System

Abstract

A system is provided.SOLUTION: A system comprising: means for capturing video in a field of view of a user with a built-in camera; means for transmitting the captured video data to a server in real time; means for applying a color conversion algorithm to the received video data; means for sending the video data to which the color conversion algorithm is applied back to the terminal; and means for displaying the converted video on a display of the user's glasses-type terminal.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] Users with color vision variability often experience inconvenience in their daily lives and in certain tasks due to difficulty distinguishing certain color combinations. To solve this problem, a system is needed that can easily and effectively correct visual information without requiring users to have special knowledge or skills. However, current color correction technologies and devices are difficult to use and lack real-time performance, making them impractical. Therefore, there is an urgent need to develop a new system that can easily achieve color universal design compliance and improve users' visual experience. [Means for solving the problem]

[0005] The present invention provides a means for capturing an image within a user's field of view using a built-in camera and transmitting the captured image data to a server in real time, and a server means for applying a color conversion algorithm to the received image data. The system also includes a means for returning the image data to which the color conversion algorithm has been applied to the terminal and displaying the converted image on the display of the user's eyeglass-type terminal. This allows the user to more easily distinguish specific color combinations and more effectively utilize visual information in daily life and specific tasks. The color conversion algorithm also includes hue conversion, saturation adjustment, and brightness correction, optimizing visual information according to the individual user's color vision characteristics. This system is easy to operate and operates in real time, providing a highly practical color correction technology.

[0006] A "built-in camera" is a device that is integrated into a glasses-type device and has the function of capturing images within the user's field of vision.

[0007] "Capture" is the act of acquiring video or images using a photographic device such as a camera.

[0008] "Video data" refers to visual information captured by the built-in camera in digital form.

[0009] "Real-time" refers to the characteristics of functions and processes that react and respond almost instantly to user operations and situations.

[0010] A "server" is a computer or system for processing data via a network, and in the present invention has the role of applying a color conversion algorithm to received video data.

[0011] A "color conversion algorithm" is a calculation method that converts and adjusts the colors of video data to make it easier for users with specific color vision characteristics to distinguish colors.

[0012] A "glasses-type terminal" is an information display device that can be worn by a user like glasses and has the function of displaying video data directly to the user.

[0013] A "display" is a display device for visually displaying video data.

[0014] "Hue transformation" is the process of changing one hue to another.

[0015] "Saturation adjustment" is the process of changing the vividness or intensity of a color.

[0016] "Lightening" is the process of adjusting the brightness of a color. [Brief explanation of the drawings]

[0017] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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, a 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), and an APU (Accelerated Processing Unit).

[0021] 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.

[0022] 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.

[0023] 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), Bluetooth (registered trademark), etc.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0029] 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.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention is a Color Universal Design (CUD) compatible system using glasses-type devices to facilitate color discrimination for users with color vision variability. This system is realized by capturing images in the field of view with a built-in camera, sending the images to a server for color conversion, and displaying the converted images in real time.

[0039] Operation overview

[0040] The user puts on the glasses-type device and starts using it. After putting it on, the camera inside the device starts up and begins capturing images in the user's field of view in real time. The captured image data is temporarily stored in the device.

[0041] The device sends the captured video data to the server using a low-latency communication protocol, enabling real-time processing.

[0042] The server then applies a color conversion algorithm to the received video data. This algorithm converts certain colors into other colors that are easier to distinguish based on the user's color vision characteristics. For example, if a user has difficulty distinguishing between red and green, the algorithm converts red into yellow. It also adjusts saturation and brightness to create an image that is easier to distinguish visually.

[0043] The converted image data is sent back to the terminal from the server, and the terminal displays the received converted image data on the display in real time. Through this converted image, the user can easily distinguish colors and perform daily life and specific tasks comfortably.

[0044] Specific examples

[0045] For example, suppose user A has difficulty distinguishing between red and green and is looking at a garden full of flowers and leaves. The system works as follows:

[0046] 1. User A wears the glasses-type device and observes the garden.

[0047] 2. The device's built-in camera captures footage of the garden.

[0048] 3. The device sends the captured video data to the server.

[0049] 4. The server receives the video data and applies a color transformation algorithm based on color vision characteristics, converting red flowers to a more easily distinguishable yellow and adjusting the saturation of green leaves.

[0050] 5. The server returns the converted video data to the device.

[0051] 6. The device displays the converted video data on the screen.

[0052] 7. User A can now clearly distinguish the image of red flowers converted to yellow from green leaves.

[0053] In this way, the system of the present invention makes it easier for users with color vision variability to distinguish colors and improves convenience in their daily lives. The system is highly practical because it is capable of real-time operation, provides advanced color correction functions while being easy for users to operate.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] The user wears the glasses-type device and observes the images in their field of vision. Any user input or setting changes are made at this point. The device is powered on and the camera and communication module are activated.

[0057] Step 2:

[0058] The device captures the user's field of view in real time through the built-in camera, which captures video frames at a constant frame rate and stores them in the device's internal memory.

[0059] Step 3:

[0060] The device splits the captured video data into small data packets over an internet connection, which contain timestamps and camera metadata (resolution, frame rate, etc.).

[0061] Step 4:

[0062] The device sends the divided data packets to the server's API endpoint, using a low-latency communication protocol (e.g., WebSocket) to maintain real-time performance.

[0063] Step 5:

[0064] The server receives the data packets sent from the terminal, reconstructs the received packets, and decodes them into the original video frames.

[0065] Step 6:

[0066] The server applies a color transformation algorithm to the decoded video frames, which converts certain colors into other, more easily distinguishable colors and adjusts saturation and brightness as needed.

[0067] Step 7:

[0068] The server then divides the transformed video frames, which are generated as a result of the color transformation algorithm, into data packets again, along with metadata about the transformation process.

[0069] Step 8:

[0070] The server then sends the converted video data packets back to the terminal, again using a low-latency communication protocol.

[0071] Step 9:

[0072] The terminal receives the converted video data packets, reconcatenates the received packets, and reconstructs the converted video frames.

[0073] Step 10:

[0074] The device then reconstructs the converted video frames and displays them in real time on the glasses-type device's display, with the display process optimized to avoid interfering with the user's visual field.

[0075] Step 11:

[0076] The user experiences color vision-compatible visual information in real time through the converted image. If the user provides feedback or changes to settings, the system will accept them appropriately and repeat the process.

[0077] Example 1

[0078] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0079] Current visual aids and software lack effective means for users with color vision variability to easily distinguish colors in their daily lives. In particular, conventional methods have difficulty converting and displaying colors in real time. Therefore, in order to improve the quality of life for users with color vision variability, the development of a new system that can convert and display colors clearly in real time is required.

[0080] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0081] In this invention, the server includes: a means for a user wearing a glasses-type terminal and capturing an image in the user's field of view in real time with a built-in camera; a means for transmitting the captured image data to the server using a low-latency communication protocol; a server means for applying a color conversion algorithm based on the user's color vision characteristics to the received image data; a means for compressing the image data to which the color conversion algorithm has been applied with a high-speed encoder and transmitting it back to the terminal in real time; and a means for decoding the converted image data and displaying it on the display of the user's glasses-type terminal. This enables a system in which users with visual color vision variability can convert and display colors in real time, making it easy to distinguish colors.

[0082] "User" refers to any individual who uses the system, and is particularly intended for people with visual color vision variability.

[0083] A "glasses-type terminal" is a glasses-type device worn by a user, which has a built-in camera and a display, and is a device for capturing and displaying visual information.

[0084] The "built-in camera" is a camera built into the glasses-type device, and serves to capture images within the user's field of vision.

[0085] "Capture" refers to the action of capturing images within the field of view using the built-in camera and saving them as data.

[0086] "Video Data" refers to data that digitally represents the image of the field of view captured by the built-in camera.

[0087] A "low-latency communication protocol" is a communication method for quickly sending and receiving data, and is used to achieve real-time processing.

[0088] A "server" is a computer system installed on a network that receives and processes data sent from multiple clients.

[0089] A "color conversion algorithm" is a calculation method for converting color information contained in video data to make it easier for users with specific color vision characteristics to distinguish.

[0090] A "high-speed encoder" is a device or software for compressing digital data that can perform data compression at high speeds.

[0091] The "display" is an image display device mounted on the glasses-type terminal, and serves to visually present color-converted image data to the user.

[0092] "Decoding" refers to the process of restoring compressed data to its original form.

[0093] "Color vision characteristics" refers to the color discrimination ability and type of color vision deficiency of each individual user, and is the information that forms the basis of the color conversion applied by the system.

[0094] This invention is a Color Universal Design (CUD) compatible system using glasses-type devices that allows users with color vision variability to easily distinguish colors so that they can comfortably perform daily activities and specific tasks. This system captures images in the field of view with a built-in camera, sends the images to a server for color conversion, and displays the converted images in real time, making it easier for users to recognize color differences.

[0095] Hardware and software used

[0096] Glasses: A device with a built-in camera and display that is worn by the user and captures video of the user's field of vision.

[0097] Built-in camera: A camera installed in the glasses-type device that captures images of the field of view in real time.

[0098] Server: A computer system for receiving video data and applying color transformation algorithms.

[0099] Communication protocol: A low-latency communication protocol (e.g., WebSocket, HTTP / 2) is used to send and receive video data in real time.

[0100] Color conversion algorithm: A calculation method for converting the color information contained in video data and converting specific colors into other, more easily distinguishable colors.

[0101] High-Speed ​​Encoder: A device or software for compressing digital data at high speed.

[0102] Display: A device installed in the glasses-type device for displaying the converted video data.

[0103] Decoder: A device or software that converts compressed data back into its original form.

[0104] Operational Overview

[0105] The user puts on the glasses and starts using them. The camera inside the device activates and captures the user's field of view in real time. The captured video data is temporarily stored in the device and then sent to a server using a low-latency communication protocol.

[0106] The server temporarily stores the received video data in a buffer and applies a color conversion algorithm based on the user's color vision characteristics. This algorithm converts certain colors into more easily distinguishable colors and adjusts saturation and brightness to create a visually more easily distinguishable image. The converted video data is then compressed using a high-speed encoder and sent back to the device in real time.

[0107] The device decodes the compressed video data and displays it on the built-in display. This allows users to more easily distinguish colors through the converted video. For example, for users who have difficulty distinguishing between red and green, red flowers will be converted to yellow and the saturation of green leaves will be enhanced, making them easier to distinguish.

[0108] Specific examples

[0109] For example, suppose that User A has difficulty distinguishing between red and green and is observing a garden. In this case, the system operates as follows:

[0110] 1. User A wears the glasses-type device and observes the garden.

[0111] 2. The camera inside the device captures images of the garden in real time.

[0112] 3. The captured video data is sent to the server using a low-latency communication protocol.

[0113] 4. The server receives the video data and applies a color transformation algorithm based on User A's color vision characteristics, converting red flowers to yellow and adjusting the saturation of green leaves.

