system

Abstract

The system according to this embodiment aims to easily create materials that are highly visible to people with color vision deficiency. [Solution] The system according to the embodiment comprises a collection unit, a checking unit, an alert unit, a suggestion unit, and a correction unit. The collection unit visually converts materials from the perspective of people with color blindness and identifies problem areas. The checking unit performs visual type-specific checks corresponding to multiple color blindness types based on the information collected by the collection unit. The alert unit automatically displays alerts and comments for areas with low visibility identified by the checking unit. The suggestion unit proposes alternative colors or color adjustments to maintain consistency for the areas pointed out by the alert unit. The correction unit automatically corrects the alternative colors and adjustments proposed by the suggestion unit.

Description

Technical Field

[0006] , ,

[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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to create materials with high visibility for people with color vision deficiencies, and there is a problem that color scheme changes and color adjustments to maintain consistency are time-consuming.

[0005] The system according to the embodiment aims to easily create materials with high visibility for people with color vision deficiencies.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a checking unit, an alert unit, a suggestion unit, and a correction unit. The collection unit visually converts materials from the perspective of people with color vision deficiency and identifies problem areas. The checking unit performs visual type-specific checks corresponding to multiple color vision types based on the information collected by the collection unit. The alert unit automatically displays alerts and comments for areas with low visibility identified by the checking unit. The suggestion unit proposes alternative colors or color adjustments to maintain consistency for the areas pointed out by the alert unit. The correction unit automatically corrects the alternative colors and adjustments proposed by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can easily create materials that are highly visible to people with color vision deficiency. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​tool according to an embodiment of the present invention is a system that supports the creation of materials that are considerate of people with color vision deficiency. This system visually transforms materials from the perspective of people with color vision deficiency and identifies problem areas. Next, it performs visual type-specific checks that correspond to multiple color vision types, such as red-green color blindness, blue-yellow color blindness, and complete color blindness. Furthermore, it automatically displays alerts for areas with low visibility and displays comments such as "This combination has low visibility." Next, it suggests alternative colors based on universal color principles and color adjustments to maintain consistency. In addition, it is equipped with an AI-powered automatic color correction function that easily corrects colors and shows the difference between the original and new colors. Finally, it provides customizable settings and insights that explain the reasons for improving the color scheme and adjustable options within a color palette based on brand guidelines. This mechanism makes it easy to create materials that are easy for everyone to understand, promoting inclusive design that is not dependent on color vision. It also enables universal design compliance while maintaining brand image. For example, the AI ​​tool visually transforms materials from the perspective of people with color vision deficiency and identifies problem areas. Next, it performs a visual type check that accommodates multiple color vision types, including red-green color blindness, blue-yellow color blindness, and complete color blindness. Furthermore, it automatically displays alerts for areas with low visibility and provides comments such as "This combination has low visibility." Next, it suggests alternative colors and consistent color adjustments based on universal color principles. In addition, it features an AI-powered automatic color correction function that easily corrects colors and shows the difference between the original and new colors. Finally, it provides customizable settings and insights that explain the reasons for improving the color scheme and adjustable options within the color palette based on brand guidelines. This mechanism makes it easy to create materials that are easy for everyone to understand, promoting inclusive design that is not dependent on color vision. It also enables universal design compliance while maintaining the brand image. As a result, the AI ​​tool can efficiently create materials that are considerate of people with color vision deficiencies.

[0029] The AI ​​tool according to this embodiment comprises a data collection unit, a checking unit, an alert unit, a suggestion unit, and a correction unit. The data collection unit visually transforms materials from the perspective of people with color vision deficiency and identifies problem areas. For example, the data collection unit uses a universal color mask to visually transform materials from the perspective of people with color vision deficiency and identifies problem areas. The checking unit performs visual type-specific checks corresponding to multiple color vision types based on the information collected by the data collection unit. For example, the checking unit supports multiple color vision types such as red-green color blindness, blue-yellow color blindness, and complete color blindness. The alert unit automatically displays alerts and comments for areas with low visibility identified by the checking unit. For example, the alert unit automatically displays alerts for areas with low visibility and displays comments such as "This combination has low visibility." The suggestion unit proposes alternative colors or color adjustments to maintain consistency for the areas pointed out by the alert unit. For example, the suggestion unit proposes alternative colors or color adjustments to maintain consistency based on universal color. The correction unit automatically corrects the alternative colors and adjustments suggested by the proposal unit. For example, the correction unit easily corrects the colors using an automatic correction function and shows the difference between the original color and the new color. As a result, the AI ​​tool according to this embodiment can efficiently create materials that are considerate of people with color vision deficiencies.

[0030] The data collection unit visually transforms materials from the perspective of individuals with color vision deficiency to identify problem areas. Specifically, it uses a universal color mask to visually transform materials from the perspective of individuals with color vision deficiency and identify problem areas. The universal color mask applies different filters depending on the type of color vision deficiency to transform the colors of the material. For example, in the case of red-green color blindness, it becomes difficult to distinguish between red and green, so these colors are transformed to identify areas with visual problems. The data collection unit uses image processing technology to analyze the transformed materials and identify areas with low visibility. This allows it to simulate how individuals with color vision deficiency view materials and identify problem areas. Furthermore, the data collection unit can centrally manage the collected data and share it with other departments. For example, the collected data is stored on a cloud server and made accessible to the checking and alerting units. The data collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The checking unit performs visual type-specific checks based on information collected by the data collection unit, addressing multiple color vision types. Specifically, it supports multiple color vision types such as red-green color blindness, blue-yellow color blindness, and complete color blindness. The checking unit applies different algorithms to each color vision type to identify areas of visual problem. For example, in the case of red-green color blindness, since distinguishing between red and green is difficult, the unit checks whether combinations of these colors affect visibility. In the case of blue-yellow color blindness, it checks combinations of blue and yellow, and in the case of complete color blindness, it checks all color combinations. The checking unit uses image analysis technology and machine learning algorithms to identify areas of visual problem. This allows the checking unit to quickly and accurately analyze the collected data and identify areas of visual problem. Furthermore, the checking unit can also analyze long-term trends in visual problems by utilizing past data and statistical information. For example, based on data on areas of visual problem in past materials, it can evaluate the impact of specific color combinations on visibility and identify points to pay attention to when creating future materials. This allows the checking unit to not only identify visual problems in real time, but also to analyze long-term trends in visual problems, thereby improving the reliability and safety of the entire system.

[0032] The alert unit automatically displays alerts and comments for areas with low visibility identified by the check unit. Specifically, it automatically displays alerts for areas with low visibility and displays comments such as "This combination has low visibility." The alert unit analyzes data provided by the check unit to identify areas with low visibility in order to pinpoint visual problems. The alert unit displays visual alerts for areas with low visibility to draw the user's attention. For example, it may display a red frame around areas with low visibility and a comment such as "This combination has low visibility." The alert unit can also offer specific improvement suggestions to the user. For example, it may display a comment such as "Visibility will be improved by changing this color combination," offering a specific improvement suggestion to the user. This allows the alert unit to quickly and accurately identify visual problems and offer specific improvement suggestions to the user. Furthermore, the alert unit can collect user feedback and continuously improve the accuracy and effectiveness of its alerts. For example, based on user feedback, it can review how alerts are displayed and the content of comments to provide more effective alerts. Furthermore, the alert unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only visual alerts but also voice notifications and vibration notifications. This allows the alert unit to quickly and reliably notify users of visual problem areas, thereby supporting improved visibility.

