can suggest color combinations suitable for a specific project based on the user's past color mixing history. In this way, the optimal color mixing method can be suggested by referring to the user's past color mixing history.

[0043] The dual color mode section can filter color mixing based on the user's current project and area of ​​interest when switching between RGB and CMY modes. For example, the dual color mode section suggests color combinations suitable for the user's current project. For example, the dual color mode section prioritizes displaying relevant color combinations based on the user's area of ​​interest. For example, the dual color mode section suggests appropriate color combinations according to the progress of the user's project. This allows for a more appropriate color mixing method by filtering color mixing based on the user's current project and area of ​​interest.

[0044] The dual-color mode section can prioritize displaying highly relevant color mixtures by considering the user's geographical location when switching between RGB and CMY modes. For example, if the user is in a specific region, the dual-color mode section suggests color combinations based on the culture and scenery of that region. For example, if the user is traveling, the dual-color mode section suggests color combinations that reflect the characteristics of the region they are visiting. For example, if the user is at home, the dual-color mode section suggests color combinations that have been used in that location in the past. In this way, by considering the user's geographical location, the display of highly relevant color mixtures can be prioritized.

[0045] The dual color mode section can analyze the user's social media activity and suggest relevant color combinations when switching between RGB and CMY modes. For example, it can analyze the colors of images the user has shared on social media and suggest relevant color combinations. It can also suggest color combinations based on the colors of works by artists the user follows, or based on topics the user has shown interest in on social media. In this way, by analyzing the user's social media activity, it can suggest relevant color combinations.

[0046] The learning interface unit can suggest the optimal learning method by referring to the user's past learning history when providing educational modules. For example, the learning interface unit can suggest learning methods the user has preferred in the past. For example, the learning interface unit can suggest avoiding learning methods the user has avoided in the past. For example, the learning interface unit can suggest learning methods suitable for a specific topic based on the user's past learning history. In this way, the optimal learning method can be suggested by referring to the user's past learning history.

[0047] The learning interface can customize learning content based on the user's current learning progress when providing educational modules. For example, the learning interface prioritizes providing content related to the topic the user is currently studying. For example, the learning interface suggests the next topic to be learned based on the user's learning progress. For example, the learning interface adjusts the difficulty level of the learning content to match the user's learning pace. This allows for a more effective learning experience by customizing learning content based on the user's current learning progress.

[0048] The learning interface unit can propose the optimal learning method when providing educational modules, taking into account the user's device information. For example, if the user is using a smartphone, the learning interface unit provides a learning method adapted to the screen size. For example, if the user is using a tablet, the learning interface unit provides a learning method optimized for a larger screen. For example, if the user is using a personal computer, the learning interface unit provides a learning method that includes detailed information. In this way, the optimal learning method can be proposed by taking into account the user's device information.

[0049] The learning interface unit can analyze the user's social media activity and suggest relevant learning content when providing educational modules. For example, the learning interface unit can suggest learning content based on content shared by the user on social media. It can also suggest learning content based on information from educators and experts followed by the user. Furthermore, it can suggest learning content based on topics the user has shown interest in on social media. In this way, by analyzing the user's social media activity, it can suggest relevant learning content.

[0050] The Creative Color Picker section can suggest the optimal color selection method by referring to the user's past color selection history when using the color picker. For example, the Creative Color Picker section may prioritize displaying colors that the user has previously preferred to use. For example, the Creative Color Picker section may suggest avoiding colors that the user has previously avoided. For example, the Creative Color Picker section may suggest colors suitable for a specific project based on the user's past color selection history. In this way, the Creative Color Picker section can suggest the optimal color selection method by referring to the user's past color selection history.

[0051] The Creative Color Picker section can filter color selections based on the user's current project or area of ​​interest when using the color picker. For example, the Creative Color Picker section prioritizes displaying colors suitable for the user's current project. For example, the Creative Color Picker section prioritizes displaying relevant colors based on the user's area of ​​interest. For example, the Creative Color Picker section suggests appropriate colors according to the user's project progress. This allows for a more appropriate color selection method by filtering color selections based on the user's current project or area of ​​interest.

[0052] The Creative Color Picker section can prioritize displaying highly relevant color selections by considering the user's geographical location when using the color picker. For example, if the user is in a specific region, the Creative Color Picker section will suggest colors based on the culture and scenery of that region. For example, if the user is traveling, the Creative Color Picker section will suggest colors that reflect the characteristics of the region they are visiting. For example, if the user is at home, the Creative Color Picker section will suggest colors that have been used in that location in the past. In this way, by considering the user's geographical location, the Creative Color Picker section can prioritize displaying highly relevant color selections.

[0053] The Creative Color Picker section can analyze a user's social media activity and suggest relevant color choices when the color picker is used. For example, the Creative Color Picker section can analyze the colors of images the user has shared on social media and suggest relevant colors. For example, the Creative Color Picker section can suggest colors based on the colors in the works of artists the user follows. For example, the Creative Color Picker section can suggest colors based on topics the user has shown interest in on social media. In this way, by analyzing the user's social media activity, it can suggest relevant color choices.