[0114] 5. The server compresses the converted video data using a high-speed encoder and sends it back to the terminal.

[0115] 6. The device decodes the compressed video data and displays it on the screen.

[0116] 7. User A can easily distinguish the red flowers (converted to yellow) and green leaves through the converted image.

[0117] Example prompts for generative AI models

[0118] "User A, who has difficulty distinguishing between red and green, is wearing a glasses-type device and observing a garden. Please explain in detail the steps involved in capturing video data, sending it to the server, converting the color, retransmitting it to the device, and displaying it."

[0119] By inputting this prompt into a generative AI model, detailed operational steps are explained.

[0120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0121] Step 1:

[0122] The user puts on the glasses and activates the built-in camera. The camera captures the image of the user's field of vision in real time. The image data is captured at a rate of 30 frames per second. The input is the scene coming into the user's field of vision, and the output is digital image data.

[0123] Step 2:

[0124] The device temporarily stores the captured video data in its internal memory. At the same time, it transmits the captured video data to the server in real time using a low-latency communication protocol (e.g., WebSocket). The input is the captured video data, and the output is the video data as communication packets.

[0125] Step 3:

[0126] The server receives video data sent from the device. The server temporarily stores the received video data in a buffer and applies a color conversion algorithm based on the user's color vision characteristics. Specifically, if it is difficult to distinguish between red and green, the server converts red to yellow and emphasizes the saturation of green. The input is unconverted video data, and the output is color-converted video data.

[0127] Step 4:

[0128] The server compresses the color-converted video data using a high-speed encoder. Compression reduces the amount of data and improves communication speed. The input is color-converted video data, and the output is compressed data.

[0129] Step 5:

[0130] The server then sends the compressed video data back to the terminal using a low-latency communication protocol. The input is compressed video data, and the output is compressed data as communication packets.

[0131] Step 6:

[0132] The terminal receives the compressed data sent from the server. The terminal uses a decoder to decode the compressed data and return it to its original format. The input is the compressed video data, and the output is the decoded video data.

[0133] Step 7:

[0134] The device displays the decoded video data on the built-in display. The real-time converted video allows the user to easily distinguish colors and clearly recognize objects and colors within their field of view. The input is the decoded video data, and the output is the video displayed on the display.

[0135] (Application example 1)

[0136] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0137] Users with color vision variability often have difficulty identifying products while shopping in physical stores. This is particularly inconvenient when the color of product labels or packaging contains important information. To solve this problem, a system is needed that can correct color vision in real time to compensate for visual impairments.

[0138] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0139] In this invention, the server includes means for capturing an image in the user's field of view with a built-in camera, means for transmitting the captured image data to the server in real time, means for applying a color conversion algorithm to the received image data, means for transmitting the image data to which the color conversion algorithm has been applied back to the terminal, means for displaying the converted image on the display of the user's eyeglass-type terminal, and means for supporting shopping in a physical store using a communication protocol, thereby enabling users with visual color vision variability to easily identify products in a physical store.

[0140] The "built-in camera" is a camera device built into the glasses-type terminal, which captures images within the user's field of vision.

[0141] "Captured video data" refers to visual information captured by the built-in camera that is saved or temporarily stored as digital data.

[0142] "Means for transmitting to a server in real time" refers to a communication method for transmitting video data captured by the built-in camera to a server without delay.

[0143] The "server means" is a central processing unit that processes received video data using various algorithms to generate converted data.

[0144] "Color conversion algorithm" refers to a calculation method and program for converting a specific color into another color so that users with color vision deficiency can easily recognize the color difference.

[0145] The "means for sending back to the terminal" is a communication method for returning data processed by the server back to the glasses-type terminal.

[0146] "Converted image" refers to image data that has undergone color correction through the application of a color conversion algorithm.

[0147] An "eyeglasses-type terminal" is an eyeglass-type electronic device that has a display and a built-in camera and provides visual assistance functions when worn by the user.

[0148] A "communication protocol" is a protocol that defines the rules and procedures for data communication, and in this case refers to a protocol that achieves low-latency communication.

[0149] "Means to support shopping in physical stores" is a visual aid system that makes it easier for users to select products in physical stores.

[0150] "Python" is a high-level programming language used to implement color conversion algorithms on the server.

[0151] "OpenCV" is an open source library for efficient image processing and is used for color conversion algorithms.

[0152] This invention is a Color Universal Design (CUD)-compliant system that allows users with color vision variability to easily distinguish colors. This system mainly utilizes glasses-type terminals and a server, and aims to improve the shopping experience in physical stores.

[0153] Operation overview

[0154] 1. User Action:

[0155] A user puts on the glasses-type device in a physical store where they are shopping, and the device begins capturing images of what is in the user's field of view using its built-in camera.

[0156] 2. Terminal processing:

[0157] The built-in camera in the glasses captures video and temporarily stores the data in real time. The device then sends the stored video data to a server via a communication protocol. The use of a low-latency communication protocol (e.g., WebSocket) enables real-time data transfer.

[0158] 3. Server processing:

[0159] The server then applies a color conversion algorithm using Python and OpenCV to the received video data. This algorithm converts certain colors into other colors that are easier to distinguish depending on the user's color vision characteristics. For example, if a user has difficulty distinguishing between red and green, red will be converted to yellow.

[0160] 4. Return to device:

[0161] The color-converted video data is sent back from the server to the terminal and displayed in real time on the display of the glasses-type terminal.

[0162] Hardware and Software

[0163] Glasses-type device: Equipped with a built-in camera, display, and communication module.

[0164] Server: A central processing unit that processes video data and applies color transformation algorithms.

[0165] Communication protocol: Uses low-latency WebSocket for data transfer.

[0166] Software: Python and OpenCV are used for video processing on the server.

[0167] Specific examples

[0168] For example, assume that user A has color vision that makes it difficult to distinguish between red and green, and is looking for a red product label in a physical store.

[0169] 1. User A puts on the glasses-type device and looks at the product shelves.

[0170] 2. The device's built-in camera captures images of the shelves and sends the data to the server.

[0171] 3. The server applies a color conversion algorithm to the received video data, converting the red label to yellow.

[0172] 4. The server returns the converted video data to the terminal and displays it on the display in real time.

[0173] 5. User A can identify the label that has been converted to yellow, making it easier for them to find the product they are looking for.

[0174] Prompt Sentence Examples

[0175] Prompt: Describe a system in which a color vision assistant transforms red labels into yellow in a brick-and-mortar store, making it easier for users to identify products.

[0176] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0177] Step 1:

[0178] A user puts on the glasses-type device and starts shopping in a physical store. The built-in camera captures the image of the user's field of view. The input is the image scene in the user's field of view, and the output is the captured image data.

[0179] Step 2:

[0180] The device temporarily stores the captured video data and sends it to the server via a communication protocol (e.g., WebSocket). The input is the captured video data, and the output is the video data sent to the server.

[0181] Step 3:

[0182] The server applies a color conversion algorithm to the received video data using Python and OpenCV. The specific data processing performed here is to convert colors into colors that are easy to recognize for people with color vision deficiency. The input is the original video data received by the server, and the output is the color-converted video data.

[0183] Step 4:

[0184] The server then sends the image data to which the color conversion algorithm has been applied back to the terminal via the communication protocol. The input is the color-converted image data, and the output is the image data sent from the server to the terminal.

[0185] Step 5:

[0186] The terminal displays the received color-converted video data on a display in real time. The input is the color-converted video data, and the output is the converted video that is visually displayed to the user.

[0187] Step 6:

[0188] The user can then use the converted image to identify the more easily distinguishable colors and search for the product. Specifically, the user identifies the red label, which has been converted to yellow, on the display of the glasses-type device and finds the product they need.

[0189] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0190] This invention combines a Color Universal Design (CUD)-compatible system using glasses-type devices to facilitate color discrimination for users with color vision variability with an emotion engine that recognizes the user's emotions. This system captures images within the field of view with a built-in camera, transmits the images to a server for color conversion, and displays the converted images in real time. The system also recognizes the user's emotions and optimizes the color conversion algorithm based on the user's emotional state, further improving the user experience.

[0191] Operation overview

[0192] The user puts on the glasses-type device and starts using it. After putting it on, the camera inside the device starts up and begins capturing images in the user's field of view in real time. The captured image data is temporarily stored in the device.

[0193] The device sends the captured video data to the server using a low-latency communication protocol, ensuring real-time transmission.

[0194] The server then runs the received video data through a color conversion algorithm, which converts specific colors into others that are easier to recognize, adjusting saturation and brightness as needed. The server then uses an emotion engine to recognize the user's emotional state. The emotion engine uses facial recognition technology and biometric sensors to analyze the user's emotions in real time.

[0195] Based on the analysis results, the parameters of the color conversion algorithm are adjusted. For example, if the user is feeling stressed, the colors are adjusted to reduce stress. By emphasizing or suppressing certain colors, the user's mental state is optimized.

[0196] The converted image data is then sent back to the device, which then displays it in real time. This allows users to more easily distinguish colors and enjoy a visual experience tailored to their emotional state.

[0197] Specific examples

[0198] For example, suppose that User A has difficulty distinguishing between red and green, and is viewing a garden full of flowers and leaves in a stressful situation. The system works as follows:

[0199] 1. User A wears the glasses-type device and observes the garden.

[0200] 2. The device's built-in camera captures footage of the garden.

[0201] 3. The device sends the captured video data to the server.

[0202] 4. The server receives the video data and applies a color conversion algorithm based on color vision characteristics. This algorithm converts red flowers to a more easily distinguishable yellow and adjusts the saturation of green leaves.

[0203] 5. The server uses the emotion engine to recognize User A's emotional state. The emotion engine uses facial recognition technology and biometric sensors to analyze User A's stress level in real time.

[0204] 6. The server adjusts the parameters of the color transformation algorithm based on the output of the emotion engine, for example, emphasizing calm colors to reduce stress.

[0205] 7. The server returns the converted video data to the terminal.

[0206] 8. The device displays the converted video data on the screen.

[0207] 9. User A can not only easily distinguish between the yellow flowers and the adjusted green leaves, but also experience visual information optimized to reduce stress.

[0208] In this way, the system of the present invention facilitates color discrimination for users with color vision variability and provides an optimal visual experience based on their emotional state. The system is highly practical because it provides advanced color correction and emotion recognition capabilities while being real-time and easy for users to operate.

[0209] The processing flow will be explained below.

[0210] Step 1:

[0211] The user wears the glasses-type device and observes the images in their field of vision. Any user input or setting changes are made at this point. The device is powered on and the camera and communication module are activated.

[0212] Step 2:

[0213] The device captures the user's field of view in real time through the built-in camera, which captures video frames at a constant frame rate and stores them in the device's internal memory.

[0214] Step 3:

[0215] The device analyzes the video data captured by the device to detect the user's face, and uses facial recognition technology to estimate the user's emotional state and generate emotion data.