[0033] The proposal team suggests alternative colors or consistent color adjustments for areas identified by the alert team. Specifically, they propose alternative colors or consistent color adjustments based on universal color principles. The proposal team utilizes knowledge of color theory and visual psychology to suggest optimal alternative colors for areas with visual problems. For example, in the case of red-green color blindness, the combination of red and green affects visibility, so they suggest alternative colors that avoid these colors. The proposal team suggests specific alternative colors for areas with visual problems, helping users improve visibility. For example, they might display a comment such as, "Changing this color combination will improve visibility," and suggest specific alternative colors to the user. The proposal team can also adjust the overall color scheme to maintain visual consistency. For example, they might review the color scheme of the entire document and suggest highly visible color combinations. This allows the proposal team to suggest specific alternative colors for areas with visual problems and help improve visibility. Furthermore, the proposal team can collect user feedback and continuously improve the accuracy and effectiveness of their suggestions. For example, they might revise their suggestions based on user feedback and propose more effective alternative colors. Furthermore, the proposal unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only visual suggestions but also voice notifications and vibration notifications. This allows the proposal unit to quickly and reliably notify users of visual problem areas and support improved visibility.

[0034] The correction unit automatically corrects the alternative colors and adjustments suggested by the suggestion unit. Specifically, it easily corrects colors using an automatic correction function and shows the difference between the original and new colors. The correction unit automatically applies suggested alternative colors to areas with visual problems to improve visibility. For example, in the case of red-green color blindness, the combination of red and green affects visibility, so the unit automatically applies alternative colors that avoid these colors. The correction unit automatically applies specific alternative colors to areas with visual problems, helping users improve visibility. Furthermore, the correction unit can clearly communicate the changes to the user by showing the difference between the original and new colors. For example, it can display a comment such as "The original color was red, but the new color is blue," clearly communicating the changes to the user. This allows the correction unit to automatically apply specific alternative colors to areas with visual problems, helping to improve visibility. In addition, the correction unit can collect user feedback and continuously improve the accuracy and effectiveness of the corrections. For example, it can review the corrections based on user feedback and apply more effective alternative colors. Furthermore, the correction unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only visual correction but also voice notifications and vibration notifications. This allows the correction unit to quickly and reliably notify the user of visual problem areas and support improved visibility.

[0035] The data collection unit can use a universal color mask to visually transform materials from the perspective of people with color vision deficiency and identify problem areas. For example, the data collection unit can use a universal color mask to visually transform materials from the perspective of people with color vision deficiency and identify problem areas. This allows for the accurate identification of problem areas from the perspective of people with color vision deficiency by using a universal color mask.

[0036] The checking unit can accommodate multiple color vision types, such as red-green color blindness, blue-yellow color blindness, and complete color blindness. This allows for checks that accommodate various types of color vision deficiencies.

[0037] The alert function can automatically display alerts for areas with low visibility and show comments such as "This combination has low visibility." For example, the alert function can automatically display alerts for areas with low visibility and show comments such as "This combination has low visibility." This allows for the clear identification of problem areas by automatically displaying alerts and comments for areas with low visibility.

[0038] The proposal department can suggest alternative colors and consistent color adjustments based on universal color theory. For example, the proposal department can suggest alternative colors and consistent color adjustments based on universal color theory. This allows for improved visibility and consistent color adjustments.

[0039] The correction unit can easily correct colors using its automatic correction function and show the difference between the original and new colors. For example, the correction unit can easily correct colors using its automatic correction function and show the difference between the original and new colors. This allows for clearer identification of the corrections made by easily correcting colors using the automatic correction function and showing the difference between the original and new colors.

[0040] The correction section can explain the adjustable options and reasons for improving the color scheme within the color palette based on the brand's guidelines. For example, the correction section explains the adjustable options and reasons for improving the color scheme within the color palette based on the brand's guidelines. This allows for color improvements while maintaining the brand image.

[0041] The data collection unit can apply different visual transformation algorithms depending on the type of color vision deficiency. For example, in the case of red-green color blindness, the data collection unit applies an algorithm that emphasizes the contrast between red and green. For example, in the case of blue-yellow color blindness, the data collection unit applies an algorithm that emphasizes the contrast between blue and yellow. For example, in the case of complete color blindness, the data collection unit applies an algorithm that equally emphasizes the contrast of all colors. By applying different visual transformation algorithms depending on the type of color vision deficiency, it becomes possible to perform visual transformations that correspond to each type of color vision deficiency.

[0042] The data collection unit can adjust the level of detail in the visual transformation based on the content of the material. For example, if the material is text-based, the data collection unit will emphasize the contrast between the text color and the background color. For example, if the material is graph-based, the data collection unit will emphasize the contrast between the graph color and the background color. For example, if the material is image-based, the data collection unit will emphasize the contrast between the image color and the background color. By adjusting the level of detail in the visual transformation based on the content of the material, it becomes possible to achieve optimal visual transformation according to the type of material.

[0043] The data collection unit can customize the visual transformation method according to the type of material. For example, the data collection unit customizes the visual transformation method according to the type of material (presentation, report, web page, etc.). For example, in the case of a presentation, the data collection unit customizes the visual transformation method for each slide. For example, in the case of a report, the data collection unit customizes the visual transformation method for each chapter. For example, in the case of a web page, the data collection unit customizes the visual transformation method for each page. By customizing the visual transformation method according to the type of material, it becomes possible to achieve optimal visual transformation for each material.

[0044] The data collection unit can select the optimal visual transformation method by referring to the document creator's past visual transformation history. For example, the data collection unit proposes the optimal visual transformation method based on the visual transformation methods the document creator has used in the past. For example, the data collection unit selects the most effective visual transformation method from the document creator's past visual transformation history. For example, the data collection unit analyzes the document creator's past visual transformation history and selects the optimal visual transformation method. This allows the optimal visual transformation method to be selected by referring to the document creator's past visual transformation history.

[0045] The checking unit can apply different checking algorithms depending on the type of color vision deficiency. For example, in the case of red-green color blindness, the checking unit applies a checking algorithm that emphasizes the contrast between red and green. For example, in the case of blue-yellow color blindness, the checking unit applies a checking algorithm that emphasizes the contrast between blue and yellow. For example, in the case of complete color blindness, the checking unit applies a checking algorithm that equally emphasizes the contrast of all colors. In this way, by applying different checking algorithms for each type of color vision deficiency, it becomes possible to perform checks that correspond to each type of color vision deficiency.

[0046] The checking function can adjust the level of detail based on the content of the document. For example, if the document is text-heavy, the checking function will emphasize the contrast between the text color and the background color. If the document is graph-heavy, the checking function will emphasize the contrast between the graph color and the background color. If the document is image-heavy, the checking function will emphasize the contrast between the image color and the background color. By adjusting the level of detail based on the content of the document, it becomes possible to perform optimal checking according to the type of document.

[0047] The checking function can customize its checking method depending on the type of document. For example, the checking function customizes the checking method depending on the type of document (presentation, report, web page, etc.). For example, in the case of a presentation, the checking function customizes the checking method for each slide. For example, in the case of a report, the checking function customizes the checking method for each chapter. For example, in the case of a web page, the checking function customizes the checking method for each page. This allows for optimal checking of each document by customizing the checking method according to the type of document.

[0048] The checking unit can select the optimal checking method by referring to the document creator's past checking history. For example, the checking unit proposes the optimal checking method based on the checking methods the document creator has used in the past. For example, the checking unit selects the most effective checking method from the document creator's past checking history. For example, the checking unit analyzes the document creator's past checking history and selects the optimal checking method. This allows the optimal checking method to be selected by referring to the document creator's past checking history.

[0049] The alert function can adjust the level of detail displayed in the comments based on the content of the alert. For example, if the alert is important, the alert function will display detailed comments to clarify the problem. For example, if the alert is minor, the alert function will display concise comments to quickly identify the problem. For example, if the alert is of moderate importance, the alert function will display comments with appropriate level of detail to clarify the problem. In this way, the problem can be clearly identified by adjusting the level of detail displayed in the comments based on the content of the alert.