[0054] The art simulation tool can suggest the optimal simulation method by referring to the user's past art history when running a simulation. For example, the art simulation tool can perform a simulation by referring to the color tones of works the user has created in the past. For example, the art simulation tool can perform a simulation while avoiding color tones that the user has avoided in the past. For example, the art simulation tool can suggest a simulation method suitable for a specific project based on the user's past art history. In this way, the optimal simulation method can be suggested by referring to the user's past art history.

[0055] The art simulation tool can filter simulations based on the user's current project and areas of interest during execution. For example, it can provide simulations suitable for the user's current project. It can also prioritize displaying relevant simulations based on the user's areas of interest. Furthermore, it can suggest appropriate simulations based on the user's project progress. By filtering simulations based on the user's current project and areas of interest, it can provide a more appropriate simulation method.

[0056] The art simulation tool can prioritize displaying simulations that are highly relevant to the user's geographical location when running simulations. For example, if the user is in a specific region, the art simulation tool will provide a simulation based on the culture and scenery of that region. For example, if the user is traveling, the art simulation tool will provide a simulation that reflects the characteristics of the region they are visiting. For example, if the user is at home, the art simulation tool will perform a simulation referencing the color tones of artwork previously created in that location. In this way, by considering the user's geographical location, the system can prioritize displaying simulations that are highly relevant to the user.

[0057] The art simulation tool can analyze the user's social media activity during simulation execution and suggest relevant simulations. For example, it can analyze the color palette of artwork shared by the user on social media and provide relevant simulations. It can also perform simulations based on the color palette of artwork by artists the user follows. Furthermore, it can suggest simulations based on topics the user has shown interest in on social media. In this way, by analyzing the user's social media activity, it can suggest relevant simulations.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The color theory understanding support system can suggest the optimal learning method by referring to the user's past learning history. For example, it can suggest learning methods that the user has preferred in the past. It can also suggest learning methods that the user has avoided in the past. Based on the user's past learning history, it can suggest learning methods suitable for specific topics. In this way, by referring to the user's past learning history, it can suggest the optimal learning method.

[0060] The color theory understanding support system can customize learning content based on the user's current learning progress. For example, it can prioritize providing content related to the topic the user is currently studying. It can suggest the next topic to learn based on the user's learning progress. It can adjust the difficulty level of the learning content to match the user's learning pace. By customizing learning content based on the user's current learning progress, it can provide a more effective learning experience.

[0061] The color theory understanding support system can suggest the optimal learning method by considering the user's device information. For example, if the user is using a smartphone, it can provide a learning method adapted to the screen size. If the user is using a tablet, it can provide a learning method optimized for a larger screen. If the user is using a personal computer, it can provide a learning method that includes detailed information. In this way, by considering the user's device information, it can suggest the most suitable learning method.

[0062] The color theory understanding support system can analyze a user's social media activity and suggest relevant learning content. For example, it can suggest learning content based on content the user has shared on social media. It can also suggest learning content based on information from educators and experts the user follows. It can suggest learning content based on topics the user has shown interest in on social media. In this way, by analyzing the user's social media activity, it can suggest relevant learning content.

[0063] The color theory understanding support system can suggest the optimal color selection method by referring to the user's past color selection history when using a color picker. For example, it can prioritize displaying colors that the user has previously preferred to use. It can also suggest avoiding colors that the user has previously avoided. Based on the user's past color selection history, it can suggest colors suitable for a specific project. In this way, by referring to the user's past color selection history, it can suggest the optimal color selection method.

[0064] The color theory understanding support system can filter color selections based on the user's current project and areas of interest when using a color picker. For example, it can prioritize displaying colors suitable for the user's current project. It can prioritize displaying relevant colors based on the user's areas of interest. It can suggest appropriate colors according to the progress of the user's project. In this way, by filtering color selections based on the user's current project and areas of interest, it can provide a more appropriate method of color selection.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The dual color mode section can be switched between RGB and CMY modes to visualize different color mixing. For example, mixing red and green in RGB mode produces yellow, and mixing cyan and magenta in CMY mode produces blue. This allows for a visual understanding of color mixing in different color modes. Step 2: The learning interface section provides educational modules on color theory, allowing users to learn through hands-on experience. For example, by learning about the color wheel and complementary colors, and then actually mixing colors, users can gain a deeper understanding of color theory. Step 3: The Creative Color Picker section generates unique colors using a new color picker that combines digital and physical colors. For example, it can generate new colors by combining a color selected digitally with a paint color the user physically possesses. Step 4: The art simulation tool section displays a real-time prediction of the finished artwork using the three primary colors of light and pigment. For example, when a user inputs a digitally drawn picture into the simulation tool, it can display in real time what the actual colors will look like using the three primary colors of light and pigment.