[0216] Step 4:

[0217] The device divides the video data and emotion data into packets and adds timestamps and additional metadata (resolution, frame rate, emotional state, etc.).

[0218] Step 5:

[0219] The device sends the divided data packets to the server's API endpoint in real time using a low-latency communication protocol (e.g., WebSocket).

[0220] Step 6:

[0221] The server receives the data packets sent from the device, reconstructs the received packets, and decodes them into the original video frames and emotion data.

[0222] Step 7:

[0223] The server applies a color transformation algorithm to the decoded video frames, which converts certain colors into other, more easily distinguishable colors and adjusts saturation and brightness as needed.

[0224] Step 8:

[0225] The server dynamically adjusts the parameters of the color conversion algorithm based on the emotional data it recognizes. For example, if the user is feeling stressed, the colors will be changed to a more gentle tone.

[0226] Step 9:

[0227] The server re-divides the converted video data into data packets and also adds metadata about the conversion process.

[0228] Step 10:

[0229] The server then sends the converted video data packets back to the terminal, again using a low-latency communication protocol.

[0230] Step 11:

[0231] The terminal receives the converted video data packets, reconcatenates the received packets, and reconstructs the converted video frames.

[0232] Step 12:

[0233] The device then reconstructs the converted video frames and displays them in real time on the glasses-type device's display, with the display process optimized to avoid interfering with the user's visual field.

[0234] Step 13:

[0235] The user experiences visual information adapted to color vision variability in real time through the converted image. Additionally, the user's mental state is also optimized by providing optimized visual information based on their emotional state. If the user provides feedback or additional setting changes, the system accepts them appropriately and repeats the process.

[0236] Example 2

[0237] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0238] Users with color vision variability often have difficulty distinguishing certain colors in daily life, which can cause stress and anxiety in certain situations. To solve this problem, a system is needed that makes color distinction easier and provides a visual experience optimized according to the user's emotional state.

[0239] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for capturing an image in the user's field of view with a built-in camera, means for transmitting the captured image data to the server in real time, means for applying a color conversion algorithm to the received image data, means for recognizing the user's emotional state using an emotion engine, means for adjusting parameters of the color conversion algorithm based on the output of the emotion engine, means for transmitting the image data to which the color conversion algorithm has been applied back to the terminal, and means for displaying the converted image on the display of the user's eyeglass-type terminal. This not only makes it easier to distinguish colors, but also makes it possible to provide an optimal visual experience according to the user's emotional state.

[0240] The "built-in camera" is a camera device built into the glasses-type terminal, and serves to capture images within the user's field of vision in real time.

[0241] "Captured video data" refers to digital data of video captured by the built-in camera, which is temporarily stored in the terminal and then sent to the server.

[0242] "Real-time transmission" refers to the process of transmitting captured video data to a server immediately with minimal delay, primarily using low-latency communication protocols.

[0243] The "server means" refers to the function of the server that processes the received video data, and specifically includes the application of a color conversion algorithm and emotion recognition using an emotion engine.

[0244] A "color conversion algorithm" is a series of processing steps that converts a specific color into another color based on the user's color vision characteristics and adjusts the saturation and brightness.

[0245] The "emotion engine" is a system that uses facial recognition technology and biometric sensors to recognize the user's emotional state in real time, and optimizes processing based on that output.

[0246] "Emotion recognition" is the process of determining a user's current emotional state from their facial expressions and biometric information using an emotion engine.

[0247] The "display" is a display device mounted on the glasses-type terminal, and serves to allow the user to view the converted video data sent from the server.

[0248] This invention is a Color Universal Design (CUD)-compatible system that combines a glasses-type device that allows users with color vision variability to easily distinguish colors with an emotion engine that recognizes the user's emotions. The system captures images in the field of view with a built-in camera, transmits the images to a server for color conversion, and displays the converted images in real time. The system also recognizes the user's emotions and optimizes the color conversion algorithm based on the user's emotional state, further improving the user experience.

[0249] Hardware and software configuration used

[0250] 1. Glasses-type device

[0251] Built-in camera: Captures what is in the user's field of view.

[0252] Display: Shows the converted video to the user.

[0253] Communication module: Sends video data to the server and receives data from the server.

[0254] 2. Server

[0255] Color conversion algorithm: Converts the color of the video data based on the user's color vision characteristics and adjusts saturation and brightness.

[0256] Emotion Engine: Recognizes user emotions in real time and adjusts the parameters of the color conversion algorithm.

[0257] Communication protocol: Send and receive video data with low latency (e.g., WebSocket).

[0258] Data processing and calculation

[0259] 1. Capture and send

[0260] The user puts on the glasses and starts using it. The built-in camera activates and captures the user's field of view in real time. The captured video data is temporarily stored in the device and then sent to a server using a low-latency communication protocol.

[0261] 2. Applying color transformation

[0262] The server applies a color conversion algorithm to the received video data. This algorithm converts certain colors into other colors and adjusts saturation and brightness to suit the user's color vision characteristics. For example, it may change red to a more easily distinguishable yellow.

[0263] 3. Emotion Recognition and Algorithm Adjustment

[0264] The server uses an emotion engine to recognize the user's emotional state in real time. The emotion engine uses facial recognition technology and biometric sensors to analyze the user's stress level and other factors. Based on the results, it adjusts the parameters of the color conversion algorithm accordingly. In this way, colors are adjusted to provide the optimal visual experience according to the user's emotional state.

[0265] 4. Returning and displaying video data

[0266] The server then sends the converted video data back to the device, where it is displayed on the glasses in real time. This allows users to easily distinguish colors and enjoy an optimal visual experience tailored to their emotional state.

[0267] Specific examples

[0268] For example, suppose that User A, who has color vision variability, has difficulty distinguishing between red and green and is feeling stressed, and is looking at a garden. In this case, the following processing is performed:

[0269] 1. User A wears the glasses-type device and observes the garden.

[0270] 2. The device's built-in camera captures images of the garden and sends the image data to the server.

[0271] 3. The server applies a color transformation algorithm, converting red flowers to yellow and adjusting the saturation of green leaves.

[0272] 4. The server uses the emotion engine to analyze User A's stress level and adjusts the color to a calmer tone.

[0273] 5. The server returns the converted video data to the terminal, which then displays the video on its screen.

[0274] 6. User A can now easily distinguish colors and experience visual information with reduced stress.

[0275] In this way, the present invention optimizes the visual experience according to the user's color discrimination and emotional state.

[0276] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0277] Step 1:

[0278] The user puts on the glasses-type device and turns it on. The device automatically performs initial settings and the built-in camera starts up. The user's color vision characteristics information is read from the glasses-type device. This initial setting uses a profile based on the user's color vision characteristics.

[0279] Input: User's color vision characteristics information, powering on the glasses-type device

[0280] Output: Default color vision profile, camera startup

[0281] Step 2:

[0282] After the device's built-in camera is activated, it captures the image in the user's field of view in real time. The captured image data is temporarily stored in the device's memory.

[0283] Input: Camera capture of field of view

[0284] Output: Captured video data (stored in memory)

[0285] Step 3:

[0286] The device sends the captured video data to the server using a low-latency communication protocol such as WebSocket.

[0287] Input: Captured video data

[0288] Output: Video data sent to the server

[0289] Step 4:

[0290] The server then applies a color conversion algorithm to the received video data. This algorithm converts certain colors to other colors and adjusts saturation and brightness based on the user's color vision characteristics. For example, it converts red to yellow and adjusts the saturation of green.

[0291] Input: Received video data, color vision profile

[0292] Output: Color converted video data

[0293] Step 5:

[0294] The server recognizes the user's emotional state using an emotion engine, which analyzes data obtained from facial recognition technology and biometric sensors (e.g., heart rate, facial expressions) to determine the user's stress level.

[0295] Input: User's facial recognition data, biometric information

[0296] Output: User's emotional state (e.g., stress level)

[0297] Step 6:

[0298] The server readjusts the parameters of the color conversion algorithm based on the output of the emotion engine. For example, if the user is feeling high stress, the overall color will be changed to a calmer shade.

[0299] Input: Emotion engine output (user's emotional state)

[0300] Output: Re-adjusted color transformation parameters

[0301] Step 7:

[0302] The server then performs color conversion again to generate optimized video data, which is adjusted based on the user's color vision characteristics and emotional state.

[0303] Input: Retuned color transformation parameters

[0304] Output: Optimized video data

[0305] Step 8:

[0306] The server sends optimized video data to the terminal using a low-latency communication protocol.

[0307] Input: Optimized video data

[0308] Output: Video data sent to the device

[0309] Step 9:

[0310] The optimized video data received by the terminal is displayed on the display.

[0311] Input: Optimized video data

[0312] Output: Image displayed on the display

[0313] Step 10:

[0314] By viewing optimized images through the display, users can easily distinguish colors and enjoy an optimal visual experience according to their emotional state.

[0315] Input: Image displayed on the display

[0316] Output: Improved user visual experience, reduced stress

[0317] (Application example 2)

[0318] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0319] Users with color vision variability have difficulty distinguishing colors when selecting products in physical stores, and the experience is further exacerbated by added stress and confusion. Current color vision correction systems also lack real-time performance and are not optimized to take the user's emotional state into account, limiting the user experience. Therefore, a new system that simultaneously considers color vision correction and the user's emotional state is needed.

[0320] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for capturing an image in the user's field of view with a built-in camera, means for transmitting the captured image data to the server in real time, means for applying a color conversion algorithm to the received image data, means for transmitting the image data to which the color conversion algorithm has been applied back to the terminal, and means for optimizing the color conversion algorithm using an emotion engine that recognizes the user's emotion. This makes it easier for users with visual color vision variability to identify products and also makes it possible to provide visual information that is optimized based on the user's emotional state.

[0321] The "built-in camera" is a camera device built into the glasses-type terminal, and is a device for capturing images within the user's field of vision in real time.

[0322] "Video Data" means data containing visual information captured by the built-in camera that is processed and transmitted to enhance the user experience.

[0323] A "server" is a computer system that receives captured video data, applies color transformation algorithms, and further optimizes the data using an emotion engine.

[0324] A "color conversion algorithm" is a program process for converting specific colors in video data into other colors, taking into account color vision variability to make color identification easier.

[0325] The "emotion engine" is a software component that recognizes the user's emotions using facial recognition technology and biometric sensors, and optimizes the system's operation based on that emotional state.

[0326] A "glasses-type terminal" is a device in the shape of glasses that is worn by a user, and is equipped with a built-in camera and a display device for displaying video data in real time.

[0327] "Optimizing" means adjusting system parameters and settings according to emotional state and environment to improve the user experience.

[0328] This invention combines a Color Universal Design (CUD) compatible system using glasses-type terminals to make it easier for users with color vision variability to distinguish colors with an emotion engine that recognizes the user's emotions. Specific embodiments of this invention are described below.

[0329] When a user puts on the glasses and starts using them, the built-in camera activates and begins capturing images of the user's field of vision in real time. The captured image data is temporarily stored on the device and then sent to a server via a low-latency communication protocol.