[0050] The alert function can adjust the timing of alerts based on the content of the document. For example, if the document is text-heavy, the alert function will display alerts in areas where the contrast between the text color and background color is low. If the document is graph-heavy, the alert function will display alerts in areas where the contrast between the graph color and background color is low. If the document is image-heavy, the alert function will display alerts in areas where the contrast between the image color and background color is low. By adjusting the timing of alerts based on the content of the document, it becomes possible to display alerts optimally for each document.

[0051] The alert function allows you to customize how alerts are displayed depending on the type of document. For example, the alert function customizes how alerts are displayed depending on the type of document (presentation, report, web page, etc.). For example, in the case of a presentation, the alert function customizes how alerts are displayed for each slide. For example, in the case of a report, the alert function customizes how alerts are displayed for each chapter. For example, in the case of a web page, the alert function customizes how alerts are displayed for each page. By customizing how alerts are displayed according to the type of document, it becomes possible to display alerts in a way that is optimal for each document.

[0052] The alerting unit can select the optimal alerting method by referring to the document creator's past alert history. For example, the alerting unit proposes the optimal alerting method based on the alerting methods the document creator has used in the past. For example, the alerting unit selects the most effective alerting method from the document creator's past alert history. For example, the alerting unit analyzes the document creator's past alert history to select the optimal alerting method. This allows the optimal alerting method to be selected by referring to the document creator's past alert history.

[0053] The proposal team can adjust the level of detail in their proposals based on the importance of the alternative colors. For example, for important alternative colors, the proposal team will provide a detailed explanation. For minor alternative colors, the proposal team will provide a concise proposal. For moderately important alternative colors, the proposal team will provide a proposal with a moderate level of detail. By adjusting the level of detail in proposals based on the importance of the alternative colors, it becomes possible to provide appropriate proposals for important alternative colors.

[0054] The suggestion function can apply different suggestion algorithms depending on the category of the document. For example, the suggestion function can apply different suggestion algorithms depending on the category of the document. For example, in the case of a presentation, the suggestion function can suggest the optimal alternative color for each slide. For example, in the case of a report, the suggestion function can suggest the optimal alternative color for each chapter. For example, in the case of a web page, the suggestion function can suggest the optimal alternative color for each page. This makes it possible to provide optimal suggestions for each document by applying different suggestion algorithms depending on the category of the document.

[0055] The proposal department can customize its proposal methods depending on the type of document. For example, the proposal department customizes its proposal methods depending on the type of document (presentation, report, web page, etc.). For example, in the case of a presentation, the proposal department customizes the proposal method for each slide. For example, in the case of a report, the proposal department customizes the proposal method for each chapter. For example, in the case of a web page, the proposal department customizes the proposal method for each page. By customizing the proposal method according to the type of document, it becomes possible to provide the most suitable proposal for each document.

[0056] The proposal department can select the optimal proposal method by referring to the document creator's past proposal history. For example, the proposal department proposes the optimal proposal method based on the proposal methods the document creator has used in the past. For example, the proposal department selects the most effective proposal method from the document creator's past proposal history. For example, the proposal department analyzes the document creator's past proposal history to select the optimal proposal method. This allows the proposal department to select the optimal proposal method by referring to the document creator's past proposal history.

[0057] The correction unit can adjust how the difference between the original and new colors is visually displayed. For example, in the case of significant corrections, the correction unit visually displays detailed differences. For example, in the case of minor corrections, the correction unit visually displays concise differences. For example, in the case of moderate corrections, the correction unit visually displays differences with a moderate level of detail. This allows for clearer identification of the corrections by adjusting how the difference between the original and new colors is visually displayed.

[0058] The correction unit can adjust the level of detail of the correction based on the content of the document. For example, if the document is text-heavy, the correction unit will enhance the contrast between the text color and the background color. For example, if the document is graph-heavy, the correction unit will enhance the contrast between the graph color and the background color. For example, if the document is image-heavy, the correction unit will enhance the contrast between the image color and the background color. By adjusting the level of detail of the correction based on the content of the document, it becomes possible to perform the optimal correction for each document.

[0059] The correction unit can customize the correction method according to the type of document. For example, the correction unit customizes the correction method according to the type of document (presentation, report, web page, etc.). For example, in the case of a presentation, the correction unit customizes the correction method for each slide. For example, in the case of a report, the correction unit customizes the correction method for each chapter. For example, in the case of a web page, the correction unit customizes the correction method for each page. This allows for optimal correction for each document by customizing the correction method according to the type of document.

[0060] The correction unit can select the optimal correction method by referring to the document creator's past correction history. For example, the correction unit proposes the optimal correction method based on correction methods previously used by the document creator. For example, the correction unit selects the most effective correction method from the document creator's past correction history. For example, the correction unit analyzes the document creator's past correction history and selects the optimal correction method. This allows the optimal correction method to be selected by referring to the document creator's past correction history.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The data collection unit can adjust the level of detail in the visual transformation based on the content of the document. For example, if the document is text-heavy, it emphasizes the contrast between the text color and background color. If the document is graph-heavy, it emphasizes the contrast between the graph color and background color. If the document is image-heavy, it emphasizes the contrast between the image color and background color. This enables optimal visual transformation according to the type of document. The data collection unit can also select the optimal visual transformation method by referring to the document creator's past visual transformation history. For example, it can suggest the optimal visual transformation method based on the visual transformation methods the document creator has used in the past. It can select the most effective visual transformation method from the document creator's past visual transformation history. This allows for the selection of the optimal visual transformation method by referring to the document creator's past visual transformation history.

[0063] The checking function can adjust the level of detail of the check based on the content of the document. For example, if the document is text-heavy, it will emphasize the contrast between the text color and background color. If the document is graph-heavy, it will emphasize the contrast between the graph color and background color. If the document is image-heavy, it will emphasize the contrast between the image color and background color. This allows for optimal checking according to the type of document. The checking function can also select the optimal checking method by referring to the document creator's past checking history. For example, it can suggest the optimal checking method based on the checking methods the document creator has used in the past. It can select the most effective checking method from the document creator's past checking history. This allows for the selection of the optimal checking method by referring to the document creator's past checking history.

[0064] The alert function can adjust the timing of alerts based on the content of the document. For example, if the document is text-heavy, alerts will be displayed in areas with low contrast between the text color and background color. If the document is graph-heavy, alerts will be displayed in areas with low contrast between the graph color and background color. If the document is image-heavy, alerts will be displayed in areas with low contrast between the image color and background color. By adjusting the timing of alerts based on the content of the document, it is possible to display alerts optimally for each document. The alert function can also select the optimal alert method by referring to the document creator's past alert history. For example, it can suggest the optimal alert method based on the alert methods the document creator has used in the past. It can select the most effective alert method from the document creator's past alert history. This allows for the selection of the optimal alert method by referring to the document creator's past alert history.

[0065] The proposal function can apply different proposal algorithms depending on the category of the document. For example, for a presentation, it can suggest the best alternative color for each slide. For a report, it can suggest the best alternative color for each chapter. For a web page, it can suggest the best alternative color for each page. This allows for optimal proposals for each document by applying different proposal algorithms depending on the document category. The proposal function can also select the best proposal method by referring to the document creator's past proposal history. For example, it can suggest the best proposal method based on proposal methods the document creator has used in the past. It can select the most effective proposal method from the document creator's past proposal history. This allows for the selection of the best proposal method by referring to the document creator's past proposal history.