[0067] (Example of form 2) The color theory understanding support system according to an embodiment of the present invention is a system that supports the understanding of color theory and art production. This color theory understanding support system is switchable between RGB and CMY modes and features a dual color mode that visualizes different color mixing. It provides educational modules on color theory and features a learning interface that allows users to learn through hands-on experience. It features a creative color picker that generates unique colors using a new color picker that combines digital and physical colors. It features an art simulation tool that displays a real-time prediction of the finished artwork using the three primary colors of light and the three primary colors of pigment. This system can deepen understanding of color and broaden the scope of creativity. It also provides a new approach that integrates digital and real-world color experiences and innovates the art production process. For example, the dual color mode is switchable between RGB and CMY modes and visualizes different color mixing. When a user mixes red and green in RGB mode, yellow is produced, and when they mix cyan and magenta in CMY mode, blue is produced. In this way, the mixing of colors in different color modes can be visually understood. The learning interface provides educational modules on color theory and allows users to learn through hands-on experience. For example, by learning about the color wheel and complementary colors, and then actually mixing colors, users can gain a deeper understanding of color theory. The Creative Color Picker uses a new color picker that combines digital and physical colors to generate unique colors. For instance, users can combine a digitally selected color with a paint color they physically possess to create a new color. The Art Simulation Tool uses the three primary colors of light and pigment to display a real-time prediction of the finished artwork. For example, when a user inputs a digitally drawn image into the simulation tool, it can display in real-time what the resulting color scheme will look like using the three primary colors of light and pigment. This deepens understanding of color and broadens creative possibilities. It also integrates digital and real-world color experiences, offering a new approach that innovates the art creation process.This allows the color theory understanding support system to deepen understanding of color and broaden the scope of creativity.

[0068] The color theory understanding support system according to this embodiment comprises a dual color mode unit, a learning interface unit, a creative color picker unit, and an art simulation tool unit. The dual color mode unit is switchable between RGB and CMY modes and visualizes different color mixing. For example, the dual color mode unit generates yellow when red and green are mixed in RGB mode, and generates blue when cyan and magenta are mixed in CMY mode. The dual color mode unit allows for a visual understanding of color mixing in different color modes. The learning interface unit provides educational modules on color theory, allowing users to learn through hands-on experience. For example, the learning interface unit allows for a deeper understanding of color theory by learning concepts such as the color wheel and complementary colors while actually mixing colors. The creative color picker unit generates unique colors using a new color picker that combines digital and physical colors. For example, the creative color picker unit can generate new colors by combining a color selected digitally by the user with a color from paints they physically possess. The art simulation tool unit uses the three primary colors of light and the three primary colors of pigment to simulate the finished artwork and display a real-time prediction of its final form. For example, when a user inputs a digitally drawn image into the simulation tool, the art simulation tool unit can use the three primary colors of light and the three primary colors of pigment to display in real time what the resulting color scheme would look like. As a result, the color theory understanding support system according to this embodiment can deepen understanding of color and broaden the scope of creativity.

[0069] The dual color mode section can be switched between RGB and CMY modes, visualizing different color mixing processes. Specifically, RGB mode uses the three primary colors of light—red, green, and blue—to generate colors, while CMY mode uses the three primary colors of light—cyan, magenta, and yellow. For example, mixing red and green in RGB mode produces yellow, and mixing blue and red produces magenta. In CMY mode, mixing cyan and magenta produces blue, and mixing cyan and yellow produces green. The dual color mode section visualizes these color mixing processes in real time, allowing users to intuitively understand the color generation process in different color modes. Furthermore, the dual color mode section also includes a function to adjust color density and transparency when users experiment with color mixing. This allows users to observe subtle color changes and gradients in detail. In addition, the dual color mode section can save color mixing results for later reuse. This allows users to manage their color generation history and reuse it as needed. The dual-color mode section not only supports the learning of color theory but also serves as a useful tool in creative projects.

[0070] The learning interface provides educational modules on color theory, allowing users to learn through hands-on experience. Specifically, it offers interactive learning materials that include detailed explanations of the color wheel, complementary colors, and the three attributes of color (hue, saturation, and lightness). Through these materials, users can learn the basic concepts of color theory. Furthermore, the learning interface includes features that allow users to practically understand the theory by actually mixing colors. For example, users can select different colors on a virtual palette and see what colors are produced by mixing them. The learning interface also displays the color mixing results in real time, providing an environment where users can learn through trial and error. In addition, the learning interface includes features to evaluate the user's understanding through quizzes and tests. This allows users to check their learning progress and review as needed. The learning interface functions as a tool to make learning color theory fun and effective.

[0071] The Creative Color Picker section generates unique colors using a new color picker that combines digital and physical colors. Specifically, it can generate new colors by combining a color selected digitally with the color of paint or ink the user physically possesses. The Creative Color Picker section has the ability to recognize physical colors using the camera of a digital device and capture those colors as digital data. For example, the user can photograph the color of a paint they possess with the camera and save that color as digital data. Then, the user can use the digital color picker to combine the digitally selected color with the physical color to generate a new color. The Creative Color Picker section displays the RGB and CMY values ​​of the generated color, allowing the user to check detailed color information. It is also possible to save the generated color and reuse it later. This allows users to generate and use their own unique colors in their creative projects. The Creative Color Picker section is a powerful tool for enhancing color creativity.

[0072] The Art Simulation Tool section uses the three primary colors of light and pigment to display a real-time prediction of the finished artwork. Specifically, when a user inputs a digitally drawn image into the simulation tool, it uses the three primary colors of light (red, green, blue) and the three primary colors of pigment (cyan, magenta, yellow) to display in real time what the actual colors will look like. The Art Simulation Tool section has the function to simulate a prediction of the finished artwork based on the colors and drawing style selected by the user. For example, when a user applies a specific color to a digitally drawn image, they can see in real time how that color will look. The Art Simulation Tool section also simulates the interaction of the three primary colors of light and pigment, accurately displaying the results of color mixing. This allows users to check in advance how the actual artwork will look and adjust the colors and design as needed. Furthermore, the Art Simulation Tool section allows users to save simulation results and reuse them later. This allows users to manage the progress of their artwork and work while checking the final prediction. The Art Simulation Tool section is a powerful tool for deepening understanding of color and expanding creative possibilities.

