System

Abstract

An object of the system according to the embodiment is to realize consistent color display even under different display environments.SOLUTION: A system includes a color information analysis unit, a display environment information collection unit, and a color display adjustment unit. The color information analysis unit analyzes color information of the image. The display environment information collection unit collects information on a display environment. A color display adjustment part adjusts color display on the basis of the color information analyzed by the color information analysis part and the information of the display environment collected by the display environment information collection part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of inconsistent color display of images under different display environments.

[0005] The system according to the embodiment aims to realize consistent color display even under different display environments. [Means for solving the problem]

[0006] The system according to the embodiment includes a color information analysis unit, a display environment information collection unit, and a color display adjustment unit. The color information analysis unit analyzes color information of an image. The display environment information collection unit collects information about the display environment. The color display adjustment unit adjusts color display based on the color information analyzed by the color information analysis unit and the information about the display environment collected by the display environment information collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can achieve consistent color display even under different display environments. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The image display assistance system according to an embodiment of the present invention utilizes AI to assist image display and standardize color display in different environments, thereby enabling the image display assistance system to achieve consistent color display across different devices and lighting conditions.

[0029] An image display assistance system according to an embodiment includes a color information analysis unit, a display environment information collection unit, and a color display adjustment unit. The color information analysis unit analyzes color information of an image. For example, the color information analysis unit uses a generation AI to analyze information such as the hue, saturation, and brightness of each pixel in the image. The color information analysis unit can also use the generation AI to analyze the percentage of a specific color present in the image. The color information analysis unit can also use the generation AI to analyze not only color information in the image but also texture and pattern information. For example, the generation AI can recognize the texture of fabric or wood grain patterns in the image and adjust the color display based on that information. The display environment information collection unit collects information about the display environment. For example, the display environment information collection unit uses the generation AI to collect information about the device's display characteristics (e.g., resolution, color gamut, brightness) and ambient lighting conditions (e.g., light intensity, color temperature). The display environment information collection unit can also use the generation AI to adjust the color display taking into account not only the device's display characteristics but also the user's visual characteristics (e.g., color blindness). The display environment information collection unit can use the generation AI to collect not only ambient lighting conditions but also time of day and weather information, and display colors accordingly. The color display adjustment unit adjusts the color display based on the color information analyzed by the color information analysis unit and the display environment information collected by the display environment information collection unit. For example, the color display adjustment unit can improve visibility by increasing image brightness in dark environments and adjusting saturation in bright environments. The color display adjustment unit can also adjust the color display to maintain consistency across different devices. The color display adjustment unit can also use the generation AI to estimate a user's emotions using an emotion estimation function and display colors according to those emotions. For example, the emotion estimation function can emphasize warm colors when a user is relaxed. This allows the image display assistance system according to the embodiment to achieve consistent color display even in different environments. For example, professional designers can work on their work on different devices without worrying about color differences. Furthermore, when displaying product images on an online shopping site, users can view the product in the same colors regardless of the device they are viewing.This is expected to improve user experience and operational efficiency.

[0030] The color information analysis unit analyzes the proportion of specific colors present in an image and can adjust the color display based on that information. For example, when the generation AI analyzes the color information in an image, the color information analysis unit simultaneously analyzes texture and pattern information. For example, it recognizes the texture of fabric or wood grain patterns in the image and adjusts the color display based on that. The color information analysis unit also analyzes texture and pattern information along with the color information in the image and reflects this in the color display adjustments. For example, it analyzes ripples on the surface of water or the patterns of sand particles in the image to achieve a natural color display. The color information analysis unit also analyzes texture and pattern information in addition to the color information in the image and reflects this in the color display adjustments. For example, it recognizes the pattern of bricks or the shape of leaves in the image and adjusts the color display based on that. This allows for detailed analysis of the color information in the image and achieves optimal color display.

[0031] The display environment information collection unit can grasp the display characteristics of the device and adjust the color display to provide the optimal color display within that range. For example, the generation AI in the display environment information collection unit grasps the display characteristics of the device and adjusts the color display to provide the optimal color display within that range. For example, the generation AI collects information such as the device's resolution, color gamut, and brightness, and adjusts the color display based on that information. The display environment information collection unit also adjusts the color display by having the generation AI take into account the display characteristics of the device and the user's visual characteristics. For example, it provides easy-to-see color display for users with color vision deficiencies. The display environment information collection unit also adjusts the color display by having the generation AI take into account the display characteristics of the device and the user's visual characteristics. For example, it provides color display with enhanced contrast for users with visual impairments. The display environment information collection unit also adjusts the color display by having the generation AI take into account the display characteristics of the device and the user's visual characteristics. For example, it adjusts the character size and color for users with presbyopia. This achieves the optimal color display according to the device's characteristics.

[0032] The color display adjustment unit can improve visibility by increasing the brightness of an image in a dark environment and adjusting the saturation in a bright environment. The color display adjustment unit, for example, uses an emotion estimation function to estimate the emotion of a user viewing an image and displays a color according to the emotion. For example, if the user is relaxed, warm colors are emphasized. The color display adjustment unit also uses the emotion estimation function to estimate the emotion of a user viewing an image and displays a color according to the emotion. For example, if the user is excited, vivid colors are emphasized. The color display adjustment unit also uses the emotion estimation function to estimate the emotion of a user viewing an image and displays a color according to the emotion. For example, if the user is sad, muted colors are emphasized. This achieves improved visibility according to the environment.

[0033] The color display adjustment unit can make adjustments to maintain consistency in color display across different devices. For example, when the generation AI analyzes the color information of an image, the color display adjustment unit simultaneously analyzes audio data and text data, and adjusts the color display based on multimodal information. For example, it analyzes explanatory audio and captions related to the image and reflects them in the color display. In addition, when the generation AI analyzes the color information of an image, the color display adjustment unit simultaneously analyzes audio data and text data, and adjusts the color display based on multimodal information. For example, it analyzes audio and text related to scenes in the image and reflects them in the color display. In addition, when the generation AI analyzes the color information of an image, the color display adjustment unit simultaneously analyzes audio data and text data, and adjusts the color display based on multimodal information. For example, it analyzes music and narration related to the image and reflects them in the color display. This achieves consistent color display across different devices.