[0066] The correction unit can adjust the level of detail of the correction based on the content of the document. For example, if the document is text-heavy, it will perform correction to enhance the contrast between the text color and background color. If the document is graph-heavy, it will perform correction to enhance the contrast between the graph color and background color. If the document is image-heavy, it will perform correction to enhance the contrast between the image color and background color. By adjusting the level of detail of the correction based on the content of the document, it is possible to perform corrections that are optimal for each document. In addition, the correction unit can also select the optimal correction method by referring to the past correction history of the document creator. For example, it can suggest the optimal correction method based on the correction methods the document creator has used in the past. It can select the most effective correction method from the document creator's past correction history. This allows for the selection of the optimal correction method by referring to the document creator's past correction history.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection team visually transforms the materials from the perspective of people with color vision deficiency and identifies problem areas. For example, they use a universal color mask to visually transform the materials from the perspective of people with color vision deficiency and identify problem areas. Step 2: The checking unit performs a visual type-specific check based on the information collected by the collection unit, corresponding to multiple color vision types. For example, it can handle multiple color vision types such as red-green color blindness, blue-yellow color blindness, and complete color blindness. Step 3: The alert section automatically displays alerts and comments for areas with low visibility identified by the check section. For example, it automatically displays alerts for areas with low visibility and comments such as "This combination has low visibility." Step 4: The proposal team proposes alternative colors or consistent color adjustments for the areas pointed out by the alert team. For example, they might propose alternative colors or consistent color adjustments based on universal color theory. Step 5: The correction unit automatically corrects the alternative colors and adjustments suggested by the suggestion unit. For example, it easily corrects the colors using the automatic correction function and shows the difference between the original and new colors.

[0069] (Example of form 2) The AI ​​tool according to an embodiment of the present invention is a system that supports the creation of materials that are considerate of people with color vision deficiency. This system visually transforms materials from the perspective of people with color vision deficiency and identifies problem areas. Next, it performs visual type-specific checks that correspond to multiple color vision types, such as red-green color blindness, blue-yellow color blindness, and complete color blindness. Furthermore, it automatically displays alerts for areas with low visibility and displays comments such as "This combination has low visibility." Next, it suggests alternative colors based on universal color principles and color adjustments to maintain consistency. In addition, it is equipped with an AI-powered automatic color correction function that easily corrects colors and shows the difference between the original and new colors. Finally, it provides customizable settings and insights that explain the reasons for improving the color scheme and adjustable options within a color palette based on brand guidelines. This mechanism makes it easy to create materials that are easy for everyone to understand, promoting inclusive design that is not dependent on color vision. It also enables universal design compliance while maintaining brand image. For example, the AI ​​tool visually transforms materials from the perspective of people with color vision deficiency and identifies problem areas. Next, it performs a visual type check that accommodates multiple color vision types, including red-green color blindness, blue-yellow color blindness, and complete color blindness. Furthermore, it automatically displays alerts for areas with low visibility and provides comments such as "This combination has low visibility." Next, it suggests alternative colors and consistent color adjustments based on universal color principles. In addition, it features an AI-powered automatic color correction function that easily corrects colors and shows the difference between the original and new colors. Finally, it provides customizable settings and insights that explain the reasons for improving the color scheme and adjustable options within the color palette based on brand guidelines. This mechanism makes it easy to create materials that are easy for everyone to understand, promoting inclusive design that is not dependent on color vision. It also enables universal design compliance while maintaining the brand image. As a result, the AI ​​tool can efficiently create materials that are considerate of people with color vision deficiencies.

[0070] The AI ​​tool according to this embodiment comprises a data collection unit, a checking unit, an alert unit, a suggestion unit, and a correction unit. The data collection unit visually transforms materials from the perspective of people with color vision deficiency and identifies problem areas. For example, the data collection unit uses a universal color mask to visually transform materials from the perspective of people with color vision deficiency and identifies problem areas. The checking unit performs visual type-specific checks corresponding to multiple color vision types based on the information collected by the data collection unit. For example, the checking unit supports multiple color vision types such as red-green color blindness, blue-yellow color blindness, and complete color blindness. The alert unit automatically displays alerts and comments for areas with low visibility identified by the checking unit. For example, the alert unit automatically displays alerts for areas with low visibility and displays comments such as "This combination has low visibility." The suggestion unit proposes alternative colors or color adjustments to maintain consistency for the areas pointed out by the alert unit. For example, the suggestion unit proposes alternative colors or color adjustments to maintain consistency based on universal color. The correction unit automatically corrects the alternative colors and adjustments suggested by the proposal unit. For example, the correction unit easily corrects the colors using an automatic correction function and shows the difference between the original color and the new color. As a result, the AI ​​tool according to this embodiment can efficiently create materials that are considerate of people with color vision deficiencies.

[0071] The data collection unit visually transforms materials from the perspective of individuals with color vision deficiency to identify problem areas. Specifically, it uses a universal color mask to visually transform materials from the perspective of individuals with color vision deficiency and identify problem areas. The universal color mask applies different filters depending on the type of color vision deficiency to transform the colors of the material. For example, in the case of red-green color blindness, it becomes difficult to distinguish between red and green, so these colors are transformed to identify areas with visual problems. The data collection unit uses image processing technology to analyze the transformed materials and identify areas with low visibility. This allows it to simulate how individuals with color vision deficiency view materials and identify problem areas. Furthermore, the data collection unit can centrally manage the collected data and share it with other departments. For example, the collected data is stored on a cloud server and made accessible to the checking and alerting units. The data collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0072] The checking unit performs visual type-specific checks based on information collected by the data collection unit, addressing multiple color vision types. Specifically, it supports multiple color vision types such as red-green color blindness, blue-yellow color blindness, and complete color blindness. The checking unit applies different algorithms to each color vision type to identify areas of visual problem. For example, in the case of red-green color blindness, since distinguishing between red and green is difficult, the unit checks whether combinations of these colors affect visibility. In the case of blue-yellow color blindness, it checks combinations of blue and yellow, and in the case of complete color blindness, it checks all color combinations. The checking unit uses image analysis technology and machine learning algorithms to identify areas of visual problem. This allows the checking unit to quickly and accurately analyze the collected data and identify areas of visual problem. Furthermore, the checking unit can also analyze long-term trends in visual problems by utilizing past data and statistical information. For example, based on data on areas of visual problem in past materials, it can evaluate the impact of specific color combinations on visibility and identify points to pay attention to when creating future materials. This allows the checking unit to not only identify visual problems in real time, but also to analyze long-term trends in visual problems, thereby improving the reliability and safety of the entire system.

[0073] The alert unit automatically displays alerts and comments for areas with low visibility identified by the check unit. Specifically, it automatically displays alerts for areas with low visibility and displays comments such as "This combination has low visibility." The alert unit analyzes data provided by the check unit to identify areas with low visibility in order to pinpoint visual problems. The alert unit displays visual alerts for areas with low visibility to draw the user's attention. For example, it may display a red frame around areas with low visibility and a comment such as "This combination has low visibility." The alert unit can also offer specific improvement suggestions to the user. For example, it may display a comment such as "Visibility will be improved by changing this color combination," offering a specific improvement suggestion to the user. This allows the alert unit to quickly and accurately identify visual problems and offer specific improvement suggestions to the user. Furthermore, the alert unit can collect user feedback and continuously improve the accuracy and effectiveness of its alerts. For example, based on user feedback, it can review how alerts are displayed and the content of comments to provide more effective alerts. Furthermore, the alert unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only visual alerts but also voice notifications and vibration notifications. This allows the alert unit to quickly and reliably notify users of visual problem areas, thereby supporting improved visibility.