[0073] The dual color mode section can be switched between RGB and CMY modes, allowing for the visualization of different color mixtures. For example, in RGB mode, mixing red and green produces yellow, while in CMY mode, mixing cyan and magenta produces blue. The dual color mode section allows for a visual understanding of color mixing in different color modes. This visualization of different color mixtures deepens the understanding of color theory.

[0074] The learning interface provides educational modules on color theory, allowing users to learn through hands-on experience. For example, the learning interface allows users to deepen their understanding of color theory by learning about concepts such as the color wheel and complementary colors, and then actually mixing colors. This hands-on learning approach enhances understanding of color theory.

[0075] The Creative Color Picker section allows users to generate unique colors using a new color picker that combines digital and physical colors. For example, the Creative Color Picker can combine a color selected digitally with a color from a paint the user physically possesses to create a new color. This allows for the creation of unique colors by combining digital and physical elements.

[0076] The art simulation tool uses the three primary colors of light and pigment to display a real-time prediction of the finished artwork. For example, when a user inputs a digitally drawn image into the simulation tool, it uses the three primary colors of light and pigment to display in real time what the actual colors will look like. This revolutionizes the creative process by displaying a real-time prediction of the finished artwork.

[0077] The dual color mode allows users to generate yellow by mixing red and green in RGB mode, and blue by mixing cyan and magenta in CMY mode. For example, mixing red and green in RGB mode generates yellow, and mixing cyan and magenta in CMY mode generates blue. This allows for a visual understanding of color mixing in different color modes.

[0078] The learning interface allows users to deeply understand color theory by learning about concepts such as the color wheel and complementary colors, and by actually mixing colors. For example, the learning interface allows users to deeply understand color theory by learning about concepts such as the color wheel and complementary colors, and by actually mixing colors. This allows for a deeper understanding of color theory.

[0079] The Creative Color Picker section can generate new colors by combining a color digitally selected by the user with a paint color they physically possess. For example, the Creative Color Picker section can generate new colors by combining a color digitally selected by the user with a paint color they physically possess. This allows for the creation of new colors by combining digital and physical colors.

[0080] The art simulation tool allows users to input a digitally drawn image into the simulation tool and, using the three primary colors of light and pigment, display in real time what the resulting colors would actually look like. For example, when a user inputs a digitally drawn image into the simulation tool, the art simulation tool can display in real time what the resulting colors would actually look like using the three primary colors of light and pigment. This allows for real-time display of what the resulting colors would actually look like.

[0081] The dual color mode unit can estimate the user's emotions and adjust the timing of switching between RGB and CMY modes based on the estimated emotions. For example, if the user is relaxed, the dual color mode unit will switch modes at a time suitable for long work sessions. If the user is focused, the dual color mode unit will minimize mode switching to avoid disrupting the workflow. If the user is tired, the dual color mode unit will switch modes at an appropriate time to encourage a break. By adjusting the timing of mode switching according to the user's emotions, a more appropriate color mixing experience can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The dual color mode section can suggest the optimal color mixing method by referring to the user's past color mixing history when switching between RGB and CMY modes. For example, the dual color mode section can suggest color combinations that the user has previously preferred. For example, the dual color mode section can suggest avoiding color combinations that the user has previously avoided. For example, the dual color mode section can suggest color combinations suitable for a specific project based on the user's past color mixing history. In this way, the optimal color mixing method can be suggested by referring to the user's past color mixing history.

[0083] The dual color mode section can filter color mixing based on the user's current project and area of ​​interest when switching between RGB and CMY modes. For example, the dual color mode section suggests color combinations suitable for the user's current project. For example, the dual color mode section prioritizes displaying relevant color combinations based on the user's area of ​​interest. For example, the dual color mode section suggests appropriate color combinations according to the progress of the user's project. This allows for a more appropriate color mixing method by filtering color mixing based on the user's current project and area of ​​interest.

[0084] The dual color mode unit can estimate the user's emotions and adjust the frequency of switching between RGB and CMY modes based on the estimated emotions. For example, if the user is relaxed, the dual color mode unit will frequently switch modes to try new color combinations. If the user is focused, for example, the dual color mode unit will minimize mode switching to maintain the workflow. If the user is tired, for example, the dual color mode unit will switch modes at an appropriate frequency to encourage breaks. This allows for a more appropriate color mixing experience by adjusting the frequency of mode switching according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The dual-color mode section can prioritize displaying highly relevant color mixtures by considering the user's geographical location when switching between RGB and CMY modes. For example, if the user is in a specific region, the dual-color mode section suggests color combinations based on the culture and scenery of that region. For example, if the user is traveling, the dual-color mode section suggests color combinations that reflect the characteristics of the region they are visiting. For example, if the user is at home, the dual-color mode section suggests color combinations that have been used in that location in the past. In this way, by considering the user's geographical location, the display of highly relevant color mixtures can be prioritized.