[0330] The server applies a color conversion algorithm to the received video data, converting specific colors to other colors. This algorithm includes hue conversion, saturation adjustment, and brightness correction, making it easier for users with color vision deficiency to distinguish color differences. The server also uses an emotion engine to recognize the user's emotional state in real time. The emotion engine uses facial recognition technology and biometric sensors to analyze the user's emotions and optimizes the parameters of the color conversion algorithm based on the results.

[0331] As a concrete example, consider a scenario where User A is buying vegetables at a supermarket. User A has difficulty distinguishing between red and green, and is also feeling stressed while shopping. In this case, the system operates as follows:

[0332] 1. User A wears the glasses-type device and observes the vegetable section.

[0333] 2. The device's built-in camera captures video of the sales floor.

[0334] 3. The device sends the captured video data to the server.

[0335] 4. The server receives the video data and applies a color transformation algorithm, for example, converting red vegetables to yellow to make them more recognizable and adjusting the saturation of green vegetables.

[0336] 5. The server uses the emotion engine to recognize User A's emotional state. The emotion engine uses facial recognition technology and biometric sensors to analyze User A's stress level.

[0337] 6. The server optimizes the parameters of the color conversion algorithm based on the output of the emotion engine, for example, converting colors to a gentler tone to reduce stress.

[0338] 7. The server returns the converted video data to the terminal.

[0339] 8. The device displays the converted video data on the screen.

[0340] 9. User A can not only easily distinguish between the highlighted yellow vegetables and the adjusted green vegetables, but also experience optimized visual information.

[0341] The system uses the following main hardware and software:

[0342] Hardware: Smart glasses (e.g., Google® Glass®)

[0343] Software: OpenCV (face recognition), EmotionRecognition (emotion recognition), daltonize (color conversion)

[0344] As an example of a prompt, the following format can be fed into a generative AI model to provide detailed instructions for a specific scenario:

[0345] Imagine a scenario where a user goes to the supermarket and is stressed and has difficulty distinguishing colors. The user is wearing smart glasses, a camera captures the image, an emotion engine recognizes the user's emotions, and a color transformation algorithm optimizes the user's visual experience. Explain in detail how the smart glasses work and what the user's visual experience is.

[0346] In this way, the system of the present invention can provide users with color vision variability with easy color discrimination and an optimal visual experience tailored to their emotional state. The system is highly practical because it provides advanced color correction and emotion recognition functions while being real-time and easy for users to operate.

[0347] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0348] Step 1:

[0349] The user puts on the glasses-type device and begins to use it. The device's built-in camera starts up and begins capturing images in the user's field of vision in real time. The input is the image in the user's field of vision, and the output is the image data captured by the camera. Specifically, the camera captures high-resolution still images or videos and saves them as digital data.

[0350] Step 2:

[0351] The device sends the captured video data to the server. Here, a low-latency communication protocol (e.g., WebSocket or RTSP) is used to send the data quickly. The input is the captured video data, and the output is the video data received by the server. Specifically, the captured data is divided into packets of a fixed size and sent.

[0352] Step 3:

[0353] The server applies a color conversion algorithm to the received video data. The input is the received video data, and the output is the video data with the color conversion applied. Specific data processing involves converting specific colors to other colors that are easier to distinguish, and adjusting saturation and brightness as needed. For example, converting red to yellow and reducing the saturation of green.

[0354] Step 4:

[0355] The server uses an emotion engine to recognize the user's emotional state. The input is the user's facial image and biometric information, and the output is data indicating the user's emotional state (e.g., stress level). Specifically, it acquires data from facial recognition technology and biometric sensors, and estimates emotions using an emotion recognition model (e.g., OpenCV or EmotionRecognition).

[0356] Step 5:

[0357] The server optimizes the parameters of the color conversion algorithm based on the output of the emotion engine. The input is the user's emotional state data and the video data before conversion, and the output is the optimized color conversion parameters. Specifically, the server changes the parameters, such as adjusting the colors more gently when the stress level is high.

[0358] Step 6:

[0359] The server then returns the converted video data to the device. The input is the video data that has been color converted and optimized, and the output is the video data received by the device. Specifically, the data is sent again using a low-latency communication protocol.

[0360] Step 7:

[0361] The terminal displays the received converted video data on the display. The input is the video data received from the server, and the output is the video displayed on the display. In concrete terms, the display device displays the video data, allowing the user to experience visual information.

[0362] Step 8:

[0363] Users can easily see products through images that are color-corrected based on their color vision characteristics, and enjoy a visual experience optimized according to their emotional state. The input is the image displayed on the screen, and the output is the user's visual experience. Specifically, this system makes the process of selecting products in a physical store smoother and reduces stress.

[0364] In this way, the system can help users with color vision variability distinguish colors and provide an optimal visual experience according to the user's emotional state.

[0365] 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.

[0366] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0367] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0368] [Second embodiment]

[0369] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0370] 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.

[0371] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0372] 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.

[0373] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0374] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0375] 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. 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.

[0376] 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.

[0377] 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 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.

[0378] 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.

[0379] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0380] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0381] This invention is a Color Universal Design (CUD) compatible system using glasses-type devices to facilitate color discrimination for users with color vision variability. This system is realized by capturing images in the field of view with a built-in camera, sending the images to a server for color conversion, and displaying the converted images in real time.

[0382] Operation overview

[0383] The user puts on the glasses-type device and starts using it. After putting it on, the camera inside the device starts up and begins capturing images in the user's field of view in real time. The captured image data is temporarily stored in the device.

[0384] The device sends the captured video data to the server using a low-latency communication protocol, enabling real-time processing.

[0385] The server then applies a color conversion algorithm to the received video data. This algorithm converts certain colors into other colors that are easier to distinguish based on the user's color vision characteristics. For example, if a user has difficulty distinguishing between red and green, the algorithm converts red into yellow. It also adjusts saturation and brightness to create an image that is easier to distinguish visually.

[0386] The converted image data is sent back to the terminal from the server, and the terminal displays the received converted image data on the display in real time. Through this converted image, the user can easily distinguish colors and perform daily life and specific tasks comfortably.

[0387] Specific examples

[0388] For example, suppose user A has difficulty distinguishing between red and green and is looking at a garden full of flowers and leaves. The system works as follows:

[0389] 1. User A wears the glasses-type device and observes the garden.

[0390] 2. The device's built-in camera captures footage of the garden.

[0391] 3. The device sends the captured video data to the server.

[0392] 4. The server receives the video data and applies a color transformation algorithm based on color vision characteristics, converting red flowers to a more easily distinguishable yellow and adjusting the saturation of green leaves.

[0393] 5. The server returns the converted video data to the device.

[0394] 6. The device displays the converted video data on the screen.

[0395] 7. User A can now clearly distinguish the image of red flowers converted to yellow from green leaves.

[0396] In this way, the system of the present invention makes it easier for users with color vision variability to distinguish colors and improves convenience in their daily lives. The system is highly practical because it is capable of real-time operation, provides advanced color correction functions while being easy for users to operate.

[0397] The processing flow will be explained below.

[0398] Step 1:

[0399] The user wears the glasses-type device and observes the images in their field of vision. Any user input or setting changes are made at this point. The device is powered on and the camera and communication module are activated.

[0400] Step 2:

[0401] The device captures the user's field of view in real time through the built-in camera, which captures video frames at a constant frame rate and stores them in the device's internal memory.

[0402] Step 3:

[0403] The device splits the captured video data into small data packets over an internet connection, which contain timestamps and camera metadata (resolution, frame rate, etc.).

[0404] Step 4:

[0405] The device sends the divided data packets to the server's API endpoint, using a low-latency communication protocol (e.g., WebSocket) to maintain real-time performance.

[0406] Step 5:

[0407] The server receives the data packets sent from the terminal, reconstructs the received packets, and decodes them into the original video frames.

[0408] Step 6:

[0409] The server applies a color transformation algorithm to the decoded video frames, which converts certain colors into other, more easily distinguishable colors and adjusts saturation and brightness as needed.

[0410] Step 7:

[0411] The server then divides the transformed video frames, which are generated as a result of the color transformation algorithm, into data packets again, along with metadata about the transformation process.

[0412] Step 8:

[0413] The server then sends the converted video data packets back to the terminal, again using a low-latency communication protocol.

[0414] Step 9:

[0415] The terminal receives the converted video data packets, reconcatenates the received packets, and reconstructs the converted video frames.

[0416] Step 10:

[0417] The device then reconstructs the converted video frames and displays them in real time on the glasses-type device's display, with the display process optimized to avoid interfering with the user's visual field.

[0418] Step 11:

[0419] The user experiences color vision-compatible visual information in real time through the converted image. If the user provides feedback or changes to settings, the system will accept them appropriately and repeat the process.

[0420] Example 1

[0421] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0422] Current visual aids and software lack effective means for users with color vision variability to easily distinguish colors in their daily lives. In particular, conventional methods have difficulty converting and displaying colors in real time. Therefore, in order to improve the quality of life for users with color vision variability, the development of a new system that can convert and display colors clearly in real time is required.

[0423] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0424] In this invention, the server includes: a means for a user wearing a glasses-type terminal and capturing an image in the user's field of view in real time with a built-in camera; a means for transmitting the captured image data to the server using a low-latency communication protocol; a server means for applying a color conversion algorithm based on the user's color vision characteristics to the received image data; a means for compressing the image data to which the color conversion algorithm has been applied with a high-speed encoder and transmitting it back to the terminal in real time; and a means for decoding the converted image data and displaying it on the display of the user's glasses-type terminal. This enables a system in which users with visual color vision variability can convert and display colors in real time, making it easy to distinguish colors.

[0425] "User" refers to any individual who uses the system, and is particularly intended for people with visual color vision variability.

[0426] A "glasses-type terminal" is a glasses-type device worn by a user, which has a built-in camera and a display, and is a device for capturing and displaying visual information.

[0427] The "built-in camera" is a camera built into the glasses-type device, and serves to capture images within the user's field of vision.

[0428] "Capture" refers to the action of capturing images within the field of view using the built-in camera and saving them as data.

[0429] "Video Data" refers to data that digitally represents the image of the field of view captured by the built-in camera.

[0430] A "low-latency communication protocol" is a communication method for quickly sending and receiving data, and is used to achieve real-time processing.

[0431] A "server" is a computer system installed on a network that receives and processes data sent from multiple clients.

[0432] A "color conversion algorithm" is a calculation method for converting color information contained in video data to make it easier for users with specific color vision characteristics to distinguish.

[0433] A "high-speed encoder" is a device or software for compressing digital data that can perform data compression at high speeds.

[0434] The "display" is an image display device mounted on the glasses-type terminal, and serves to visually present color-converted image data to the user.

[0435] "Decoding" refers to the process of restoring compressed data to its original form.

[0436] "Color vision characteristics" refers to the color discrimination ability and type of color vision deficiency of each individual user, and is the information that forms the basis of the color conversion applied by the system.