[0074] The proposal team suggests alternative colors or consistent color adjustments for areas identified by the alert team. Specifically, they propose alternative colors or consistent color adjustments based on universal color principles. The proposal team utilizes knowledge of color theory and visual psychology to suggest optimal alternative colors for areas with visual problems. For example, in the case of red-green color blindness, the combination of red and green affects visibility, so they suggest alternative colors that avoid these colors. The proposal team suggests specific alternative colors for areas with visual problems, helping users improve visibility. For example, they might display a comment such as, "Changing this color combination will improve visibility," and suggest specific alternative colors to the user. The proposal team can also adjust the overall color scheme to maintain visual consistency. For example, they might review the color scheme of the entire document and suggest highly visible color combinations. This allows the proposal team to suggest specific alternative colors for areas with visual problems and help improve visibility. Furthermore, the proposal team can collect user feedback and continuously improve the accuracy and effectiveness of their suggestions. For example, they might revise their suggestions based on user feedback and propose more effective alternative colors. Furthermore, the proposal unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only visual suggestions but also voice notifications and vibration notifications. This allows the proposal unit to quickly and reliably notify users of visual problem areas and support improved visibility.

[0075] The correction unit automatically corrects the alternative colors and adjustments suggested by the suggestion unit. Specifically, it easily corrects colors using an automatic correction function and shows the difference between the original and new colors. The correction unit automatically applies suggested alternative colors to areas with visual problems to improve visibility. For example, in the case of red-green color blindness, the combination of red and green affects visibility, so the unit automatically applies alternative colors that avoid these colors. The correction unit automatically applies specific alternative colors to areas with visual problems, helping users improve visibility. Furthermore, the correction unit can clearly communicate the changes to the user by showing the difference between the original and new colors. For example, it can display a comment such as "The original color was red, but the new color is blue," clearly communicating the changes to the user. This allows the correction unit to automatically apply specific alternative colors to areas with visual problems, helping to improve visibility. In addition, the correction unit can collect user feedback and continuously improve the accuracy and effectiveness of the corrections. For example, it can review the corrections based on user feedback and apply more effective alternative colors. Furthermore, the correction unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only visual correction but also voice notifications and vibration notifications. This allows the correction unit to quickly and reliably notify the user of visual problem areas and support improved visibility.

[0076] The data collection unit can use a universal color mask to visually transform materials from the perspective of people with color vision deficiency and identify problem areas. For example, the data collection unit can use a universal color mask to visually transform materials from the perspective of people with color vision deficiency and identify problem areas. This allows for the accurate identification of problem areas from the perspective of people with color vision deficiency by using a universal color mask.

[0077] The checking unit can accommodate multiple color vision types, such as red-green color blindness, blue-yellow color blindness, and complete color blindness. This allows for checks that accommodate various types of color vision deficiencies.

[0078] The alert function can automatically display alerts for areas with low visibility and show comments such as "This combination has low visibility." For example, the alert function can automatically display alerts for areas with low visibility and show comments such as "This combination has low visibility." This allows for the clear identification of problem areas by automatically displaying alerts and comments for areas with low visibility.

[0079] The proposal department can suggest alternative colors and consistent color adjustments based on universal color theory. For example, the proposal department can suggest alternative colors and consistent color adjustments based on universal color theory. This allows for improved visibility and consistent color adjustments.

[0080] The correction unit can easily correct colors using its automatic correction function and show the difference between the original and new colors. For example, the correction unit can easily correct colors using its automatic correction function and show the difference between the original and new colors. This allows for clearer identification of the corrections made by easily correcting colors using the automatic correction function and showing the difference between the original and new colors.

[0081] The correction section can explain the adjustable options and reasons for improving the color scheme within the color palette based on the brand's guidelines. For example, the correction section explains the adjustable options and reasons for improving the color scheme within the color palette based on the brand's guidelines. This allows for color improvements while maintaining the brand image.

[0082] The data collection unit can estimate the user's emotions and adjust the timing of visual transformations based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the timing of visual transformations to give the user time to relax. For example, if the user is relaxed, the data collection unit can speed up the timing of visual transformations to efficiently identify problem areas. For example, if the user is in a hurry, the data collection unit can optimize the timing of visual transformations to quickly identify problem areas. In this way, by adjusting the timing of visual transformations according to the user's emotions, the burden on the user can be reduced and problem areas can be efficiently identified. 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.

[0083] The data collection unit can apply different visual transformation algorithms depending on the type of color vision deficiency. For example, in the case of red-green color blindness, the data collection unit applies an algorithm that emphasizes the contrast between red and green. For example, in the case of blue-yellow color blindness, the data collection unit applies an algorithm that emphasizes the contrast between blue and yellow. For example, in the case of complete color blindness, the data collection unit applies an algorithm that equally emphasizes the contrast of all colors. By applying different visual transformation algorithms depending on the type of color vision deficiency, it becomes possible to perform visual transformations that correspond to each type of color vision deficiency.

[0084] The data collection unit can adjust the level of detail in the visual transformation based on the content of the material. For example, if the material is text-based, the data collection unit will emphasize the contrast between the text color and the background color. For example, if the material is graph-based, the data collection unit will emphasize the contrast between the graph color and the background color. For example, if the material is image-based, the data collection unit will emphasize the contrast between the image color and the background color. By adjusting the level of detail in the visual transformation based on the content of the material, it becomes possible to achieve optimal visual transformation according to the type of material.

[0085] The data collection unit can estimate the user's emotions and determine the priority of visual transformations based on the estimated emotions. For example, if the user is stressed, the data collection unit will set a lower priority for visual transformations to give the user time to relax. For example, if the user is relaxed, the data collection unit will set a higher priority for visual transformations to efficiently identify problem areas. For example, if the user is in a hurry, the data collection unit will optimize the priority of visual transformations to quickly identify problem areas. In this way, by determining the priority of visual transformations according to the user's emotions, the burden on the user can be reduced and problem areas can be efficiently identified. 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.

[0086] The data collection unit can customize the visual transformation method according to the type of material. For example, the data collection unit customizes the visual transformation method according to the type of material (presentation, report, web page, etc.). For example, in the case of a presentation, the data collection unit customizes the visual transformation method for each slide. For example, in the case of a report, the data collection unit customizes the visual transformation method for each chapter. For example, in the case of a web page, the data collection unit customizes the visual transformation method for each page. By customizing the visual transformation method according to the type of material, it becomes possible to achieve optimal visual transformation for each material.

[0087] The data collection unit can select the optimal visual transformation method by referring to the document creator's past visual transformation history. For example, the data collection unit proposes the optimal visual transformation method based on the visual transformation methods the document creator has used in the past. For example, the data collection unit selects the most effective visual transformation method from the document creator's past visual transformation history. For example, the data collection unit analyzes the document creator's past visual transformation history and selects the optimal visual transformation method. This allows the optimal visual transformation method to be selected by referring to the document creator's past visual transformation history.

[0088] The checking unit can estimate the user's emotions and adjust the checking criteria based on the estimated emotions. For example, if the user is stressed, the checking unit will loosen the checking criteria to give the user time to relax. For example, if the user is relaxed, the checking unit will tighten the checking criteria to efficiently identify problem areas. For example, if the user is in a hurry, the checking unit will optimize the checking criteria to quickly identify problem areas. In this way, by adjusting the checking criteria according to the user's emotions, the burden on the user can be reduced and problem areas can be efficiently identified. 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.

[0089] The checking unit can apply different checking algorithms depending on the type of color vision deficiency. For example, in the case of red-green color blindness, the checking unit applies a checking algorithm that emphasizes the contrast between red and green. For example, in the case of blue-yellow color blindness, the checking unit applies a checking algorithm that emphasizes the contrast between blue and yellow. For example, in the case of complete color blindness, the checking unit applies a checking algorithm that equally emphasizes the contrast of all colors. In this way, by applying different checking algorithms for each type of color vision deficiency, it becomes possible to perform checks that correspond to each type of color vision deficiency.