[0034] The color display adjustment unit can adjust the color of an image according to the ambient lighting conditions. For example, the generation AI in the color display adjustment unit learns the color sensibilities of different cultural spheres and regions and, based on that, displays the optimal color for each region. For example, people in Asia tend to prefer vivid colors, so the color display is adjusted to match that. The generation AI also learns the color sensibilities of different cultural spheres and regions and, based on that, displays the optimal color for each region. For example, people in Europe tend to prefer subdued colors, so the color display is adjusted to match that. The generation AI also learns the color sensibilities of different cultural spheres and regions and, based on that, displays the optimal color for each region. For example, people in South America tend to prefer bright colors, so the color display is adjusted to match that. This achieves the optimal color display according to the lighting conditions.

[0035] The color information analysis unit analyzes not only the color information in an image, but also the texture and pattern information, and reflects this in color display adjustments. For example, when the generation AI analyzes the color information in an image, the color information analysis unit simultaneously analyzes the texture and pattern information. For example, it recognizes the texture of fabric or wood grain patterns in the image and adjusts the color display based on that. The color information analysis unit also analyzes the texture and pattern information along with the color information in the image, and reflects this in color display adjustments. For example, it analyzes the ripples on the water surface or the patterns of sand particles in the image to achieve natural color display. The color information analysis unit also analyzes the texture and pattern information in the image, and reflects this in color display adjustments. For example, it recognizes the brick pattern or the shape of leaves in the image and adjusts the color display based on that. This achieves color display that takes the texture and pattern of the image into account.

[0036] The color information analysis unit can recognize specific objects within an image and adjust the color display to optimally suit each object. For example, the color information analysis unit allows the generation AI to recognize specific objects within an image and display the optimal color for each object. For example, to display a person's skin color naturally, only the skin part is adjusted. The color information analysis unit also allows the generation AI to recognize specific objects within an image and display the optimal color for each object. For example, it recognizes the sky and ocean parts within a landscape image and optimizes the colors of each. The color information analysis unit also allows the generation AI to recognize specific objects within an image and display the optimal color for each object. For example, it recognizes the product part within a product image and accurately displays the product color. This allows the optimal color display for specific objects.

[0037] When analyzing the color information of an image, the color information analysis unit simultaneously analyzes audio data and text data, and can adjust the color display based on multimodal information. For example, when the generation AI analyzes the color information of an image, the color information analysis unit simultaneously analyzes audio data and text data, and adjusts the color display based on multimodal information. For example, it analyzes explanatory audio and captions related to the image and reflects this in the color display. In addition, when the generation AI analyzes the color information of an image, the color information analysis unit simultaneously analyzes audio data and text data, and adjusts the color display based on multimodal information. For example, it analyzes audio and text related to scenes in the image and reflects this in the color display. In addition, when the generation AI analyzes the color information of an image, the color information analysis unit simultaneously analyzes audio data and text data, and adjusts the color display based on multimodal information. For example, it analyzes music and narration related to the image and reflects this in the color display. This enables color display based on multimodal information.

[0038] The color information analysis unit learns the color sensibilities of different cultural spheres and regions, and based on that, can display colors that are optimal for each region. For example, the generation AI in the color information analysis unit learns the color sensibilities of different cultural spheres and regions, and based on that, displays colors that are optimal for each region. For example, people in Asia tend to prefer vivid colors, so the color display is tailored to that. The generation AI also learns the color sensibilities of different cultural spheres and regions, and based on that, displays colors that are optimal for each region. For example, people in Europe tend to prefer subdued colors, so the color display is tailored to that. The generation AI also learns the color sensibilities of different cultural spheres and regions, and based on that, displays colors that are optimal for each region. For example, people in South America tend to prefer bright colors, so the color display is tailored to that. This allows for optimal color display for each region.

[0039] The display environment information collecting unit can adjust color display taking into account not only the display characteristics of the device but also the visual characteristics of the user. In the display environment information collecting unit, for example, the generation AI adjusts color display taking into account the display characteristics of the device and the visual characteristics of the user. For example, it provides color display that is easy to see for users with color vision deficiency. In addition, the display environment information collecting unit adjusts color display taking into account the display characteristics of the device and the visual characteristics of the user. For example, it provides color display with enhanced contrast for users with visual impairments. In addition, the display environment information collecting unit adjusts color display taking into account the display characteristics of the device and the visual characteristics of the user. For example, it adjusts the size and color of characters for users with presbyopia. In this way, it realizes color display that suits the user's visual characteristics.

[0040] The display environment information collection unit collects not only ambient lighting conditions but also time of day and weather information, and can display colors accordingly. In the display environment information collection unit, for example, the generation AI collects ambient lighting conditions, time of day, and weather information, and displays colors accordingly. For example, bright colors are emphasized during sunny daytime weather. In addition, the display environment information collection unit collects ambient lighting conditions, time of day, and weather information, and displays colors accordingly. For example, muted colors are emphasized during cloudy nighttime weather. In addition, the display environment information collection unit collects ambient lighting conditions, time of day, and weather information, and displays colors accordingly. For example, warm colors are emphasized in the dim light of the evening. This allows for optimal color display according to the time of day and weather.

[0041] The display environment information collecting unit can take into consideration the user's location information and activity status when collecting information about the display environment, and display colors accordingly. For example, when the generation AI collects information about the display environment, the display environment information collecting unit takes into consideration the user's location information and activity status, and displays colors accordingly. For example, it emphasizes bright colors when the user is active outdoors. Furthermore, when the generation AI collects information about the display environment, the display environment information collecting unit takes into consideration the user's location information and activity status, and displays colors accordingly. For example, it emphasizes warm colors when the user is relaxing indoors. Furthermore, when the generation AI collects information about the display environment, the display environment information collecting unit takes into consideration the user's location information and activity status, and displays colors accordingly. For example, it emphasizes vivid colors when the user is exercising. This enables color display according to the user's location information and activity status.