[0437] This invention is a Color Universal Design (CUD) compatible system using glasses-type devices that allows users with color vision variability to easily distinguish colors so that they can comfortably perform daily activities and specific tasks. This system captures images in the field of view with a built-in camera, sends the images to a server for color conversion, and displays the converted images in real time, making it easier for users to recognize color differences.

[0438] Hardware and software used

[0439] Glasses: A device with a built-in camera and display that is worn by the user and captures video of the user's field of vision.

[0440] Built-in camera: A camera installed in the glasses-type device that captures images of the field of view in real time.

[0441] Server: A computer system for receiving video data and applying color transformation algorithms.

[0442] Communication protocol: A low-latency communication protocol (e.g., WebSocket, HTTP / 2) is used to send and receive video data in real time.

[0443] Color conversion algorithm: A calculation method for converting the color information contained in video data and converting specific colors into other, more easily distinguishable colors.

[0444] High-Speed ​​Encoder: A device or software for compressing digital data at high speed.

[0445] Display: A device installed in the glasses-type device for displaying the converted video data.

[0446] Decoder: A device or software that converts compressed data back into its original form.

[0447] Operational Overview

[0448] The user puts on the glasses and starts using them. The camera inside the device activates and captures the user's field of view in real time. The captured video data is temporarily stored in the device and then sent to a server using a low-latency communication protocol.

[0449] The server temporarily stores the received video data in a buffer and applies a color conversion algorithm based on the user's color vision characteristics. This algorithm converts certain colors into more easily distinguishable colors and adjusts saturation and brightness to create a visually more easily distinguishable image. The converted video data is then compressed using a high-speed encoder and sent back to the device in real time.

[0450] The device decodes the compressed video data and displays it on the built-in display. This allows users to more easily distinguish colors through the converted video. For example, for users who have difficulty distinguishing between red and green, red flowers will be converted to yellow and the saturation of green leaves will be enhanced, making them easier to distinguish.

[0451] Specific examples

[0452] For example, suppose that User A has difficulty distinguishing between red and green and is observing a garden. In this case, the system operates as follows:

[0453] 1. User A wears the glasses-type device and observes the garden.

[0454] 2. The camera inside the device captures images of the garden in real time.

[0455] 3. The captured video data is sent to the server using a low-latency communication protocol.

[0456] 4. The server receives the video data and applies a color transformation algorithm based on User A's color vision characteristics, converting red flowers to yellow and adjusting the saturation of green leaves.

[0457] 5. The server compresses the converted video data using a high-speed encoder and sends it back to the terminal.

[0458] 6. The device decodes the compressed video data and displays it on the screen.

[0459] 7. User A can easily distinguish the red flowers (converted to yellow) and green leaves through the converted image.

[0460] Example prompts for generative AI models

[0461] "User A, who has difficulty distinguishing between red and green, is wearing a glasses-type device and observing a garden. Please explain in detail the steps involved in capturing video data, sending it to the server, converting the color, retransmitting it to the device, and displaying it."

[0462] By inputting this prompt into a generative AI model, detailed operational steps are explained.

[0463] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0464] Step 1:

[0465] The user puts on the glasses and activates the built-in camera. The camera captures the image of the user's field of vision in real time. The image data is captured at a rate of 30 frames per second. The input is the scene coming into the user's field of vision, and the output is digital image data.

[0466] Step 2:

[0467] The device temporarily stores the captured video data in its internal memory. At the same time, it transmits the captured video data to the server in real time using a low-latency communication protocol (e.g., WebSocket). The input is the captured video data, and the output is the video data as communication packets.

[0468] Step 3:

[0469] The server receives video data sent from the device. The server temporarily stores the received video data in a buffer and applies a color conversion algorithm based on the user's color vision characteristics. Specifically, if it is difficult to distinguish between red and green, the server converts red to yellow and emphasizes the saturation of green. The input is unconverted video data, and the output is color-converted video data.

[0470] Step 4:

[0471] The server compresses the color-converted video data using a high-speed encoder. Compression reduces the amount of data and improves communication speed. The input is color-converted video data, and the output is compressed data.

[0472] Step 5:

[0473] The server then sends the compressed video data back to the terminal using a low-latency communication protocol. The input is compressed video data, and the output is compressed data as communication packets.

[0474] Step 6:

[0475] The terminal receives the compressed data sent from the server. The terminal uses a decoder to decode the compressed data and return it to its original format. The input is the compressed video data, and the output is the decoded video data.

[0476] Step 7:

[0477] The device displays the decoded video data on the built-in display. The real-time converted video allows the user to easily distinguish colors and clearly recognize objects and colors within their field of view. The input is the decoded video data, and the output is the video displayed on the display.

[0478] (Application example 1)

[0479] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0480] Users with color vision variability often have difficulty identifying products while shopping in physical stores. This is particularly inconvenient when the color of product labels or packaging contains important information. To solve this problem, a system is needed that can correct color vision in real time to compensate for visual impairments.

[0481] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0482] In this invention, the server includes means for capturing an image in the user's field of view with a built-in camera, means for transmitting the captured image data to the server in real time, means for applying a color conversion algorithm to the received image data, means for transmitting the image data to which the color conversion algorithm has been applied back to the terminal, means for displaying the converted image on the display of the user's eyeglass-type terminal, and means for supporting shopping in a physical store using a communication protocol, thereby enabling users with visual color vision variability to easily identify products in a physical store.

[0483] The "built-in camera" is a camera device built into the glasses-type terminal, which captures images within the user's field of vision.

[0484] "Captured video data" refers to visual information captured by the built-in camera that is saved or temporarily stored as digital data.

[0485] "Means for transmitting to a server in real time" refers to a communication method for transmitting video data captured by the built-in camera to a server without delay.

[0486] The "server means" is a central processing unit that processes received video data using various algorithms to generate converted data.

[0487] "Color conversion algorithm" refers to a calculation method and program for converting a specific color into another color so that users with color vision deficiency can easily recognize the color difference.

[0488] The "means for sending back to the terminal" is a communication method for returning data processed by the server back to the glasses-type terminal.

[0489] "Converted image" refers to image data that has undergone color correction through the application of a color conversion algorithm.

[0490] An "eyeglasses-type terminal" is an eyeglass-type electronic device that has a display and a built-in camera and provides visual assistance functions when worn by the user.

[0491] A "communication protocol" is a protocol that defines the rules and procedures for data communication, and in this case refers to a protocol that achieves low-latency communication.

[0492] "Means to support shopping in physical stores" is a visual aid system that makes it easier for users to select products in physical stores.

[0493] "Python" is a high-level programming language used to implement color conversion algorithms on the server.

[0494] "OpenCV" is an open source library for efficient image processing and is used for color conversion algorithms.

[0495] This invention is a Color Universal Design (CUD)-compliant system that allows users with color vision variability to easily distinguish colors. This system mainly utilizes glasses-type terminals and a server, and aims to improve the shopping experience in physical stores.

[0496] Operation overview

[0497] 1. User Action:

[0498] A user puts on the glasses-type device in a physical store where they are shopping, and the device begins capturing images of what is in the user's field of view using its built-in camera.

[0499] 2. Terminal processing:

[0500] The built-in camera in the glasses captures video and temporarily stores the data in real time. The device then sends the stored video data to a server via a communication protocol. The use of a low-latency communication protocol (e.g., WebSocket) enables real-time data transfer.

[0501] 3. Server processing:

[0502] The server then applies a color conversion algorithm using Python and OpenCV to the received video data. This algorithm converts certain colors into other colors that are easier to distinguish depending on the user's color vision characteristics. For example, if a user has difficulty distinguishing between red and green, red will be converted to yellow.

[0503] 4. Return to device:

[0504] The color-converted video data is sent back from the server to the terminal and displayed in real time on the display of the glasses-type terminal.

[0505] Hardware and Software

[0506] Glasses-type device: Equipped with a built-in camera, display, and communication module.

[0507] Server: A central processing unit that processes video data and applies color transformation algorithms.

[0508] Communication protocol: Uses low-latency WebSocket for data transfer.

[0509] Software: Python and OpenCV are used for video processing on the server.

[0510] Specific examples

[0511] For example, assume that user A has color vision that makes it difficult to distinguish between red and green, and is looking for a red product label in a physical store.

[0512] 1. User A puts on the glasses-type device and looks at the product shelves.

[0513] 2. The device's built-in camera captures images of the shelves and sends the data to the server.

[0514] 3. The server applies a color conversion algorithm to the received video data, converting the red label to yellow.

[0515] 4. The server returns the converted video data to the terminal and displays it on the display in real time.

[0516] 5. User A can identify the label that has been converted to yellow, making it easier for them to find the product they are looking for.

[0517] Prompt Sentence Examples

[0518] Prompt: Describe a system in which a color vision assistant transforms red labels into yellow in a brick-and-mortar store, making it easier for users to identify products.

[0519] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0520] Step 1:

[0521] A user puts on the glasses-type device and starts shopping in a physical store. The built-in camera captures the image of the user's field of view. The input is the image scene in the user's field of view, and the output is the captured image data.

[0522] Step 2:

[0523] The device temporarily stores the captured video data and sends it to the server via a communication protocol (e.g., WebSocket). The input is the captured video data, and the output is the video data sent to the server.

[0524] Step 3:

[0525] The server applies a color conversion algorithm to the received video data using Python and OpenCV. The specific data processing performed here is to convert colors into colors that are easy to recognize for people with color vision deficiency. The input is the original video data received by the server, and the output is the color-converted video data.

[0526] Step 4:

[0527] The server then sends the image data to which the color conversion algorithm has been applied back to the terminal via the communication protocol. The input is the color-converted image data, and the output is the image data sent from the server to the terminal.

[0528] Step 5:

[0529] The terminal displays the received color-converted video data on a display in real time. The input is the color-converted video data, and the output is the converted video that is visually displayed to the user.

[0530] Step 6:

[0531] The user can then use the converted image to identify the more easily distinguishable colors and search for the product. Specifically, the user identifies the red label, which has been converted to yellow, on the display of the glasses-type device and finds the product they need.

[0532] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0533] This invention combines a Color Universal Design (CUD)-compatible system using glasses-type devices to facilitate color discrimination for users with color vision variability with an emotion engine that recognizes the user's emotions. This system captures images within the field of view with a built-in camera, transmits the images to a server for color conversion, and displays the converted images in real time. The system also recognizes the user's emotions and optimizes the color conversion algorithm based on the user's emotional state, further improving the user experience.

[0534] Operation overview

[0535] The user puts on the glasses-type device and starts using it. After putting it on, the camera inside the device starts up and begins capturing images in the user's field of view in real time. The captured image data is temporarily stored in the device.

[0536] The device sends the captured video data to the server using a low-latency communication protocol, ensuring real-time transmission.