[0090] The checking function can adjust the level of detail based on the content of the document. For example, if the document is text-heavy, the checking function will emphasize the contrast between the text color and the background color. If the document is graph-heavy, the checking function will emphasize the contrast between the graph color and the background color. If the document is image-heavy, the checking function will emphasize the contrast between the image color and the background color. By adjusting the level of detail based on the content of the document, it becomes possible to perform optimal checking according to the type of document.

[0091] The checking unit can estimate the user's emotions and determine the priority of checks based on the estimated emotions. For example, if the user is stressed, the checking unit will set a lower priority for checks, giving the user time to relax. For example, if the user is relaxed, the checking unit will set a higher priority for checks, efficiently identifying problem areas. For example, if the user is in a hurry, the checking unit will optimize the check priorities to quickly identify problem areas. In this way, by determining the priority of checks according to the user's emotions, the burden on the user can be reduced and problem areas can be efficiently identified. 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.

[0092] The checking function can customize its checking method depending on the type of document. For example, the checking function customizes the checking method depending on the type of document (presentation, report, web page, etc.). For example, in the case of a presentation, the checking function customizes the checking method for each slide. For example, in the case of a report, the checking function customizes the checking method for each chapter. For example, in the case of a web page, the checking function customizes the checking method for each page. This allows for optimal checking of each document by customizing the checking method according to the type of document.

[0093] The checking unit can select the optimal checking method by referring to the document creator's past checking history. For example, the checking unit proposes the optimal checking method based on the checking methods the document creator has used in the past. For example, the checking unit selects the most effective checking method from the document creator's past checking history. For example, the checking unit analyzes the document creator's past checking history and selects the optimal checking method. This allows the optimal checking method to be selected by referring to the document creator's past checking history.

[0094] The alert unit can estimate the user's emotions and adjust how alerts are displayed based on those emotions. For example, if the user is stressed, the alert unit simplifies the display to reduce visual burden. If the user is relaxed, for example, the alert unit displays a detailed alert to clarify the problem area. If the user is in a hurry, for example, the alert unit displays a concise alert to quickly identify the problem area. By adjusting the alert display according to the user's emotions, the user's burden is reduced and problem areas can be identified efficiently. 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.

[0095] The alert function can adjust the level of detail displayed in the comments based on the content of the alert. For example, if the alert is important, the alert function will display detailed comments to clarify the problem. For example, if the alert is minor, the alert function will display concise comments to quickly identify the problem. For example, if the alert is of moderate importance, the alert function will display comments with appropriate level of detail to clarify the problem. In this way, the problem can be clearly identified by adjusting the level of detail displayed in the comments based on the content of the alert.

[0096] The alert function can adjust the timing of alerts based on the content of the document. For example, if the document is text-heavy, the alert function will display alerts in areas where the contrast between the text color and background color is low. If the document is graph-heavy, the alert function will display alerts in areas where the contrast between the graph color and background color is low. If the document is image-heavy, the alert function will display alerts in areas where the contrast between the image color and background color is low. By adjusting the timing of alerts based on the content of the document, it becomes possible to display alerts optimally for each document.

[0097] The alerting unit can estimate the user's emotions and determine the priority of alerts based on the estimated emotions. For example, if the user is stressed, the alerting unit will set a lower priority for the alert, giving the user time to relax. For example, if the user is relaxed, the alerting unit will set a higher priority for the alert, efficiently identifying the problem area. For example, if the user is in a hurry, the alerting unit will optimize the alert priority to quickly identify the problem area. In this way, by determining the priority of alerts according to the user's emotions, the burden on the user can be reduced and the problem area can be efficiently identified. 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.

[0098] The alert function allows you to customize how alerts are displayed depending on the type of document. For example, the alert function customizes how alerts are displayed depending on the type of document (presentation, report, web page, etc.). For example, in the case of a presentation, the alert function customizes how alerts are displayed for each slide. For example, in the case of a report, the alert function customizes how alerts are displayed for each chapter. For example, in the case of a web page, the alert function customizes how alerts are displayed for each page. By customizing how alerts are displayed according to the type of document, it becomes possible to display alerts in a way that is optimal for each document.

[0099] The alerting unit can select the optimal alerting method by referring to the document creator's past alert history. For example, the alerting unit proposes the optimal alerting method based on the alerting methods the document creator has used in the past. For example, the alerting unit selects the most effective alerting method from the document creator's past alert history. For example, the alerting unit analyzes the document creator's past alert history to select the optimal alerting method. This allows the optimal alerting method to be selected by referring to the document creator's past alert history.

[0100] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function will present simple and highly visible suggestions. If the user is relaxed, the suggestion function will present detailed suggestions to make the user understand the options more easily. If the user is in a hurry, the suggestion function will present concise suggestions for quick understanding. By adjusting the way suggestions are presented according to the user's emotions, the burden on the user is reduced, and problem areas can be efficiently identified. 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.

[0101] The proposal team can adjust the level of detail in their proposals based on the importance of the alternative colors. For example, for important alternative colors, the proposal team will provide a detailed explanation. For minor alternative colors, the proposal team will provide a concise proposal. For moderately important alternative colors, the proposal team will provide a proposal with a moderate level of detail. By adjusting the level of detail in proposals based on the importance of the alternative colors, it becomes possible to provide appropriate proposals for important alternative colors.

[0102] The suggestion function can apply different suggestion algorithms depending on the category of the document. For example, the suggestion function can apply different suggestion algorithms depending on the category of the document. For example, in the case of a presentation, the suggestion function can suggest the optimal alternative color for each slide. For example, in the case of a report, the suggestion function can suggest the optimal alternative color for each chapter. For example, in the case of a web page, the suggestion function can suggest the optimal alternative color for each page. This makes it possible to provide optimal suggestions for each document by applying different suggestion algorithms depending on the category of the document.

[0103] The suggestion function can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is stressed, the suggestion function will set a lower priority for suggestions, giving the user time to relax. For example, if the user is relaxed, the suggestion function will set a higher priority for suggestions, efficiently identifying problem areas. For example, if the user is in a hurry, the suggestion function will optimize the priority of suggestions to quickly identify problem areas. In this way, by determining the priority of suggestions according to the user's emotions, the burden on the user can be reduced and problem areas can be efficiently identified. 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.

[0104] The proposal department can customize its proposal methods depending on the type of document. For example, the proposal department customizes its proposal methods depending on the type of document (presentation, report, web page, etc.). For example, in the case of a presentation, the proposal department customizes the proposal method for each slide. For example, in the case of a report, the proposal department customizes the proposal method for each chapter. For example, in the case of a web page, the proposal department customizes the proposal method for each page. By customizing the proposal method according to the type of document, it becomes possible to provide the most suitable proposal for each document.

[0105] The proposal department can select the optimal proposal method by referring to the document creator's past proposal history. For example, the proposal department proposes the optimal proposal method based on the proposal methods the document creator has used in the past. For example, the proposal department selects the most effective proposal method from the document creator's past proposal history. For example, the proposal department analyzes the document creator's past proposal history to select the optimal proposal method. This allows the proposal department to select the optimal proposal method by referring to the document creator's past proposal history.

[0106] The correction unit can estimate the user's emotions and adjust the correction method based on the estimated emotions. For example, if the user is stressed, the correction unit provides a simple and highly visible correction method. For example, if the user is relaxed, the correction unit provides a detailed correction method to make the user understand the options more easily. For example, if the user is in a hurry, the correction unit provides a concise correction method that can be quickly understood. By adjusting the correction method according to the user's emotions, the burden on the user is reduced and problem areas can be efficiently identified. 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.