[0042] The display environment information collecting unit can share information between different devices and achieve consistent color display across multiple devices. The display environment information collecting unit, for example, shares information between devices with different generation AIs and achieves consistent color display across multiple devices. For example, when the same image is displayed on a smartphone and a tablet, the color display is unified. The display environment information collecting unit also shares information between devices with different generation AIs and achieves consistent color display across multiple devices. For example, when the same image is displayed on a PC and a TV, the color display is unified. The display environment information collecting unit also shares information between devices with different generation AIs and achieves consistent color display across multiple devices. For example, when the same image is displayed on a smartwatch and a smartphone, the color display is unified. This achieves consistent color display across multiple devices.

[0043] The color display adjustment unit can perform optimal color display according to the content of the image when standardizing color display. For example, when the generation AI standardizes color display, the color display adjustment unit performs optimal color display according to the content of the image. For example, it emphasizes natural colors in landscape images and displays natural skin colors in portrait images. Furthermore, when the generation AI standardizes color display, the color display adjustment unit performs optimal color display according to the content of the image. For example, in product images, it accurately displays the product color and adjusts the background color. Furthermore, when the generation AI standardizes color display, the color display adjustment unit performs optimal color display according to the content of the image. For example, in images of artwork, it faithfully reproduces the colors of the artwork. This achieves optimal color display according to the content of the image.

[0044] The color display adjustment unit can learn the user's past browsing history and preferences when standardizing color display, and adjust the color display based on that. For example, when the generation AI standardizes color display, the color display adjustment unit learns the user's past browsing history and preferences and adjusts the color display based on that. For example, it prioritizes displaying colors preferred by the user. Furthermore, when the generation AI standardizes color display, the color display adjustment unit learns the user's past browsing history and preferences and adjusts the color display based on that. For example, it refers to the colors of content frequently viewed by the user. Furthermore, when the generation AI standardizes color display, the color display adjustment unit learns the user's past browsing history and preferences and adjusts the color display based on that. For example, it reflects the user's preferred color tone and brightness. This allows for color display that meets the user's preferences.

[0045] The color display adjustment unit can provide optimal color display for different industries and uses when standardizing color display. For example, when the generation AI standardizes color display, the color display adjustment unit provides optimal color display for different industries and uses. For example, in the medical field, it provides color display that emphasizes visibility. Furthermore, when the generation AI standardizes color display, the color display adjustment unit provides optimal color display for different industries and uses. For example, in the education field, it provides color display that enhances learning effectiveness. Furthermore, when the generation AI standardizes color display, the color display adjustment unit provides optimal color display for different industries and uses. For example, in the entertainment field, it provides color display that enhances visual appeal. In this way, optimal color display for different industries and uses is realized.

[0046] The color display adjustment unit can automatically generate a profile for maintaining consistency in color display across different devices when standardizing color display. For example, when the generation AI standardizes color display, the color display adjustment unit automatically generates a profile for maintaining consistency in color display across different devices. For example, when the same image is displayed on a smartphone and a PC, the color display is unified. The color display adjustment unit also automatically generates a profile for maintaining consistency in color display across different devices when the generation AI standardizes color display. For example, when the same image is displayed on a tablet and a television, the color display is unified. The color display adjustment unit also automatically generates a profile for maintaining consistency in color display across different devices when the generation AI standardizes color display. For example, when the same image is displayed on a smartwatch and a smartphone, the color display is unified. This achieves consistent color display across different devices.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The image display assistance system may further include a voice recognition unit. The voice recognition unit can analyze a user's voice commands and adjust the image display. For example, if the user says "make it brighter," the voice recognition unit analyzes the command and sends an instruction to the color display adjustment unit to increase the brightness of the image. If the user says "make it warmer," the voice recognition unit analyzes the command and sends an instruction to the color display adjustment unit to emphasize warm colors. If the user says "set it to night mode," the voice recognition unit analyzes the command and sends an instruction to the color display adjustment unit to display colors suitable for nighttime use. This allows the user to intuitively adjust the image display using voice commands.

[0049] The image display assistance system may further include a gesture recognition unit. The gesture recognition unit can analyze the user's hand movements and adjust the image display. For example, when the user gestures to raise their hand, the gesture recognition unit analyzes the movement and sends an instruction to the color display adjustment unit to increase the brightness of the image. When the user gestures to wave their hand from side to side, the gesture recognition unit analyzes the movement and sends an instruction to the color display adjustment unit to change the color tone. When the user gestures to move their hand back and forth, the gesture recognition unit analyzes the movement and sends an instruction to the color display adjustment unit to adjust the saturation of the image. This allows the user to intuitively adjust the image display using gestures.

[0050] The image display assistance system can further include a content recognition unit. The content recognition unit can analyze the content of the image being displayed and display colors according to that content. For example, in the case of a landscape image, natural colors are emphasized, and in the case of a portrait image, skin tones are displayed naturally. In addition, in the case of a product image, the color of the product is accurately displayed and the color of the background is adjusted. Furthermore, in the case of an image of an artwork, the colors of the artwork can be faithfully reproduced. This makes it possible to realize optimal color display according to the content of the image.

[0051] The image display assistance system can further include a learning unit that learns the user's past browsing history. The learning unit records what images the user has viewed in the past and can adjust the color display based on that information. For example, it can prioritize the display of colors that the user has viewed in the past. It can also refer to the colors of content that the user frequently views. It can also reflect the user's preferred color tone and brightness. This makes it possible to realize color display that suits the user's preferences.

[0052] The image display assistance system can further include an industry adaptation unit that performs color display according to different industries and applications. The industry adaptation unit can perform optimal color display according to different industries and applications, such as the medical field, education field, and entertainment field. For example, in the medical field, color display that emphasizes visibility can be performed, while in the education field, color display that enhances learning effectiveness can be performed. Furthermore, in the entertainment field, color display that enhances visual appeal can be performed. This makes it possible to achieve optimal color display according to different industries and applications.