[0537] The server then runs the received video data through a color conversion algorithm, which converts specific colors into others that are easier to recognize, adjusting saturation and brightness as needed. The server then uses an emotion engine to recognize the user's emotional state. The emotion engine uses facial recognition technology and biometric sensors to analyze the user's emotions in real time.

[0538] Based on the analysis results, the parameters of the color conversion algorithm are adjusted. For example, if the user is feeling stressed, the colors are adjusted to reduce stress. By emphasizing or suppressing certain colors, the user's mental state is optimized.

[0539] The converted image data is then sent back to the device, which then displays it in real time. This allows users to more easily distinguish colors and enjoy a visual experience tailored to their emotional state.

[0540] Specific examples

[0541] For example, suppose that User A has difficulty distinguishing between red and green, and is viewing a garden full of flowers and leaves in a stressful situation. The system works as follows:

[0542] 1. User A wears the glasses-type device and observes the garden.

[0543] 2. The device's built-in camera captures footage of the garden.

[0544] 3. The device sends the captured video data to the server.

[0545] 4. The server receives the video data and applies a color conversion algorithm based on color vision characteristics. This algorithm converts red flowers to a more easily distinguishable yellow and adjusts the saturation of green leaves.

[0546] 5. The server uses the emotion engine to recognize User A's emotional state. The emotion engine uses facial recognition technology and biometric sensors to analyze User A's stress level in real time.

[0547] 6. The server adjusts the parameters of the color transformation algorithm based on the output of the emotion engine, for example, emphasizing calm colors to reduce stress.

[0548] 7. The server returns the converted video data to the terminal.

[0549] 8. The device displays the converted video data on the screen.

[0550] 9. User A can not only easily distinguish between the yellow flowers and the adjusted green leaves, but also experience visual information optimized to reduce stress.

[0551] In this way, the system of the present invention facilitates color discrimination for users with color vision variability and provides an optimal visual experience based on their emotional state. The system is highly practical because it provides advanced color correction and emotion recognition capabilities while being real-time and easy for users to operate.

[0552] The processing flow will be explained below.

[0553] Step 1:

[0554] The user wears the glasses-type device and observes the images in their field of vision. Any user input or setting changes are made at this point. The device is powered on and the camera and communication module are activated.

[0555] Step 2:

[0556] The device captures the user's field of view in real time through the built-in camera, which captures video frames at a constant frame rate and stores them in the device's internal memory.

[0557] Step 3:

[0558] The device analyzes the video data captured by the device to detect the user's face, and uses facial recognition technology to estimate the user's emotional state and generate emotion data.

[0559] Step 4:

[0560] The device divides the video data and emotion data into packets and adds timestamps and additional metadata (resolution, frame rate, emotional state, etc.).

[0561] Step 5:

[0562] The device sends the divided data packets to the server's API endpoint in real time using a low-latency communication protocol (e.g., WebSocket).

[0563] Step 6:

[0564] The server receives the data packets sent from the device, reconstructs the received packets, and decodes them into the original video frames and emotion data.

[0565] Step 7:

[0566] The server applies a color transformation algorithm to the decoded video frames, which converts certain colors into other, more easily distinguishable colors and adjusts saturation and brightness as needed.

[0567] Step 8:

[0568] The server dynamically adjusts the parameters of the color conversion algorithm based on the emotional data it recognizes. For example, if the user is feeling stressed, the colors will be changed to a more gentle tone.

[0569] Step 9:

[0570] The server re-divides the converted video data into data packets and also adds metadata about the conversion process.

[0571] Step 10:

[0572] The server then sends the converted video data packets back to the terminal, again using a low-latency communication protocol.

[0573] Step 11:

[0574] The terminal receives the converted video data packets, reconcatenates the received packets, and reconstructs the converted video frames.

[0575] Step 12:

[0576] The device then reconstructs the converted video frames and displays them in real time on the glasses-type device's display, with the display process optimized to avoid interfering with the user's visual field.

[0577] Step 13:

[0578] The user experiences visual information adapted to color vision variability in real time through the converted image. Additionally, the user's mental state is also optimized by providing optimized visual information based on their emotional state. If the user provides feedback or additional setting changes, the system accepts them appropriately and repeats the process.

[0579] Example 2

[0580] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0581] Users with color vision variability often have difficulty distinguishing certain colors in daily life, which can cause stress and anxiety in certain situations. To solve this problem, a system is needed that makes color distinction easier and provides a visual experience optimized according to the user's emotional state.

[0582] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for capturing an image in the user's field of view with a built-in camera, means for transmitting the captured image data to the server in real time, means for applying a color conversion algorithm to the received image data, means for recognizing the user's emotional state using an emotion engine, means for adjusting parameters of the color conversion algorithm based on the output of the emotion engine, means for transmitting the image data to which the color conversion algorithm has been applied back to the terminal, and means for displaying the converted image on the display of the user's eyeglass-type terminal. This not only makes it easier to distinguish colors, but also makes it possible to provide an optimal visual experience according to the user's emotional state.

[0583] The "built-in camera" is a camera device built into the glasses-type terminal, and serves to capture images within the user's field of vision in real time.

[0584] "Captured video data" refers to digital data of video captured by the built-in camera, which is temporarily stored in the terminal and then sent to the server.

[0585] "Real-time transmission" refers to the process of transmitting captured video data to a server immediately with minimal delay, primarily using low-latency communication protocols.

[0586] The "server means" refers to the function of the server that processes the received video data, and specifically includes the application of a color conversion algorithm and emotion recognition using an emotion engine.

[0587] A "color conversion algorithm" is a series of processing steps that converts a specific color into another color based on the user's color vision characteristics and adjusts the saturation and brightness.

[0588] The "emotion engine" is a system that uses facial recognition technology and biometric sensors to recognize the user's emotional state in real time, and optimizes processing based on that output.

[0589] "Emotion recognition" is the process of determining a user's current emotional state from their facial expressions and biometric information using an emotion engine.

[0590] The "display" is a display device mounted on the glasses-type terminal, and serves to allow the user to view the converted video data sent from the server.

[0591] This invention is a Color Universal Design (CUD)-compatible system that combines a glasses-type device that allows users with color vision variability to easily distinguish colors with an emotion engine that recognizes the user's emotions. The system captures images in the field of view with a built-in camera, transmits the images to a server for color conversion, and displays the converted images in real time. The system also recognizes the user's emotions and optimizes the color conversion algorithm based on the user's emotional state, further improving the user experience.

[0592] Hardware and software configuration used

[0593] 1. Glasses-type device

[0594] Built-in camera: Captures what is in the user's field of view.

[0595] Display: Shows the converted video to the user.

[0596] Communication module: Sends video data to the server and receives data from the server.

[0597] 2. Server

[0598] Color conversion algorithm: Converts the color of the video data based on the user's color vision characteristics and adjusts saturation and brightness.

[0599] Emotion Engine: Recognizes user emotions in real time and adjusts the parameters of the color conversion algorithm.

[0600] Communication protocol: Send and receive video data with low latency (e.g., WebSocket).

[0601] Data processing and calculation

[0602] 1. Capture and send

[0603] The user puts on the glasses and starts using it. The built-in camera activates and captures the user's field of view in real time. The captured video data is temporarily stored in the device and then sent to a server using a low-latency communication protocol.

[0604] 2. Applying color transformation

[0605] The server applies a color conversion algorithm to the received video data. This algorithm converts certain colors into other colors and adjusts saturation and brightness to suit the user's color vision characteristics. For example, it may change red to a more easily distinguishable yellow.

[0606] 3. Emotion Recognition and Algorithm Adjustment

[0607] The server uses an emotion engine to recognize the user's emotional state in real time. The emotion engine uses facial recognition technology and biometric sensors to analyze the user's stress level and other factors. Based on the results, it adjusts the parameters of the color conversion algorithm accordingly. In this way, colors are adjusted to provide the optimal visual experience according to the user's emotional state.

[0608] 4. Returning and displaying video data

[0609] The server then sends the converted video data back to the device, where it is displayed on the glasses in real time. This allows users to easily distinguish colors and enjoy an optimal visual experience tailored to their emotional state.

[0610] Specific examples

[0611] For example, suppose that User A, who has color vision variability, has difficulty distinguishing between red and green and is feeling stressed, and is looking at a garden. In this case, the following processing is performed:

[0612] 1. User A wears the glasses-type device and observes the garden.

[0613] 2. The device's built-in camera captures images of the garden and sends the image data to the server.

[0614] 3. The server applies a color transformation algorithm, converting red flowers to yellow and adjusting the saturation of green leaves.

[0615] 4. The server uses the emotion engine to analyze User A's stress level and adjusts the color to a calmer tone.

[0616] 5. The server returns the converted video data to the terminal, which then displays the video on its screen.

[0617] 6. User A can now easily distinguish colors and experience visual information with reduced stress.

[0618] In this way, the present invention optimizes the visual experience according to the user's color discrimination and emotional state.

[0619] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0620] Step 1:

[0621] The user puts on the glasses-type device and turns it on. The device automatically performs initial settings and the built-in camera starts up. The user's color vision characteristics information is read from the glasses-type device. This initial setting uses a profile based on the user's color vision characteristics.

[0622] Input: User's color vision characteristics information, powering on the glasses-type device

[0623] Output: Default color vision profile, camera startup

[0624] Step 2:

[0625] After the device's built-in camera is activated, it captures the image in the user's field of view in real time. The captured image data is temporarily stored in the device's memory.

[0626] Input: Camera capture of field of view

[0627] Output: Captured video data (stored in memory)

[0628] Step 3:

[0629] The device sends the captured video data to the server using a low-latency communication protocol such as WebSocket.

[0630] Input: Captured video data

[0631] Output: Video data sent to the server

[0632] Step 4:

[0633] The server then applies a color conversion algorithm to the received video data. This algorithm converts certain colors to other colors and adjusts saturation and brightness based on the user's color vision characteristics. For example, it converts red to yellow and adjusts the saturation of green.

[0634] Input: Received video data, color vision profile

[0635] Output: Color converted video data

[0636] Step 5:

[0637] The server recognizes the user's emotional state using an emotion engine, which analyzes data obtained from facial recognition technology and biometric sensors (e.g., heart rate, facial expressions) to determine the user's stress level.

[0638] Input: User's facial recognition data, biometric information

[0639] Output: User's emotional state (e.g., stress level)

[0640] Step 6:

[0641] The server readjusts the parameters of the color conversion algorithm based on the output of the emotion engine. For example, if the user is feeling high stress, the overall color will be changed to a calmer shade.

[0642] Input: Emotion engine output (user's emotional state)

[0643] Output: Re-adjusted color transformation parameters

[0644] Step 7:

[0645] The server then performs color conversion again to generate optimized video data, which is adjusted based on the user's color vision characteristics and emotional state.

[0646] Input: Retuned color transformation parameters

[0647] Output: Optimized video data

[0648] Step 8:

[0649] The server sends optimized video data to the terminal using a low-latency communication protocol.