[0107] The correction unit can adjust how the difference between the original and new colors is visually displayed. For example, in the case of significant corrections, the correction unit visually displays detailed differences. For example, in the case of minor corrections, the correction unit visually displays concise differences. For example, in the case of moderate corrections, the correction unit visually displays differences with a moderate level of detail. This allows for clearer identification of the corrections by adjusting how the difference between the original and new colors is visually displayed.

[0108] The correction unit can adjust the level of detail of the correction based on the content of the document. For example, if the document is text-heavy, the correction unit will enhance the contrast between the text color and the background color. For example, if the document is graph-heavy, the correction unit will enhance the contrast between the graph color and the background color. For example, if the document is image-heavy, the correction unit will enhance the contrast between the image color and the background color. By adjusting the level of detail of the correction based on the content of the document, it becomes possible to perform the optimal correction for each document.

[0109] The correction unit can estimate the user's emotions and determine the priority of corrections based on the estimated emotions. For example, if the user is stressed, the correction unit will set a low priority for corrections, giving the user time to relax. For example, if the user is relaxed, the correction unit will set a high priority for corrections, efficiently identifying problem areas. For example, if the user is in a hurry, the correction unit will optimize the priority of corrections to quickly identify problem areas. In this way, by determining the priority of corrections according to the user's emotions, the burden on the user can be reduced and problem areas can be efficiently identified. 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.

[0110] The correction unit can customize the correction method according to the type of document. For example, the correction unit customizes the correction method according to the type of document (presentation, report, web page, etc.). For example, in the case of a presentation, the correction unit customizes the correction method for each slide. For example, in the case of a report, the correction unit customizes the correction method for each chapter. For example, in the case of a web page, the correction unit customizes the correction method for each page. This allows for optimal correction for each document by customizing the correction method according to the type of document.

[0111] The correction unit can select the optimal correction method by referring to the document creator's past correction history. For example, the correction unit proposes the optimal correction method based on correction methods previously used by the document creator. For example, the correction unit selects the most effective correction method from the document creator's past correction history. For example, the correction unit analyzes the document creator's past correction history and selects the optimal correction method. This allows the optimal correction method to be selected by referring to the document creator's past correction history.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The data collection unit can adjust the level of detail in the visual transformation based on the content of the document. For example, if the document is text-heavy, it emphasizes the contrast between the text color and background color. If the document is graph-heavy, it emphasizes the contrast between the graph color and background color. If the document is image-heavy, it emphasizes the contrast between the image color and background color. This enables optimal visual transformation according to the type of document. The data collection unit can also select the optimal visual transformation method by referring to the document creator's past visual transformation history. For example, it can suggest the optimal visual transformation method based on the visual transformation methods the document creator has used in the past. It can select the most effective visual transformation method from the document creator's past visual transformation history. This allows for the selection of the optimal visual transformation method by referring to the document creator's past visual transformation history.

[0114] The checking function can adjust the level of detail of the check based on the content of the document. For example, if the document is text-heavy, it will emphasize the contrast between the text color and background color. If the document is graph-heavy, it will emphasize the contrast between the graph color and background color. If the document is image-heavy, it will emphasize the contrast between the image color and background color. This allows for optimal checking according to the type of document. The checking function can also select the optimal checking method by referring to the document creator's past checking history. For example, it can suggest the optimal checking method based on the checking methods the document creator has used in the past. It can select the most effective checking method from the document creator's past checking history. This allows for the selection of the optimal checking method by referring to the document creator's past checking history.

[0115] The alert function can adjust the timing of alerts based on the content of the document. For example, if the document is text-heavy, alerts will be displayed in areas with low contrast between the text color and background color. If the document is graph-heavy, alerts will be displayed in areas with low contrast between the graph color and background color. If the document is image-heavy, alerts will be displayed in areas with low contrast between the image color and background color. By adjusting the timing of alerts based on the content of the document, it is possible to display alerts optimally for each document. The alert function can also select the optimal alert method by referring to the document creator's past alert history. For example, it can suggest the optimal alert method based on the alert methods the document creator has used in the past. It can select the most effective alert method from the document creator's past alert history. This allows for the selection of the optimal alert method by referring to the document creator's past alert history.

[0116] The proposal function can apply different proposal algorithms depending on the category of the document. For example, for a presentation, it can suggest the best alternative color for each slide. For a report, it can suggest the best alternative color for each chapter. For a web page, it can suggest the best alternative color for each page. This allows for optimal proposals for each document by applying different proposal algorithms depending on the document category. The proposal function can also select the best proposal method by referring to the document creator's past proposal history. For example, it can suggest the best proposal method based on proposal methods the document creator has used in the past. It can select the most effective proposal method from the document creator's past proposal history. This allows for the selection of the best proposal method by referring to the document creator's past proposal history.

[0117] The correction unit can adjust the level of detail of the correction based on the content of the document. For example, if the document is text-heavy, it will perform correction to enhance the contrast between the text color and background color. If the document is graph-heavy, it will perform correction to enhance the contrast between the graph color and background color. If the document is image-heavy, it will perform correction to enhance the contrast between the image color and background color. By adjusting the level of detail of the correction based on the content of the document, it is possible to perform corrections that are optimal for each document. In addition, the correction unit can also select the optimal correction method by referring to the past correction history of the document creator. For example, it can suggest the optimal correction method based on the correction methods the document creator has used in the past. It can select the most effective correction method from the document creator's past correction history. This allows for the selection of the optimal correction method by referring to the document creator's past correction history.

[0118] The data collection unit can estimate the user's emotions and adjust the timing of visual transformations based on the estimated emotions. For example, if the user is stressed, the timing of visual transformations can be delayed to give the user time to relax. If the user is relaxed, the timing of visual transformations can be sped up to efficiently identify problem areas. If the user is in a hurry, the timing of visual transformations can be optimized to quickly identify problem areas. In this way, by adjusting the timing of visual transformations according to the user's emotions, the burden on the user can be reduced and problem areas can be efficiently identified. 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.

[0119] The checking unit can estimate the user's emotions and adjust the checking criteria based on the estimated emotions. For example, if the user is stressed, the checking criteria can be relaxed to give the user time to relax. If the user is relaxed, the checking criteria can be tightened to efficiently identify problem areas. If the user is in a hurry, the checking criteria can be optimized to quickly identify problem areas. In this way, by adjusting the checking criteria according to the user's emotions, the burden on the user can be reduced and problem areas can be identified efficiently. 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.

[0120] The alert function can estimate the user's emotions and adjust how alerts are displayed based on those emotions. For example, if the user is stressed, the alert display can be simplified to reduce visual burden. If the user is relaxed, a detailed alert can be displayed to clarify the problem. If the user is in a hurry, a concise alert can be displayed to quickly identify the problem. By adjusting the alert display according to the user's emotions, the burden on the user can be reduced and problem areas can be identified efficiently. 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.

[0121] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, it will provide simple and highly visible suggestions. If the user is relaxed, it will provide detailed suggestions to make the user understand the options more easily. If the user is in a hurry, it will provide concise suggestions to ensure quick understanding. By adjusting the way suggestions are presented according to the user's emotions, the burden on the user is reduced, and problem areas can be identified efficiently. 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.