[0053] The image display assistance system may further include a device linking unit that shares information between different devices. The device linking unit shares information between different devices, such as smartphones, tablets, PCs, and televisions, and can achieve consistent color display across multiple devices. For example, when the same image is displayed on a smartphone and a tablet, the color display is unified. Also, when the same image is displayed on a PC and a television, the color display can be unified. Furthermore, when the same image is displayed on a smartwatch and a smartphone, the color display can be unified. This allows consistent color display across multiple devices.

[0054] The image display assistance system can further include a location information recognition unit that takes into account the user's location information and activity status. The location information recognition unit can analyze the user's location information and activity status and display colors accordingly. For example, bright colors can be emphasized when the user is active outdoors, and warm colors can be emphasized when the user is relaxing indoors. Also, vivid colors can be emphasized when the user is exercising. This makes it possible to display colors according to the user's location information and activity status.

[0055] The image display assistance system can further include a cultural adaptation unit that learns the color sensibilities of different cultural spheres and regions. The cultural adaptation unit can learn the color sensibilities of different cultural spheres and regions and, based on that, display colors that are optimal for each region. For example, since people in Asia tend to prefer vivid colors, the color adaptation unit can display colors that match that. Meanwhile, people in Europe tend to prefer subdued colors, the color adaptation unit can display colors that match that. Furthermore, people in South America tend to prefer bright colors, so the color adaptation unit can display colors that match that. This makes it possible to achieve optimal color display for each region.

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

[0057] Step 1: The color information analysis unit analyzes the color information of the image. For example, the color information analysis unit uses the generation AI to analyze information such as hue, saturation, and brightness of each pixel in the image. The color information analysis unit can also use the generation AI to analyze the percentage of a specific color present in the image. Furthermore, the color information analysis unit can use the generation AI to analyze not only the color information in the image, but also the texture and pattern information. For example, the generation AI can recognize the texture of fabric or wood grain patterns in the image and adjust the color display based on that. Step 2: The display environment information collection unit collects information about the display environment. For example, the display environment information collection unit uses the generation AI to collect the device's display characteristics (resolution, color gamut, brightness, etc.) and the surrounding lighting conditions (light intensity, color temperature, etc.). The display environment information collection unit can also use the generation AI to adjust the color display taking into account not only the device's display characteristics but also the user's visual characteristics (e.g., color vision deficiency). Furthermore, the display environment information collection unit can use the generation AI to collect not only the surrounding lighting conditions but also time of day and weather information, and display colors accordingly. Step 3: The color display adjustment unit adjusts the color display based on the color information analyzed by the color information analysis unit and the display environment information collected by the display environment information collection unit. For example, the color display adjustment unit improves visibility by increasing the brightness of the image in a dark environment and adjusting the saturation in a bright environment. The color display adjustment unit can also make adjustments to maintain consistency in color display across different devices. Furthermore, the color display adjustment unit can use the generative AI to estimate the user's emotions using an emotion estimation function and display colors according to those emotions. For example, if the user is relaxed, the emotion estimation function can be used to emphasize warm colors.

[0058] (Example 2) The image display assistance system according to an embodiment of the present invention utilizes AI to assist image display and standardize color display in different environments, thereby enabling the image display assistance system to achieve consistent color display across different devices and lighting conditions.

[0059] An image display assistance system according to an embodiment includes a color information analysis unit, a display environment information collection unit, and a color display adjustment unit. The color information analysis unit analyzes color information of an image. For example, the color information analysis unit uses a generation AI to analyze information such as the hue, saturation, and brightness of each pixel in the image. The color information analysis unit can also use the generation AI to analyze the percentage of a specific color present in the image. The color information analysis unit can also use the generation AI to analyze not only color information in the image but also texture and pattern information. For example, the generation AI can recognize the texture of fabric or wood grain patterns in the image and adjust the color display based on that information. The display environment information collection unit collects information about the display environment. For example, the display environment information collection unit uses the generation AI to collect information about the device's display characteristics (e.g., resolution, color gamut, brightness) and ambient lighting conditions (e.g., light intensity, color temperature). The display environment information collection unit can also use the generation AI to adjust the color display taking into account not only the device's display characteristics but also the user's visual characteristics (e.g., color blindness). The display environment information collection unit can use the generation AI to collect not only ambient lighting conditions but also time of day and weather information, and display colors accordingly. The color display adjustment unit adjusts the color display based on the color information analyzed by the color information analysis unit and the display environment information collected by the display environment information collection unit. For example, the color display adjustment unit can improve visibility by increasing image brightness in dark environments and adjusting saturation in bright environments. The color display adjustment unit can also adjust the color display to maintain consistency across different devices. The color display adjustment unit can also use the generation AI to estimate a user's emotions using an emotion estimation function and display colors according to those emotions. For example, the emotion estimation function can emphasize warm colors when a user is relaxed. This allows the image display assistance system according to the embodiment to achieve consistent color display even in different environments. For example, professional designers can work on their work on different devices without worrying about color differences. Furthermore, when displaying product images on an online shopping site, users can view the product in the same colors regardless of the device they are viewing.This is expected to improve user experience and operational efficiency.

[0060] The color information analysis unit analyzes the proportion of specific colors present in an image and can adjust the color display based on that information. For example, when the generation AI analyzes the color information in an image, the color information analysis unit simultaneously analyzes texture and pattern information. For example, it recognizes the texture of fabric or wood grain patterns in the image and adjusts the color display based on that. The color information analysis unit also analyzes texture and pattern information along with the color information in the image and reflects this in the color display adjustments. For example, it analyzes ripples on the surface of water or the patterns of sand particles in the image to achieve a natural color display. The color information analysis unit also analyzes texture and pattern information in addition to the color information in the image and reflects this in the color display adjustments. For example, it recognizes the pattern of bricks or the shape of leaves in the image and adjusts the color display based on that. This allows for detailed analysis of the color information in the image and achieves optimal color display.