[0650] Input: Optimized video data

[0651] Output: Video data sent to the device

[0652] Step 9:

[0653] The optimized video data received by the terminal is displayed on the display.

[0654] Input: Optimized video data

[0655] Output: Image displayed on the display

[0656] Step 10:

[0657] By viewing optimized images through the display, users can easily distinguish colors and enjoy an optimal visual experience according to their emotional state.

[0658] Input: Image displayed on the display

[0659] Output: Improved user visual experience, reduced stress

[0660] (Application example 2)

[0661] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0662] Users with color vision variability have difficulty distinguishing colors when selecting products in physical stores, and the experience is further exacerbated by added stress and confusion. Current color vision correction systems also lack real-time performance and are not optimized to take the user's emotional state into account, limiting the user experience. Therefore, a new system that simultaneously considers color vision correction and the user's emotional state is needed.

[0663] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for capturing an image in the user's field of view with a built-in camera, means for transmitting the captured image data to the server in real time, means for applying a color conversion algorithm to the received image data, means for transmitting the image data to which the color conversion algorithm has been applied back to the terminal, and means for optimizing the color conversion algorithm using an emotion engine that recognizes the user's emotion. This makes it easier for users with visual color vision variability to identify products and also makes it possible to provide visual information that is optimized based on the user's emotional state.

[0664] The "built-in camera" is a camera device built into the glasses-type terminal, and is a device for capturing images within the user's field of vision in real time.

[0665] "Video Data" means data containing visual information captured by the built-in camera that is processed and transmitted to enhance the user experience.

[0666] A "server" is a computer system that receives captured video data, applies color transformation algorithms, and further optimizes the data using an emotion engine.

[0667] A "color conversion algorithm" is a program process for converting specific colors in video data into other colors, taking into account color vision variability to make color identification easier.

[0668] The "emotion engine" is a software component that recognizes the user's emotions using facial recognition technology and biometric sensors, and optimizes the system's operation based on that emotional state.

[0669] A "glasses-type terminal" is a device in the shape of glasses that is worn by a user, and is equipped with a built-in camera and a display device for displaying video data in real time.

[0670] "Optimizing" means adjusting system parameters and settings according to emotional state and environment to improve the user experience.

[0671] This invention combines a Color Universal Design (CUD) compatible system using glasses-type terminals to make it easier for users with color vision variability to distinguish colors with an emotion engine that recognizes the user's emotions. Specific embodiments of this invention are described below.

[0672] When a user puts on the glasses and starts using them, the built-in camera activates and begins capturing images of the user's field of vision in real time. The captured image data is temporarily stored on the device and then sent to a server via a low-latency communication protocol.

[0673] The server applies a color conversion algorithm to the received video data, converting specific colors to other colors. This algorithm includes hue conversion, saturation adjustment, and brightness correction, making it easier for users with color vision deficiency to distinguish color differences. The server also uses an emotion engine to recognize the user's emotional state in real time. The emotion engine uses facial recognition technology and biometric sensors to analyze the user's emotions and optimizes the parameters of the color conversion algorithm based on the results.

[0674] As a concrete example, consider a scenario where User A is buying vegetables at a supermarket. User A has difficulty distinguishing between red and green, and is also feeling stressed while shopping. In this case, the system operates as follows:

[0675] 1. User A wears the glasses-type device and observes the vegetable section.

[0676] 2. The device's built-in camera captures video of the sales floor.

[0677] 3. The device sends the captured video data to the server.

[0678] 4. The server receives the video data and applies a color transformation algorithm, for example, converting red vegetables to yellow to make them more recognizable and adjusting the saturation of green vegetables.

[0679] 5. The server uses the emotion engine to recognize User A's emotional state. The emotion engine uses facial recognition technology and biometric sensors to analyze User A's stress level.

[0680] 6. The server optimizes the parameters of the color conversion algorithm based on the output of the emotion engine, for example, converting colors to a gentler tone to reduce stress.

[0681] 7. The server returns the converted video data to the terminal.

[0682] 8. The device displays the converted video data on the screen.

[0683] 9. User A can not only easily distinguish between the highlighted yellow vegetables and the adjusted green vegetables, but also experience optimized visual information.

[0684] The system uses the following main hardware and software:

[0685] Hardware: Smart glasses (e.g., Google Glass)

[0686] Software: OpenCV (face recognition), EmotionRecognition (emotion recognition), daltonize (color conversion)

[0687] As an example of a prompt, the following format can be fed into a generative AI model to provide detailed instructions for a specific scenario:

[0688] Imagine a scenario where a user goes to the supermarket and is stressed and has difficulty distinguishing colors. The user is wearing smart glasses, a camera captures the image, an emotion engine recognizes the user's emotions, and a color transformation algorithm optimizes the user's visual experience. Explain in detail how the smart glasses work and what the user's visual experience is.

[0689] In this way, the system of the present invention can provide users with color vision variability with easy color discrimination and an optimal visual experience tailored to their emotional state. The system is highly practical because it provides advanced color correction and emotion recognition functions while being real-time and easy for users to operate.

[0690] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0691] Step 1:

[0692] The user puts on the glasses-type device and begins to use it. The device's built-in camera starts up and begins capturing images in the user's field of vision in real time. The input is the image in the user's field of vision, and the output is the image data captured by the camera. Specifically, the camera captures high-resolution still images or videos and saves them as digital data.

[0693] Step 2:

[0694] The device sends the captured video data to the server. Here, a low-latency communication protocol (e.g., WebSocket or RTSP) is used to send the data quickly. The input is the captured video data, and the output is the video data received by the server. Specifically, the captured data is divided into packets of a fixed size and sent.

[0695] Step 3:

[0696] The server applies a color conversion algorithm to the received video data. The input is the received video data, and the output is the video data with the color conversion applied. Specific data processing involves converting specific colors to other colors that are easier to distinguish, and adjusting saturation and brightness as needed. For example, converting red to yellow and reducing the saturation of green.

[0697] Step 4:

[0698] The server uses an emotion engine to recognize the user's emotional state. The input is the user's facial image and biometric information, and the output is data indicating the user's emotional state (e.g., stress level). Specifically, it acquires data from facial recognition technology and biometric sensors, and estimates emotions using an emotion recognition model (e.g., OpenCV or EmotionRecognition).

[0699] Step 5:

[0700] The server optimizes the parameters of the color conversion algorithm based on the output of the emotion engine. The input is the user's emotional state data and the video data before conversion, and the output is the optimized color conversion parameters. Specifically, the server changes the parameters, such as adjusting the colors more gently when the stress level is high.

[0701] Step 6:

[0702] The server then returns the converted video data to the device. The input is the video data that has been color converted and optimized, and the output is the video data received by the device. Specifically, the data is sent again using a low-latency communication protocol.

[0703] Step 7:

[0704] The terminal displays the received converted video data on the display. The input is the video data received from the server, and the output is the video displayed on the display. In concrete terms, the display device displays the video data, allowing the user to experience visual information.

[0705] Step 8:

[0706] Users can easily see products through images that are color-corrected based on their color vision characteristics, and enjoy a visual experience optimized according to their emotional state. The input is the image displayed on the screen, and the output is the user's visual experience. Specifically, this system makes the process of selecting products in a physical store smoother and reduces stress.

[0707] In this way, the system can help users with color vision variability distinguish colors and provide an optimal visual experience according to the user's emotional state.

[0708] 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.

[0709] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0710] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0711] [Third embodiment]

[0712] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0713] 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.

[0714] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0715] 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.

[0716] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0717] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0718] 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. 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.

[0719] 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.

[0720] 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 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.

[0721] 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.

[0722] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0723] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0724] This invention is a Color Universal Design (CUD) compatible system using glasses-type devices to facilitate color discrimination for users with color vision variability. This system is realized by capturing images in the field of view with a built-in camera, sending the images to a server for color conversion, and displaying the converted images in real time.

[0725] Operation overview

[0726] The user puts on the glasses-type device and starts using it. After putting it on, the camera inside the device starts up and begins capturing images in the user's field of view in real time. The captured image data is temporarily stored in the device.

[0727] The device sends the captured video data to the server using a low-latency communication protocol, enabling real-time processing.

[0728] The server then applies a color conversion algorithm to the received video data. This algorithm converts certain colors into other colors that are easier to distinguish based on the user's color vision characteristics. For example, if a user has difficulty distinguishing between red and green, the algorithm converts red into yellow. It also adjusts saturation and brightness to create an image that is easier to distinguish visually.

[0729] The converted image data is sent back to the terminal from the server, and the terminal displays the received converted image data on the display in real time. Through this converted image, the user can easily distinguish colors and perform daily life and specific tasks comfortably.

[0730] Specific examples

[0731] For example, suppose user A has difficulty distinguishing between red and green and is looking at a garden full of flowers and leaves. The system works as follows:

[0732] 1. User A wears the glasses-type device and observes the garden.

[0733] 2. The device's built-in camera captures footage of the garden.

[0734] 3. The device sends the captured video data to the server.

[0735] 4. The server receives the video data and applies a color transformation algorithm based on color vision characteristics, converting red flowers to a more easily distinguishable yellow and adjusting the saturation of green leaves.

[0736] 5. The server returns the converted video data to the device.

[0737] 6. The device displays the converted video data on the screen.

[0738] 7. User A can now clearly distinguish the image of red flowers converted to yellow from green leaves.

[0739] In this way, the system of the present invention makes it easier for users with color vision variability to distinguish colors and improves convenience in their daily lives. The system is highly practical because it is capable of real-time operation, provides advanced color correction functions while being easy for users to operate.

[0740] The processing flow will be explained below.

[0741] Step 1:

[0742] The user wears the glasses-type device and observes the images in their field of vision. Any user input or setting changes are made at this point. The device is powered on and the camera and communication module are activated.

[0743] Step 2:

[0744] The device captures the user's field of view in real time through the built-in camera, which captures video frames at a constant frame rate and stores them in the device's internal memory.

[0745] Step 3:

[0746] The device splits the captured video data into small data packets over an internet connection, which contain timestamps and camera metadata (resolution, frame rate, etc.).

[0747] Step 4:

[0748] The device sends the divided data packets to the server's API endpoint, using a low-latency communication protocol (e.g., WebSocket) to maintain real-time performance.

[0749] Step 5:

[0750] The server receives the data packets sent from the terminal, reconstructs the received packets, and decodes them into the original video frames.

[0751] Step 6:

[0752] The server applies a color transformation algorithm to the decoded video frames, which converts certain colors into other, more easily distinguishable colors and adjusts saturation and brightness as needed.

[0753] Step 7:

[0754] The server then divides the transformed video frames, which are generated as a result of the color transformation algorithm, into data packets again, along with metadata about the transformation process.

[0755] Step 8:

[0756] The server then sends the converted video data packets back to the terminal, again using a low-latency communication protocol.

[0757] Step 9:

[0758] The terminal receives the converted video data packets, reconcatenates the received packets, and reconstructs the converted video frames.