[0086] The dual color mode section can analyze the user's social media activity and suggest relevant color combinations when switching between RGB and CMY modes. For example, it can analyze the colors of images the user has shared on social media and suggest relevant color combinations. It can also suggest color combinations based on the colors of works by artists the user follows, or based on topics the user has shown interest in on social media. In this way, by analyzing the user's social media activity, it can suggest relevant color combinations.

[0087] The learning interface can estimate the user's emotions and adjust the content of the educational modules based on the estimated emotions. For example, if the user is relaxed, the learning interface provides an educational module with detailed explanations. If the user is in a hurry, the learning interface provides a short, concise educational module. If the user is excited, the learning interface provides an educational module with visually stimulating content. By adjusting the content of the educational modules according to the user's emotions, a more effective learning experience can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The learning interface unit can suggest the optimal learning method by referring to the user's past learning history when providing educational modules. For example, the learning interface unit can suggest learning methods the user has preferred in the past. For example, the learning interface unit can suggest avoiding learning methods the user has avoided in the past. For example, the learning interface unit can suggest learning methods suitable for a specific topic based on the user's past learning history. In this way, the optimal learning method can be suggested by referring to the user's past learning history.

[0089] The learning interface can customize learning content based on the user's current learning progress when providing educational modules. For example, the learning interface prioritizes providing content related to the topic the user is currently studying. For example, the learning interface suggests the next topic to be learned based on the user's learning progress. For example, the learning interface adjusts the difficulty level of the learning content to match the user's learning pace. This allows for a more effective learning experience by customizing learning content based on the user's current learning progress.

[0090] The learning interface can estimate the user's emotions and adjust the frequency of educational module delivery based on the estimated emotions. For example, if the user is relaxed, the learning interface will frequently deliver educational modules to facilitate learning. For example, if the user is focused, the learning interface will optimize the frequency of educational module delivery to maintain the learning flow. For example, if the user is tired, the learning interface will adjust the frequency of educational module delivery to encourage breaks. In this way, a more effective learning experience can be provided by adjusting the frequency of educational module delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The learning interface unit can propose the optimal learning method when providing educational modules, taking into account the user's device information. For example, if the user is using a smartphone, the learning interface unit provides a learning method adapted to the screen size. For example, if the user is using a tablet, the learning interface unit provides a learning method optimized for a larger screen. For example, if the user is using a personal computer, the learning interface unit provides a learning method that includes detailed information. In this way, the optimal learning method can be proposed by taking into account the user's device information.

[0092] The learning interface unit can analyze the user's social media activity and suggest relevant learning content when providing educational modules. For example, the learning interface unit can suggest learning content based on content shared by the user on social media. It can also suggest learning content based on information from educators and experts followed by the user. Furthermore, it can suggest learning content based on topics the user has shown interest in on social media. In this way, by analyzing the user's social media activity, it can suggest relevant learning content.

[0093] The creative color picker section can estimate the user's emotions and adjust the display method of the color picker based on the estimated emotions. For example, if the user is relaxed, the creative color picker section provides a soft color interface. For example, if the user is focused, the creative color picker section provides a simple and highly visible interface. For example, if the user is excited, the creative color picker section provides a visually stimulating interface. By adjusting the display method of the color picker according to the user's emotions, a more appropriate color selection experience can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The Creative Color Picker section can suggest the optimal color selection method by referring to the user's past color selection history when using the color picker. For example, the Creative Color Picker section may prioritize displaying colors that the user has previously preferred to use. For example, the Creative Color Picker section may suggest avoiding colors that the user has previously avoided. For example, the Creative Color Picker section may suggest colors suitable for a specific project based on the user's past color selection history. In this way, the Creative Color Picker section can suggest the optimal color selection method by referring to the user's past color selection history.

[0095] The Creative Color Picker section can filter color selections based on the user's current project or area of ​​interest when using the color picker. For example, the Creative Color Picker section prioritizes displaying colors suitable for the user's current project. For example, the Creative Color Picker section prioritizes displaying relevant colors based on the user's area of ​​interest. For example, the Creative Color Picker section suggests appropriate colors according to the user's project progress. This allows for a more appropriate color selection method by filtering color selections based on the user's current project or area of ​​interest.

[0096] The creative color picker unit can estimate the user's emotions and adjust the frequency of color picker use based on the estimated emotions. For example, if the user is relaxed, the creative color picker unit will frequently use the color picker to try new colors. For example, if the user is focused, the creative color picker unit will minimize the frequency of color picker use to maintain the workflow. For example, if the user is tired, the creative color picker unit will adjust the frequency of color picker use to encourage breaks. This allows for a more appropriate color selection experience by adjusting the frequency of color picker use according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The Creative Color Picker section can prioritize displaying highly relevant color selections by considering the user's geographical location when using the color picker. For example, if the user is in a specific region, the Creative Color Picker section will suggest colors based on the culture and scenery of that region. For example, if the user is traveling, the Creative Color Picker section will suggest colors that reflect the characteristics of the region they are visiting. For example, if the user is at home, the Creative Color Picker section will suggest colors that have been used in that location in the past. In this way, by considering the user's geographical location, the Creative Color Picker section can prioritize displaying highly relevant color selections.