[0061] The display environment information collection unit can grasp the display characteristics of the device and adjust the color display to provide the optimal color display within that range. For example, the generation AI in the display environment information collection unit grasps the display characteristics of the device and adjusts the color display to provide the optimal color display within that range. For example, the generation AI collects information such as the device's resolution, color gamut, and brightness, and adjusts the color display based on that information. The display environment information collection unit also adjusts the color display by having the generation AI take into account the display characteristics of the device and the user's visual characteristics. For example, it provides easy-to-see color display for users with color vision deficiencies. The display environment information collection unit also adjusts the color display by having the generation AI take into account the display characteristics of the device and the user's visual characteristics. For example, it provides color display with enhanced contrast for users with visual impairments. The display environment information collection unit also adjusts the color display by having the generation AI take into account the display characteristics of the device and the user's visual characteristics. For example, it adjusts the character size and color for users with presbyopia. This achieves the optimal color display according to the device's characteristics.

[0062] The color display adjustment unit can improve visibility by increasing the brightness of an image in a dark environment and adjusting the saturation in a bright environment. The color display adjustment unit, for example, uses an emotion estimation function to estimate the emotion of a user viewing an image and displays a color according to the emotion. For example, if the user is relaxed, warm colors are emphasized. The color display adjustment unit also uses the emotion estimation function to estimate the emotion of a user viewing an image and displays a color according to the emotion. For example, if the user is excited, vivid colors are emphasized. The color display adjustment unit also uses the emotion estimation function to estimate the emotion of a user viewing an image and displays a color according to the emotion. For example, if the user is sad, muted colors are emphasized. This achieves improved visibility according to the environment.

[0063] The color display adjustment unit can make adjustments to maintain consistency in color display across different devices. For example, when the generation AI analyzes the color information of an image, the color display adjustment unit simultaneously analyzes audio data and text data, and adjusts the color display based on multimodal information. For example, it analyzes explanatory audio and captions related to the image and reflects them in the color display. In addition, when the generation AI analyzes the color information of an image, the color display adjustment unit simultaneously analyzes audio data and text data, and adjusts the color display based on multimodal information. For example, it analyzes audio and text related to scenes in the image and reflects them in the color display. In addition, when the generation AI analyzes the color information of an image, the color display adjustment unit simultaneously analyzes audio data and text data, and adjusts the color display based on multimodal information. For example, it analyzes music and narration related to the image and reflects them in the color display. This achieves consistent color display across different devices.

[0064] The color display adjustment unit can adjust the color of an image according to the ambient lighting conditions. For example, the generation AI in the color display adjustment unit learns the color sensibilities of different cultural spheres and regions and, based on that, displays the optimal color for each region. For example, people in Asia tend to prefer vivid colors, so the color display is adjusted to match that. The generation AI also learns the color sensibilities of different cultural spheres and regions and, based on that, displays the optimal color for each region. For example, people in Europe tend to prefer subdued colors, so the color display is adjusted to match that. The generation AI also learns the color sensibilities of different cultural spheres and regions and, based on that, displays the optimal color for each region. For example, people in South America tend to prefer bright colors, so the color display is adjusted to match that. This achieves the optimal color display according to the lighting conditions.

[0065] The color information analysis unit analyzes not only the color information in an image, but also the texture and pattern information, and reflects this in color display adjustments. For example, when the generation AI analyzes the color information in an image, the color information analysis unit simultaneously analyzes the texture and pattern information. For example, it recognizes the texture of fabric or wood grain patterns in the image and adjusts the color display based on that. The color information analysis unit also analyzes the texture and pattern information along with the color information in the image, and reflects this in color display adjustments. For example, it analyzes the ripples on the water surface or the patterns of sand particles in the image to achieve natural color display. The color information analysis unit also analyzes the texture and pattern information in the image, and reflects this in color display adjustments. For example, it recognizes the brick pattern or the shape of leaves in the image and adjusts the color display based on that. This achieves color display that takes the texture and pattern of the image into account.

[0066] The color information analysis unit can recognize specific objects within an image and adjust the color display to optimally suit each object. For example, the color information analysis unit allows the generation AI to recognize specific objects within an image and display the optimal color for each object. For example, to display a person's skin color naturally, only the skin part is adjusted. The color information analysis unit also allows the generation AI to recognize specific objects within an image and display the optimal color for each object. For example, it recognizes the sky and ocean parts within a landscape image and optimizes the colors of each. The color information analysis unit also allows the generation AI to recognize specific objects within an image and display the optimal color for each object. For example, it recognizes the product part within a product image and accurately displays the product color. This allows the optimal color display for specific objects.

[0067] The color information analysis unit can use the emotion estimation function to estimate the emotion of a user viewing an image and display a color according to that emotion. The color information analysis unit, for example, uses the emotion estimation function to estimate the emotion of a user viewing an image and display a color according to that emotion. For example, if the user is relaxed, warm colors are emphasized. The color information analysis unit also uses the emotion estimation function to estimate the emotion of a user viewing an image and display a color according to that emotion. For example, if the user is excited, vivid colors are emphasized. The color information analysis unit also uses the emotion estimation function to estimate the emotion of a user viewing an image and display a color according to that emotion. For example, if the user is sad, calm colors are emphasized. In this way, a color display according to the user's emotion is realized.

[0068] When analyzing the color information of an image, the color information analysis unit simultaneously analyzes audio data and text data, and can adjust the color display based on multimodal information. For example, when the generation AI analyzes the color information of an image, the color information analysis unit simultaneously analyzes audio data and text data, and adjusts the color display based on multimodal information. For example, it analyzes explanatory audio and captions related to the image and reflects this in the color display. In addition, when the generation AI analyzes the color information of an image, the color information analysis unit simultaneously analyzes audio data and text data, and adjusts the color display based on multimodal information. For example, it analyzes audio and text related to scenes in the image and reflects this in the color display. In addition, when the generation AI analyzes the color information of an image, the color information analysis unit simultaneously analyzes audio data and text data, and adjusts the color display based on multimodal information. For example, it analyzes music and narration related to the image and reflects this in the color display. This enables color display based on multimodal information.