[0122] The correction unit can estimate the user's emotions and adjust the correction method based on the estimated emotions. For example, if the user is stressed, it provides a simple and highly visible correction method. If the user is relaxed, it provides a detailed correction method to make the user understand the options more easily. If the user is in a hurry, it provides a concise correction method that can be quickly understood. By adjusting the correction method according to the user's emotions, the burden on the user is reduced and problem areas can be efficiently identified. 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.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection team visually transforms the materials from the perspective of people with color vision deficiency and identifies problem areas. For example, they use a universal color mask to visually transform the materials from the perspective of people with color vision deficiency and identify problem areas. Step 2: The checking unit performs a visual type-specific check based on the information collected by the collection unit, corresponding to multiple color vision types. For example, it can handle multiple color vision types such as red-green color blindness, blue-yellow color blindness, and complete color blindness. Step 3: The alert section automatically displays alerts and comments for areas with low visibility identified by the check section. For example, it automatically displays alerts for areas with low visibility and comments such as "This combination has low visibility." Step 4: The proposal team proposes alternative colors or consistent color adjustments for the areas pointed out by the alert team. For example, they might propose alternative colors or consistent color adjustments based on universal color theory. Step 5: The correction unit automatically corrects the alternative colors and adjustments suggested by the suggestion unit. For example, it easily corrects the colors using the automatic correction function and shows the difference between the original and new colors.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, check unit, alert unit, suggestion unit, and correction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and display 40A of the smart device 14 to visually transform the material and identify problem areas. The check unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and performs visual type-specific checks corresponding to multiple color vision types. The alert unit is implemented, for example, by the control unit 46A of the smart device 14 and automatically displays alerts and comments for areas with low visibility. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes alternative colors based on universal color or color adjustments that maintain consistency. The correction unit is implemented, for example, by the control unit 46A of the smart device 14 and easily corrects colors with an automatic correction function and shows the difference between the original color and the new color. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, check unit, alert unit, suggestion unit, and correction unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and display 40A of the smart glasses 214 to visually transform the material and identify problem areas. The check unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and performs visual type-specific checks corresponding to multiple color vision types. The alert unit is implemented, for example, by the control unit 46A of the smart glasses 214 and automatically displays alerts and comments for areas with low visibility. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and suggests alternative colors based on universal color or color adjustments to maintain consistency. The correction unit is implemented, for example, by the control unit 46A of the smart glasses 214 and easily corrects colors with an automatic correction function and shows the difference between the original color and the new color. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, check unit, alert unit, suggestion unit, and correction unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and display 40A of the headset terminal 314 to visually convert the material and identify problem areas. The check unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and performs visual type-specific checks corresponding to multiple color vision types. The alert unit is implemented, for example, by the control unit 46A of the headset terminal 314, and automatically displays alerts and comments for areas with low visibility. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes alternative colors based on universal color or color adjustments to maintain consistency. The correction unit is implemented, for example, by the control unit 46A of the headset terminal 314, and easily corrects colors with an automatic correction function, showing the difference between the original color and the new color. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, check unit, alert unit, suggestion unit, and correction unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and display 40A of the robot 414 to visually transform the material and identify problem areas. The check unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and performs visual type-specific checks corresponding to multiple color vision types. The alert unit is implemented, for example, by the control unit 46A of the robot 414, and automatically displays alerts and comments for areas with low visibility. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and suggests alternative colors based on universal color or color adjustments to maintain consistency. The correction unit is implemented, for example, by the control unit 46A of the robot 414, and easily corrects colors with an automatic correction function, showing the difference between the original color and the new color. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A collection department that visually transforms materials from the perspective of people with color vision deficiency and identifies problem areas, Based on the information collected by the aforementioned collection unit, a checking unit performs a visual type-specific check corresponding to multiple color vision types. The aforementioned checking unit automatically displays an alert and a comment for areas with low visibility, and the alert unit displays a comment. The proposal unit suggests alternative colors or color adjustments to maintain consistency for the areas pointed out by the aforementioned alert unit, The system includes a correction unit that automatically corrects the alternative colors and adjustments proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Using a universal color mask, we visually transform materials from the perspective of people with color vision deficiencies to identify problem areas. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned checking unit is Supports multiple color vision types, including red-green color blindness, blue-yellow color blindness, and complete color blindness. The system described in Appendix 1, characterized by the features described herein. (Note 4) The alert unit is, It automatically displays alerts for areas with low visibility and shows comments such as, "This combination has low visibility." The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose alternative colors and color adjustments that maintain consistency, based on universal color principles. The system described in Appendix 1, characterized by the features described herein. (Note 6) The correction unit, The automatic correction function easily corrects colors and shows the difference between the original and new colors. The system described in Appendix 1, characterized by the features described herein. (Note 7) The correction unit, This document explains the adjustable options within the color palette based on the brand's guidelines and the reasons for improving the color scheme. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of visual transformations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Depending on the type of color blindness, different visual conversion algorithms are applied. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Adjust the level of detail in the visual transformation based on the content of the document. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and determines the priority of visual transformations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Customize the visual transformation method depending on the type of document. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Refer to the document creator's past visualization history to select the most suitable visualization method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned checking unit is The system estimates the user's emotions and adjusts the check criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned checking unit is A different checking algorithm is applied for each type of color vision deficiency. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned checking unit is Adjust the level of detail in the check based on the content of the document. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned checking unit is The system estimates the user's emotions and prioritizes checks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned checking unit is Customize the checking method depending on the type of document. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned checking unit is Refer to the document creator's past review history to select the most suitable review method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The alert unit is, It estimates the user's emotions and adjusts how alerts are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The alert unit is, Adjust the level of detail displayed for comments based on the alert content. The system described in Appendix 1, characterized by the features described herein. (Note 22) The alert unit is, Based on the content of the document, adjust the timing of the alert display. The system described in Appendix 1, characterized by the features described herein. (Note 23) The alert unit is, It estimates the user's emotions and determines the priority of alerts based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The alert unit is, Customize how alerts are displayed depending on the type of document. The system described in Appendix 1, characterized by the features described herein. (Note 25) The alert unit is, Refer to the document creator's past alert history to select the most suitable alerting method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, Adjust the level of detail in the suggestions based on the importance of the alternative colors. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, Apply different proposed algorithms depending on the category of the document. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, Customize the proposal method depending on the type of document. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, Refer to the document creator's past proposal history to select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The correction unit, The system estimates the user's emotions and adjusts the correction method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The correction unit, Adjust the way the difference between the original color and the new color is visually displayed. The system described in Appendix 1, characterized by the features described herein. (Note 34) The correction unit, Adjust the level of detail of the correction based on the content of the document. The system described in Appendix 1, characterized by the features described herein. (Note 35) The correction unit, The system estimates the user's emotions and determines the priority of corrections based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The correction unit, Customize the correction method depending on the type of document. The system described in Appendix 1, characterized by the features described herein. (Note 37) The correction unit, Refer to the document creator's past correction history to select the most suitable correction method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that visually transforms materials from the perspective of people with color vision deficiency and identifies problem areas, Based on the information collected by the aforementioned collection unit, a checking unit performs a visual type-specific check corresponding to multiple color vision types. The aforementioned checking unit automatically displays an alert and a comment for areas with low visibility, and the alert unit displays a comment. The proposal unit suggests alternative colors or color adjustments to maintain consistency for the areas pointed out by the aforementioned alert unit, The system includes a correction unit that automatically corrects the alternative colors and adjustments proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Using a universal color mask, we visually transform materials from the perspective of people with color vision deficiencies to identify problem areas. The system according to feature 1.

3. The aforementioned checking unit is Supports multiple color vision types, including red-green color blindness, blue-yellow color blindness, and complete color blindness. The system according to feature 1.

4. The aforementioned proposal section is, We propose alternative colors and color adjustments that maintain consistency, based on universal color principles. The system according to feature 1.

5. The correction unit, The automatic correction function easily corrects colors and shows the difference between the original and new colors. The system according to feature 1.

6. The correction unit, This document explains the adjustable options within the color palette based on the brand's guidelines and the reasons for improving the color scheme. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of visual transformations based on those emotions. The system according to feature 1.

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