[0098] The Creative Color Picker section can analyze a user's social media activity and suggest relevant color choices when the color picker is used. For example, the Creative Color Picker section can analyze the colors of images the user has shared on social media and suggest relevant colors. For example, the Creative Color Picker section can suggest colors based on the colors in the works of artists the user follows. For example, the Creative Color Picker section can suggest colors based on topics the user has shown interest in on social media. In this way, by analyzing the user's social media activity, it can suggest relevant color choices.

[0099] The art simulation tool can estimate the user's emotions and adjust the display method of the simulation based on the estimated emotions. For example, if the user is relaxed, the art simulation tool provides a simulation with soft colors. For example, if the user is focused, the art simulation tool provides a simple and highly visible simulation. For example, if the user is excited, the art simulation tool provides a visually stimulating simulation. This allows for a more appropriate simulation experience by adjusting the display method of the simulation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The art simulation tool can suggest the optimal simulation method by referring to the user's past art history when running a simulation. For example, the art simulation tool can perform a simulation by referring to the color tones of works the user has created in the past. For example, the art simulation tool can perform a simulation while avoiding color tones that the user has avoided in the past. For example, the art simulation tool can suggest a simulation method suitable for a specific project based on the user's past art history. In this way, the optimal simulation method can be suggested by referring to the user's past art history.

[0101] The art simulation tool can filter simulations based on the user's current project and areas of interest during execution. For example, it can provide simulations suitable for the user's current project. It can also prioritize displaying relevant simulations based on the user's areas of interest. Furthermore, it can suggest appropriate simulations based on the user's project progress. By filtering simulations based on the user's current project and areas of interest, it can provide a more appropriate simulation method.

[0102] The art simulation tool can estimate the user's emotions and adjust the frequency of simulations based on those emotions. For example, if the user is relaxed, the art simulation tool will run simulations frequently to test new ideas. If the user is focused, the art simulation tool will minimize the frequency of simulations to maintain the workflow. If the user is tired, the art simulation tool will adjust the frequency of simulations to encourage breaks. By adjusting the frequency of simulations according to the user's emotions, a more appropriate simulation experience can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The art simulation tool can prioritize displaying simulations that are highly relevant to the user's geographical location when running simulations. For example, if the user is in a specific region, the art simulation tool will provide a simulation based on the culture and scenery of that region. For example, if the user is traveling, the art simulation tool will provide a simulation that reflects the characteristics of the region they are visiting. For example, if the user is at home, the art simulation tool will perform a simulation referencing the color tones of artwork previously created in that location. In this way, by considering the user's geographical location, the system can prioritize displaying simulations that are highly relevant to the user.

[0104] The art simulation tool can analyze the user's social media activity during simulation execution and suggest relevant simulations. For example, it can analyze the color palette of artwork shared by the user on social media and provide relevant simulations. It can also perform simulations based on the color palette of artwork by artists the user follows. Furthermore, it can suggest simulations based on topics the user has shown interest in on social media. In this way, by analyzing the user's social media activity, it can suggest relevant simulations.

[0105] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0106] The color theory understanding support system can estimate the user's emotions and customize the content of the learning interface based on those emotions. For example, if the user is relaxed, it can provide educational modules with detailed explanations. If the user is in a hurry, it can provide short, concise educational modules. If the user is excited, it can provide educational modules with visually stimulating content. This allows for a more effective learning experience by adjusting the content of educational modules according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other things. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The color theory understanding support system can suggest the optimal learning method by referring to the user's past learning history. For example, it can suggest learning methods that the user has preferred in the past. It can also suggest learning methods that the user has avoided in the past. Based on the user's past learning history, it can suggest learning methods suitable for specific topics. In this way, by referring to the user's past learning history, it can suggest the optimal learning method.

[0108] The color theory understanding support system can customize learning content based on the user's current learning progress. For example, it can prioritize providing content related to the topic the user is currently studying. It can suggest the next topic to learn based on the user's learning progress. It can adjust the difficulty level of the learning content to match the user's learning pace. By customizing learning content based on the user's current learning progress, it can provide a more effective learning experience.

[0109] The color theory understanding support system can estimate the user's emotions and adjust the frequency of educational modules based on those emotions. For example, if the user is relaxed, educational modules can be provided more frequently to facilitate learning. If the user is focused, the frequency of educational modules can be optimized to maintain the learning flow. If the user is tired, the frequency of educational modules can be adjusted to encourage breaks. This allows for a more effective learning experience by adjusting the frequency of educational modules according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other things. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The color theory understanding support system can suggest the optimal learning method by considering the user's device information. For example, if the user is using a smartphone, it can provide a learning method adapted to the screen size. If the user is using a tablet, it can provide a learning method optimized for a larger screen. If the user is using a personal computer, it can provide a learning method that includes detailed information. In this way, by considering the user's device information, it can suggest the most suitable learning method.

[0111] The color theory understanding support system can analyze a user's social media activity and suggest relevant learning content. For example, it can suggest learning content based on content the user has shared on social media. It can also suggest learning content based on information from educators and experts the user follows. It can suggest learning content based on topics the user has shown interest in on social media. In this way, by analyzing the user's social media activity, it can suggest relevant learning content.