[0069] The color information analysis unit learns the color sensibilities of different cultural spheres and regions, and based on that, can display colors that are optimal for each region. For example, the generation AI in the color information analysis unit learns the color sensibilities of different cultural spheres and regions, and based on that, displays colors that are optimal for each region. For example, people in Asia tend to prefer vivid colors, so the color display is tailored to that. The generation AI also learns the color sensibilities of different cultural spheres and regions, and based on that, displays colors that are optimal for each region. For example, people in Europe tend to prefer subdued colors, so the color display is tailored to that. The generation AI also learns the color sensibilities of different cultural spheres and regions, and based on that, displays colors that are optimal for each region. For example, people in South America tend to prefer bright colors, so the color display is tailored to that. This allows for optimal color display for each region.

[0070] The color information analysis unit can use the emotion estimation function to monitor the emotion of a user viewing an image in real time and dynamically adjust the color display according to the emotion. The color information analysis unit, for example, uses the emotion estimation function to monitor the emotion of a user viewing an image in real time and dynamically adjust the color display according to the emotion. For example, the color display is adjusted every time the user's emotion changes. The color information analysis unit also uses the emotion estimation function to monitor the emotion of a user viewing an image in real time and dynamically adjust the color display according to the emotion. For example, if the user is relaxed, warm colors are emphasized. The color information analysis unit also uses the emotion estimation function to monitor the emotion of a user viewing an image in real time and dynamically adjust the color display according to the emotion. For example, if the user is excited, vivid colors are emphasized. This realizes dynamic color display according to the user's emotion.

[0071] The display environment information collecting unit can adjust color display taking into account not only the display characteristics of the device but also the visual characteristics of the user. In the display environment information collecting unit, for example, the generation AI adjusts color display taking into account the display characteristics of the device and the visual characteristics of the user. For example, it provides color display that is easy to see for users with color vision deficiency. In addition, the display environment information collecting unit adjusts color display taking into account the display characteristics of the device and the visual characteristics of the user. For example, it provides color display with enhanced contrast for users with visual impairments. In addition, the display environment information collecting unit adjusts color display taking into account the display characteristics of the device and the visual characteristics of the user. For example, it adjusts the size and color of characters for users with presbyopia. In this way, it realizes color display that suits the user's visual characteristics.

[0072] The display environment information collection unit collects not only ambient lighting conditions but also time of day and weather information, and can display colors accordingly. In the display environment information collection unit, for example, the generation AI collects ambient lighting conditions, time of day, and weather information, and displays colors accordingly. For example, bright colors are emphasized during sunny daytime weather. In addition, the display environment information collection unit collects ambient lighting conditions, time of day, and weather information, and displays colors accordingly. For example, muted colors are emphasized during cloudy nighttime weather. In addition, the display environment information collection unit collects ambient lighting conditions, time of day, and weather information, and displays colors accordingly. For example, warm colors are emphasized in the dim light of the evening. This allows for optimal color display according to the time of day and weather.

[0073] The display environment information collecting unit can use the emotion estimation function to collect the user's emotional state and adjust the display environment according to the emotion. The display environment information collecting unit, for example, uses the emotion estimation function to collect the user's emotional state and adjust the display environment according to the emotion. For example, if the user is relaxed, warm lighting is emphasized. The display environment information collecting unit also uses the emotion estimation function to collect the user's emotional state and adjust the display environment according to the emotion. For example, if the user is excited, vivid colors are emphasized. The display environment information collecting unit also uses the emotion estimation function to collect the user's emotional state and adjust the display environment according to the emotion. For example, if the user is sad, calm colors are emphasized. In this way, the display environment can be adjusted according to the user's emotion.

[0074] The display environment information collecting unit can take into consideration the user's location information and activity status when collecting information about the display environment, and display colors accordingly. For example, when the generation AI collects information about the display environment, the display environment information collecting unit takes into consideration the user's location information and activity status, and displays colors accordingly. For example, it emphasizes bright colors when the user is active outdoors. Furthermore, when the generation AI collects information about the display environment, the display environment information collecting unit takes into consideration the user's location information and activity status, and displays colors accordingly. For example, it emphasizes warm colors when the user is relaxing indoors. Furthermore, when the generation AI collects information about the display environment, the display environment information collecting unit takes into consideration the user's location information and activity status, and displays colors accordingly. For example, it emphasizes vivid colors when the user is exercising. This enables color display according to the user's location information and activity status.

[0075] The display environment information collecting unit can share information between different devices and achieve consistent color display across multiple devices. The display environment information collecting unit, for example, shares information between devices with different generation AIs and achieves consistent color display across multiple devices. For example, when the same image is displayed on a smartphone and a tablet, the color display is unified. The display environment information collecting unit also shares information between devices with different generation AIs and achieves consistent color display across multiple devices. For example, when the same image is displayed on a PC and a TV, the color display is unified. The display environment information collecting unit also shares information between devices with different generation AIs and achieves consistent color display across multiple devices. For example, when the same image is displayed on a smartwatch and a smartphone, the color display is unified. This achieves consistent color display across multiple devices.

[0076] The display environment information collecting unit uses the emotion estimation function to monitor the user's emotional state in real time and dynamically adjust the display environment according to the emotion. The display environment information collecting unit, for example, uses the emotion estimation function to monitor the user's emotional state in real time and dynamically adjust the display environment according to the emotion. For example, the display environment information collecting unit adjusts lighting and color display each time the user's emotion changes. The display environment information collecting unit also uses the emotion estimation function to monitor the user's emotional state in real time and dynamically adjusts the display environment according to the emotion. For example, if the user is relaxed, warm lighting is emphasized. The display environment information collecting unit also uses the emotion estimation function to monitor the user's emotional state in real time and dynamically adjusts the display environment according to the emotion. For example, if the user is excited, vivid colors are emphasized. This realizes dynamic adjustment of the display environment according to the user's emotion.

[0077] The color display adjustment unit can perform optimal color display according to the content of the image when standardizing color display. For example, when the generation AI standardizes color display, the color display adjustment unit performs optimal color display according to the content of the image. For example, it emphasizes natural colors in landscape images and displays natural skin colors in portrait images. Furthermore, when the generation AI standardizes color display, the color display adjustment unit performs optimal color display according to the content of the image. For example, in product images, it accurately displays the product color and adjusts the background color. Furthermore, when the generation AI standardizes color display, the color display adjustment unit performs optimal color display according to the content of the image. For example, in images of artwork, it faithfully reproduces the colors of the artwork. This achieves optimal color display according to the content of the image.

[0078] The color display adjustment unit can learn the user's past browsing history and preferences when standardizing color display, and adjust the color display based on that. For example, when the generation AI standardizes color display, the color display adjustment unit learns the user's past browsing history and preferences and adjusts the color display based on that. For example, it prioritizes displaying colors preferred by the user. Furthermore, when the generation AI standardizes color display, the color display adjustment unit learns the user's past browsing history and preferences and adjusts the color display based on that. For example, it refers to the colors of content frequently viewed by the user. Furthermore, when the generation AI standardizes color display, the color display adjustment unit learns the user's past browsing history and preferences and adjusts the color display based on that. For example, it reflects the user's preferred color tone and brightness. This allows for color display that meets the user's preferences.

[0079] The color display adjustment unit uses the emotion estimation function to standardize the color display according to the user's emotion, thereby eliciting an emotionally positive response. The color display adjustment unit, for example, uses the emotion estimation function to standardize the color display according to the user's emotion, thereby eliciting an emotionally positive response. For example, if the user is relaxed, warm colors are emphasized. The color display adjustment unit also uses the emotion estimation function to standardize the color display according to the user's emotion, thereby eliciting an emotionally positive response. For example, if the user is excited, vivid colors are emphasized. The color display adjustment unit also uses the emotion estimation function to standardize the color display according to the user's emotion, thereby eliciting an emotionally positive response. For example, if the user is sad, calm colors are emphasized. In this way, a positive color display according to the user's emotion is realized.

[0080] The color display adjustment unit can provide optimal color display for different industries and uses when standardizing color display. For example, when the generation AI standardizes color display, the color display adjustment unit provides optimal color display for different industries and uses. For example, in the medical field, it provides color display that emphasizes visibility. Furthermore, when the generation AI standardizes color display, the color display adjustment unit provides optimal color display for different industries and uses. For example, in the education field, it provides color display that enhances learning effectiveness. Furthermore, when the generation AI standardizes color display, the color display adjustment unit provides optimal color display for different industries and uses. For example, in the entertainment field, it provides color display that enhances visual appeal. In this way, optimal color display for different industries and uses is realized.

[0081] The color display adjustment unit can automatically generate a profile for maintaining consistency in color display across different devices when standardizing color display. For example, when the generation AI standardizes color display, the color display adjustment unit automatically generates a profile for maintaining consistency in color display across different devices. For example, when the same image is displayed on a smartphone and a PC, the color display is unified. The color display adjustment unit also automatically generates a profile for maintaining consistency in color display across different devices when the generation AI standardizes color display. For example, when the same image is displayed on a tablet and a television, the color display is unified. The color display adjustment unit also automatically generates a profile for maintaining consistency in color display across different devices when the generation AI standardizes color display. For example, when the same image is displayed on a smartwatch and a smartphone, the color display is unified. This achieves consistent color display across different devices.

[0082] The color display adjustment unit uses the emotion estimation function to monitor the user's emotional state in real time and dynamically standardize the color display according to the emotion. The color display adjustment unit, for example, uses the emotion estimation function to monitor the user's emotional state in real time and dynamically standardize the color display according to the emotion. For example, the color display adjustment unit adjusts the color display every time the user's emotion changes. The color display adjustment unit also uses the emotion estimation function to monitor the user's emotional state in real time and dynamically standardize the color display according to the emotion. For example, if the user is relaxed, warm colors are emphasized. The color display adjustment unit also uses the emotion estimation function to monitor the user's emotional state in real time and dynamically standardize the color display according to the emotion. For example, if the user is excited, vivid colors are emphasized. This achieves dynamic standardization of the color display according to the user's emotion.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The image display assistance system may further include a voice recognition unit. The voice recognition unit can analyze a user's voice commands and adjust the image display. For example, if the user says "make it brighter," the voice recognition unit analyzes the command and sends an instruction to the color display adjustment unit to increase the brightness of the image. If the user says "make it warmer," the voice recognition unit analyzes the command and sends an instruction to the color display adjustment unit to emphasize warm colors. If the user says "set it to night mode," the voice recognition unit analyzes the command and sends an instruction to the color display adjustment unit to display colors suitable for nighttime use. This allows the user to intuitively adjust the image display using voice commands.

[0085] The image display assistance system may further include a gesture recognition unit. The gesture recognition unit can analyze the user's hand movements and adjust the image display. For example, when the user gestures to raise their hand, the gesture recognition unit analyzes the movement and sends an instruction to the color display adjustment unit to increase the brightness of the image. When the user gestures to wave their hand from side to side, the gesture recognition unit analyzes the movement and sends an instruction to the color display adjustment unit to change the color tone. When the user gestures to move their hand back and forth, the gesture recognition unit analyzes the movement and sends an instruction to the color display adjustment unit to adjust the saturation of the image. This allows the user to intuitively adjust the image display using gestures.

[0086] The image display assistance system can further include a biometrics recognition unit. The biometrics recognition unit can analyze the user's biometric information and adjust the image display. For example, the biometrics recognition unit can monitor the user's heart rate and, if the heart rate is high, display colors that have a relaxing effect. The biometrics recognition unit can also monitor the user's skin temperature and, if the skin temperature is low, emphasize warm colors. Furthermore, the biometrics recognition unit can monitor the user's pupil movement and, if the pupil is dilated, emphasize bright colors. This makes it possible to achieve optimal color display based on the user's biometric information.