[0112] The color theory understanding support system can estimate the user's emotions and adjust the display method of the color picker based on the estimated emotions. For example, if the user is relaxed, it can provide an interface with soft colors. If the user is focused, it can provide a simple and highly visible interface. If the user is excited, it can provide a visually stimulating interface. By adjusting the display method of the color picker according to the user's emotions, a more appropriate color selection experience can be provided. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The color theory understanding support system can suggest the optimal color selection method by referring to the user's past color selection history when using a color picker. For example, it can prioritize displaying colors that the user has previously preferred to use. It can also suggest avoiding colors that the user has previously avoided. Based on the user's past color selection history, it can suggest colors suitable for a specific project. In this way, by referring to the user's past color selection history, it can suggest the optimal color selection method.

[0114] The color theory understanding support system can filter color selections based on the user's current project and areas of interest when using a color picker. For example, it can prioritize displaying colors suitable for the user's current project. It can prioritize displaying relevant colors based on the user's areas of interest. It can suggest appropriate colors according to the progress of the user's project. In this way, by filtering color selections based on the user's current project and areas of interest, it can provide a more appropriate method of color selection.

[0115] The color theory understanding support system can estimate the user's emotions and adjust the frequency of color picker use based on the estimated emotions. For example, if the user is relaxed, they can frequently use the color picker to try new colors. If the user is focused, the frequency of color picker use can be minimized to maintain the workflow. If the user is tired, the frequency of color picker use can be adjusted to encourage breaks. This allows for a more appropriate color selection experience by adjusting the frequency of color picker use according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The following briefly describes the processing flow for example form 2.

[0117] Step 1: The dual color mode section can be switched between RGB and CMY modes to visualize different color mixing. For example, mixing red and green in RGB mode produces yellow, and mixing cyan and magenta in CMY mode produces blue. This allows for a visual understanding of color mixing in different color modes. Step 2: The learning interface section provides educational modules on color theory, allowing users to learn through hands-on experience. For example, by learning about the color wheel and complementary colors, and then actually mixing colors, users can gain a deeper understanding of color theory. Step 3: The Creative Color Picker section generates unique colors using a new color picker that combines digital and physical colors. For example, it can generate new colors by combining a color selected digitally with a paint color the user physically possesses. Step 4: The art simulation tool section displays a real-time prediction of the finished artwork using the three primary colors of light and pigment. For example, when a user inputs a digitally drawn picture into the simulation tool, it can display in real time what the actual colors will look like using the three primary colors of light and pigment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the dual color mode unit, learning interface unit, creative color picker unit, and art simulation tool unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the dual color mode unit is implemented by the control unit 46A of the smart device 14, is switchable between RGB and CMY modes, and visualizes different color mixtures. The learning interface unit is implemented by the specific processing unit 290 of the data processing unit 12, provides an educational module on color theory, and allows users to learn through hands-on experience. The creative color picker unit is implemented by the control unit 46A of the smart device 14, and generates unique colors using a new color picker that combines digital and physical colors. The art simulation tool unit is implemented by the specific processing unit 290 of the data processing unit 12, and displays a real-time prediction of the finished artwork using the three primary colors of light and the three primary colors of pigment in an art simulation. 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.

[0122] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the dual color mode unit, learning interface unit, creative color picker unit, and art simulation tool unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the dual color mode unit is implemented by the control unit 46A of the smart glasses 214, is switchable between RGB and CMY modes, and visualizes different color mixtures. The learning interface unit is implemented by the specific processing unit 290 of the data processing unit 12, provides an educational module on color theory, and allows users to learn through hands-on experience. The creative color picker unit is implemented by the control unit 46A of the smart glasses 214, and generates unique colors using a new color picker that combines digital and physical colors. The art simulation tool unit is implemented by the specific processing unit 290 of the data processing unit 12, and displays a real-time prediction of the finished artwork using the three primary colors of light and the three primary colors of pigment in an art simulation. 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.

[0138] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the dual color mode unit, learning interface unit, creative color picker unit, and art simulation tool unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the dual color mode unit is implemented by the control unit 46A of the headset terminal 314, is switchable between RGB and CMY modes, and visualizes different color mixtures. The learning interface unit is implemented by the specific processing unit 290 of the data processing unit 12, provides an educational module on color theory, and allows users to learn through hands-on experience. The creative color picker unit is implemented by the control unit 46A of the headset terminal 314, and generates unique colors using a new color picker that combines digital and physical colors. The art simulation tool unit is implemented by the specific processing unit 290 of the data processing unit 12, and displays a real-time prediction of the finished artwork using art simulation with the three primary colors of light and the three primary colors of pigment. 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.