[0087] The image display assistance system can further include a content recognition unit. The content recognition unit can analyze the content of the image being displayed and display colors according to that content. For example, in the case of a landscape image, natural colors are emphasized, and in the case of a portrait image, skin tones are displayed naturally. In addition, in the case of a product image, the color of the product is accurately displayed and the color of the background is adjusted. Furthermore, in the case of an image of an artwork, the colors of the artwork can be faithfully reproduced. This makes it possible to realize optimal color display according to the content of the image.

[0088] The image display assistance system can further include a learning unit that learns the user's past browsing history. The learning unit records what images the user has viewed in the past and can adjust the color display based on that information. For example, it can prioritize the display of colors that the user has viewed in the past. It can also refer to the colors of content that the user frequently views. It can also reflect the user's preferred color tone and brightness. This makes it possible to realize color display that suits the user's preferences.

[0089] The image display assistance system can further include an industry adaptation unit that performs color display according to different industries and applications. The industry adaptation unit can perform optimal color display according to different industries and applications, such as the medical field, education field, and entertainment field. For example, in the medical field, color display that emphasizes visibility can be performed, while in the education field, color display that enhances learning effectiveness can be performed. Furthermore, in the entertainment field, color display that enhances visual appeal can be performed. This makes it possible to achieve optimal color display according to different industries and applications.

[0090] The image display assistance system may further include a device linking unit that shares information between different devices. The device linking unit shares information between different devices, such as smartphones, tablets, PCs, and televisions, and can achieve consistent color display across multiple devices. For example, when the same image is displayed on a smartphone and a tablet, the color display is unified. Also, when the same image is displayed on a PC and a television, the color display can be unified. Furthermore, when the same image is displayed on a smartwatch and a smartphone, the color display can be unified. This allows consistent color display across multiple devices.

[0091] The image display assistance system can further include a location information recognition unit that takes into account the user's location information and activity status. The location information recognition unit can analyze the user's location information and activity status and display colors accordingly. For example, bright colors can be emphasized when the user is active outdoors, and warm colors can be emphasized when the user is relaxing indoors. Also, vivid colors can be emphasized when the user is exercising. This makes it possible to display colors according to the user's location information and activity status.

[0092] The image display assistance system can further include a cultural adaptation unit that learns the color sensibilities of different cultural spheres and regions. The cultural adaptation unit can learn the color sensibilities of different cultural spheres and regions and, based on that, display colors that are optimal for each region. For example, since people in Asia tend to prefer vivid colors, the color adaptation unit can display colors that match that. Meanwhile, people in Europe tend to prefer subdued colors, the color adaptation unit can display colors that match that. Furthermore, people in South America tend to prefer bright colors, so the color adaptation unit can display colors that match that. This makes it possible to achieve optimal color display for each region.

[0093] The image display assistance system may further include an emotion monitoring unit that monitors the user's emotional state in real time. The emotion monitoring unit can monitor the user's emotional state in real time and dynamically adjust the color display according to the emotion. For example, the color display may be adjusted each time the user's emotion changes. In addition, if the user is relaxed, warm colors may be emphasized, and if the user is excited, vivid colors may be emphasized. Furthermore, if the user is sad, calm colors may be emphasized. This makes it possible to realize dynamic color display according to the user's emotions.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The color information analysis unit analyzes the color information of the image. For example, the color information analysis unit uses the generation AI to analyze information such as hue, saturation, and brightness of each pixel in the image. The color information analysis unit can also use the generation AI to analyze the percentage of a specific color present in the image. Furthermore, the color information analysis unit can use the generation AI to analyze not only the color information in the image, but also the texture and pattern information. For example, the generation AI can recognize the texture of fabric or wood grain patterns in the image and adjust the color display based on that. Step 2: The display environment information collection unit collects information about the display environment. For example, the display environment information collection unit uses the generation AI to collect the device's display characteristics (resolution, color gamut, brightness, etc.) and the surrounding lighting conditions (light intensity, color temperature, etc.). The display environment information collection unit can also use the generation AI to adjust the color display taking into account not only the device's display characteristics but also the user's visual characteristics (e.g., color vision deficiency). Furthermore, the display environment information collection unit can use the generation AI to collect not only the surrounding lighting conditions but also time of day and weather information, and display colors accordingly. Step 3: The color display adjustment unit adjusts the color display based on the color information analyzed by the color information analysis unit and the display environment information collected by the display environment information collection unit. For example, the color display adjustment unit improves visibility by increasing the brightness of the image in a dark environment and adjusting the saturation in a bright environment. The color display adjustment unit can also make adjustments to maintain consistency in color display across different devices. Furthermore, the color display adjustment unit can use the generative AI to estimate the user's emotions using an emotion estimation function and display colors according to those emotions. For example, if the user is relaxed, the emotion estimation function can be used to emphasize warm colors.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0124] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0140] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0146] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0147] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0148] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0149] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0150] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0151] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0152] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0153] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0154] 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.

[0155] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0156] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0157] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0158] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0159] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0160] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0161] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0162] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a color information analysis unit that analyzes color information of an image; a display environment information collection unit that collects information about the display environment; a color display adjustment unit that adjusts color display based on the color information analyzed by the color information analysis unit and the information on the display environment collected by the display environment information collection unit. A system characterized by:

2. The color information analysis unit Analyzes the proportion of specific colors in an image and adjusts the color display based on that information 2. The system of claim 1.

3. The display environment information collecting unit Understand the display characteristics of the device and adjust to display the optimal colors within that range.

2. The system of claim 1.

4. The color display adjustment unit Improve visibility by increasing image brightness in dark environments and adjusting saturation in bright environments 2. The system of claim 1.

5. The color display adjustment unit Adjusting color consistency across devices 2. The system of claim 1.

Images