[0154] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the dual color mode unit, learning interface unit, creative color picker unit, and art simulation tool unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the dual color mode unit is implemented by the control unit 46A of the robot 414, is switchable between RGB and CMY modes, and visualizes different color mixtures. The learning interface unit is implemented by the specific processing unit 290 of the data processing unit 12, provides an educational module on color theory, and allows users to learn through hands-on experience. The creative color picker unit is implemented by the control unit 46A of the robot 414, and generates unique colors using a new color picker that combines digital and physical colors. The art simulation tool unit is implemented by the specific processing unit 290 of the data processing unit 12, and displays a real-time prediction of the finished artwork using art simulation with the three primary colors of light and the three primary colors of pigment. 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] (Note 1) It can switch between RGB and CMY modes, and features a dual-color mode section that visualizes different color mixtures. The dual-color mode unit provides an educational module on color theory, and the learning interface unit allows users to learn through hands-on experience. A creative color picker unit generates unique colors using a new color picker that combines digital and physical colors provided by the aforementioned learning interface unit, The system includes an art simulation tool unit that displays a real-time prediction of the finished artwork using the colors generated by the aforementioned creative color picker unit. A system characterized by the following features. (Note 2) The dual-color mode section is, Switchable between RGB and CMY modes to visualize different color mixtures. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning interface unit is We provide educational modules on color theory, allowing users to learn through hands-on experience. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned creative color picker section is Use a new color picker that combines digital and physical colors to generate your own unique colors. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned art simulation tool section is: This art simulation uses the three primary colors of light and the three primary colors of pigment to display a real-time prediction of the finished artwork. The system described in Appendix 1, characterized by the features described herein. (Note 6) The dual-color mode section is, When a user mixes red and green in RGB mode, yellow is produced; when a user mixes cyan and magenta in CMY mode, blue is produced. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning interface unit is By learning about the color wheel and the concept of complementary colors, and by actually mixing colors, you can gain a deeper understanding of color theory. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned creative color picker section is It generates new colors by combining the colors the user digitally selects with the colors of paints they physically possess. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned art simulation tool section is: When a user inputs a digitally drawn image into the simulation tool, it uses the three primary colors of light and the three primary colors of pigment to display in real time what the resulting colors would actually look like. The system described in Appendix 1, characterized by the features described herein. (Note 10) The dual-color mode section is, It estimates the user's emotions and adjusts the timing of switching between RGB and CMY modes based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The dual-color mode section is, When switching between RGB and CMY modes, the system refers to the user's past color mixing history to suggest the optimal color mixing method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The dual-color mode section is, When switching between RGB and CMY modes, color mixing is filtered based on the user's current project and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The dual-color mode section is, It estimates the user's emotions and adjusts the frequency of switching between RGB and CMY modes based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The dual-color mode section is, When switching between RGB and CMY modes, the system prioritizes displaying the most relevant color mixture, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The dual-color mode section is, When switching between RGB and CMY modes, the system analyzes the user's social media activity and suggests relevant color mixtures. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning interface unit is The system estimates the user's emotions and adjusts the content of the educational modules based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning interface unit is When providing educational modules, we refer to the user's past learning history to suggest the most suitable learning method. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning interface unit is When providing educational modules, customize the learning content based on the user's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning interface unit is The system estimates the user's emotions and adjusts the frequency of educational modules based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning interface unit is When providing educational modules, we propose the optimal learning method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning interface unit is When providing educational modules, we analyze users' social media activity and suggest relevant learning content. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned creative color picker section is It estimates the user's emotions and adjusts how the color picker is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned creative color picker section is When using the color picker, the system refers to the user's past color selection history to suggest the optimal color selection method. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned creative color picker section is When using the color picker, filter color selections based on the user's current project or area of ​​interest. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned creative color picker section is It estimates the user's emotions and adjusts the frequency of color picker usage based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned creative color picker section is When using the color picker, the system prioritizes displaying more relevant color selections by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned creative color picker section is When using the color picker, the system analyzes the user's social media activity and suggests relevant color choices. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned art simulation tool section is: It estimates the user's emotions and adjusts how the simulation is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned art simulation tool section is: When running a simulation, the system refers to the user's past artwork history to suggest the optimal simulation method. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned art simulation tool section is: When running a simulation, the simulation is filtered based on the user's current project and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned art simulation tool section is: It estimates the user's emotions and adjusts the frequency of simulation execution based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned art simulation tool section is: When running simulations, the system prioritizes displaying simulations that are more relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned art simulation tool section is: During simulation execution, the system analyzes the user's social media activity and suggests relevant simulations. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0190] 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. It can switch between RGB and CMY modes, and features a dual-color mode section that visualizes different color mixtures. The dual-color mode unit provides an educational module on color theory, and the learning interface unit allows users to learn through hands-on experience. A creative color picker unit generates unique colors using a new color picker that combines digital and physical colors provided by the aforementioned learning interface unit, The system includes an art simulation tool unit that displays a real-time prediction of the finished artwork using the colors generated by the aforementioned creative color picker unit. A system characterized by the following features.

2. The aforementioned art simulation tool section is: This art simulation uses the three primary colors of light and the three primary colors of pigment to display a real-time prediction of the finished artwork. The system according to feature 1.

3. The dual-color mode section is, When a user mixes red and green in RGB mode, yellow is produced; when a user mixes cyan and magenta in CMY mode, blue is produced. The system according to feature 1.

4. The aforementioned learning interface unit is By learning about the color wheel and the concept of complementary colors, and by actually mixing colors, you can gain a deeper understanding of color theory. The system according to feature 1.

5. The aforementioned creative color picker section is It generates new colors by combining the colors the user digitally selects with the colors of paints they physically possess. The system according to feature 1.

6. The aforementioned art simulation tool section is: When a user inputs a digitally drawn image into the simulation tool, it uses the three primary colors of light and the three primary colors of pigment to display in real time what the resulting colors would actually look like. The system according to feature 1.

7. The dual-color mode section is, It estimates the user's emotions and adjusts the timing of switching between RGB and CMY modes based on the estimated emotions. The system according to feature 1.

Images