[0759] Step 10:

[0760] The device then reconstructs the converted video frames and displays them in real time on the glasses-type device's display, with the display process optimized to avoid interfering with the user's visual field.

[0761] Step 11:

[0762] The user experiences color vision-compatible visual information in real time through the converted image. If the user provides feedback or changes to settings, the system will accept them appropriately and repeat the process.

[0763] Example 1

[0764] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0765] Current visual aids and software lack effective means for users with color vision variability to easily distinguish colors in their daily lives. In particular, conventional methods have difficulty converting and displaying colors in real time. Therefore, in order to improve the quality of life for users with color vision variability, the development of a new system that can convert and display colors clearly in real time is required.

[0766] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0767] In this invention, the server includes: a means for a user wearing a glasses-type terminal and capturing an image in the user's field of view in real time with a built-in camera; a means for transmitting the captured image data to the server using a low-latency communication protocol; a server means for applying a color conversion algorithm based on the user's color vision characteristics to the received image data; a means for compressing the image data to which the color conversion algorithm has been applied with a high-speed encoder and transmitting it back to the terminal in real time; and a means for decoding the converted image data and displaying it on the display of the user's glasses-type terminal. This enables a system in which users with visual color vision variability can convert and display colors in real time, making it easy to distinguish colors.

[0768] "User" refers to any individual who uses the system, and is particularly intended for people with visual color vision variability.

[0769] A "glasses-type terminal" is a glasses-type device worn by a user, which has a built-in camera and a display, and is a device for capturing and displaying visual information.

[0770] The "built-in camera" is a camera built into the glasses-type device, and serves to capture images within the user's field of vision.

[0771] "Capture" refers to the action of capturing images within the field of view using the built-in camera and saving them as data.

[0772] "Video Data" refers to data that digitally represents the image of the field of view captured by the built-in camera.

[0773] A "low-latency communication protocol" is a communication method for quickly sending and receiving data, and is used to achieve real-time processing.

[0774] A "server" is a computer system installed on a network that receives and processes data sent from multiple clients.

[0775] A "color conversion algorithm" is a calculation method for converting color information contained in video data to make it easier for users with specific color vision characteristics to distinguish.

[0776] A "high-speed encoder" is a device or software for compressing digital data that can perform data compression at high speeds.

[0777] The "display" is an image display device mounted on the glasses-type terminal, and serves to visually present color-converted image data to the user.

[0778] "Decoding" refers to the process of restoring compressed data to its original form.

[0779] "Color vision characteristics" refers to the color discrimination ability and type of color vision deficiency of each individual user, and is the information that forms the basis of the color conversion applied by the system.

[0780] This invention is a Color Universal Design (CUD) compatible system using glasses-type devices that allows users with color vision variability to easily distinguish colors so that they can comfortably perform daily activities and specific tasks. This system captures images in the field of view with a built-in camera, sends the images to a server for color conversion, and displays the converted images in real time, making it easier for users to recognize color differences.

[0781] Hardware and software used

[0782] Glasses: A device with a built-in camera and display that is worn by the user and captures video of the user's field of vision.

[0783] Built-in camera: A camera installed in the glasses-type device that captures images of the field of view in real time.

[0784] Server: A computer system for receiving video data and applying color transformation algorithms.

[0785] Communication protocol: A low-latency communication protocol (e.g., WebSocket, HTTP / 2) is used to send and receive video data in real time.

[0786] Color conversion algorithm: A calculation method for converting the color information contained in video data and converting specific colors into other, more easily distinguishable colors.

[0787] High-Speed ​​Encoder: A device or software for compressing digital data at high speed.

[0788] Display: A device installed in the glasses-type device for displaying the converted video data.

[0789] Decoder: A device or software that converts compressed data back into its original form.

[0790] Operational Overview

[0791] The user puts on the glasses and starts using them. The camera inside the device activates and captures the user's field of view in real time. The captured video data is temporarily stored in the device and then sent to a server using a low-latency communication protocol.

[0792] The server temporarily stores the received video data in a buffer and applies a color conversion algorithm based on the user's color vision characteristics. This algorithm converts certain colors into more easily distinguishable colors and adjusts saturation and brightness to create a visually more easily distinguishable image. The converted video data is then compressed using a high-speed encoder and sent back to the device in real time.

[0793] The device decodes the compressed video data and displays it on the built-in display. This allows users to more easily distinguish colors through the converted video. For example, for users who have difficulty distinguishing between red and green, red flowers will be converted to yellow and the saturation of green leaves will be enhanced, making them easier to distinguish.

[0794] Specific examples

[0795] For example, suppose that User A has difficulty distinguishing between red and green and is observing a garden. In this case, the system operates as follows:

[0796] 1. User A wears the glasses-type device and observes the garden.

[0797] 2. The camera inside the device captures images of the garden in real time.

[0798] 3. The captured video data is sent to the server using a low-latency communication protocol.

[0799] 4. The server receives the video data and applies a color transformation algorithm based on User A's color vision characteristics, converting red flowers to yellow and adjusting the saturation of green leaves.

[0800] 5. The server compresses the converted video data using a high-speed encoder and sends it back to the terminal.

[0801] 6. The device decodes the compressed video data and displays it on the screen.

[0802] 7. User A can easily distinguish the red flowers (converted to yellow) and green leaves through the converted image.

[0803] Example prompts for generative AI models

[0804] "User A, who has difficulty distinguishing between red and green, is wearing a glasses-type device and observing a garden. Please explain in detail the steps involved in capturing video data, sending it to the server, converting the color, retransmitting it to the device, and displaying it."

[0805] By inputting this prompt into a generative AI model, detailed operational steps are explained.

[0806] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0807] Step 1:

[0808] The user puts on the glasses and activates the built-in camera. The camera captures the image of the user's field of vision in real time. The image data is captured at a rate of 30 frames per second. The input is the scene coming into the user's field of vision, and the output is digital image data.

[0809] Step 2:

[0810] The device temporarily stores the captured video data in its internal memory. At the same time, it transmits the captured video data to the server in real time using a low-latency communication protocol (e.g., WebSocket). The input is the captured video data, and the output is the video data as communication packets.

[0811] Step 3:

[0812] The server receives video data sent from the device. The server temporarily stores the received video data in a buffer and applies a color conversion algorithm based on the user's color vision characteristics. Specifically, if it is difficult to distinguish between red and green, the server converts red to yellow and emphasizes the saturation of green. The input is unconverted video data, and the output is color-converted video data.

[0813] Step 4:

[0814] The server compresses the color-converted video data using a high-speed encoder. Compression reduces the amount of data and improves communication speed. The input is color-converted video data, and the output is compressed data.

[0815] Step 5:

[0816] The server then sends the compressed video data back to the terminal using a low-latency communication protocol. The input is compressed video data, and the output is compressed data as communication packets.

[0817] Step 6:

[0818] The terminal receives the compressed data sent from the server. The terminal uses a decoder to decode the compressed data and return it to its original format. The input is the compressed video data, and the output is the decoded video data.

[0819] Step 7:

[0820] The device displays the decoded video data on the built-in display. The real-time converted video allows the user to easily distinguish colors and clearly recognize objects and colors within their field of view. The input is the decoded video data, and the output is the video displayed on the display.

[0821] (Application example 1)

[0822] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0823] Users with color vision variability often have difficulty identifying products while shopping in physical stores. This is particularly inconvenient when the color of product labels or packaging contains important information. To solve this problem, a system is needed that can correct color vision in real time to compensate for visual impairments.

[0824] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0825] In this invention, the server includes means for capturing an image in the user's field of view with a built-in camera, means for transmitting the captured image data to the server in real time, means for applying a color conversion algorithm to the received image data, means for transmitting the image data to which the color conversion algorithm has been applied back to the terminal, means for displaying the converted image on the display of the user's eyeglass-type terminal, and means for supporting shopping in a physical store using a communication protocol, thereby enabling users with visual color vision variability to easily identify products in a physical store.

[0826] The "built-in camera" is a camera device built into the glasses-type terminal, which captures images within the user's field of vision.

[0827] "Captured video data" refers to visual information captured by the built-in camera that is saved or temporarily stored as digital data.

[0828] "Means for transmitting to a server in real time" refers to a communication method for transmitting video data captured by the built-in camera to a server without delay.

[0829] The "server means" is a central processing unit that processes received video data using various algorithms to generate converted data.

[0830] "Color conversion algorithm" refers to a calculation method and program for converting a specific color into another color so that users with color vision deficiency can easily recognize the color difference.

[0831] The "means for sending back to the terminal" is a communication method for returning data processed by the server back to the glasses-type terminal.

[0832] "Converted image" refers to image data that has undergone color correction through the application of a color conversion algorithm.

[0833] An "eyeglasses-type terminal" is an eyeglass-type electronic device that has a display and a built-in camera and provides visual assistance functions when worn by the user.

[0834] A "communication protocol" is a protocol that defines the rules and procedures for data communication, and in this case refers to a protocol that achieves low-latency communication.

[0835] "Means to support shopping in physical stores" is a visual aid system that makes it easier for users to select products in physical stores.

[0836] "Python" is a high-level programming language used to implement color conversion algorithms on the server.

[0837] "OpenCV" is an open source library for efficient image processing and is used for color conversion algorithms.

[0838] This invention is a Color Universal Design (CUD)-compliant system that allows users with color vision variability to easily distinguish colors. This system mainly utilizes glasses-type terminals and a server, and aims to improve the shopping experience in physical stores.

[0839] Operation overview

[0840] 1. User Action:

[0841] A user puts on the glasses-type device in a physical store where they are shopping, and the device begins capturing images of what is in the user's field of view using its built-in camera.

[0842] 2. Terminal processing:

[0843] The built-in camera in the glasses captures video and temporarily stores the data in real time. The device then sends the stored video data to a server via a communication protocol. The use of a low-latency communication protocol (e.g., WebSocket) enables real-time data transfer.

[0844] 3. Server processing:

[0845] The server then applies a color conversion algorithm using Python and OpenCV to the received video data. This algorithm converts certain colors into other colors that are easier to distinguish depending on the user's color vision characteristics. For example, if a user has difficulty distinguishing between red and green, red will be converted to yellow.

[0846] 4. Return to device:

[0847] The color-converted video data is sent back from the server to the terminal and displayed in real time on the display of the glasses-type terminal.

[0848] Hardware and Software

[0849] Glasses-type device: Equipped with a built-in camera, display, and communication module.

[0850] Server: A central processing unit that processes video data and applies color transformation algorithms.

[0851] Communication protocol: Uses low-latency WebSocket for data transfer.

[0852] Software: Python and OpenCV are used for video processing on the server.

[0853] Specific examples

[0854] For example, assume that user A has color vision that makes it difficult to distinguish between red and green, and is looking for a red product label in a physical store.

[0855] 1. User A puts on the glasses-type device and looks at the product shelves.

[0856] 2. The device's built-in camera captures images of the shelves and sends the data to the server.

[0857] 3. The server applies a color conversion algorithm to the received video data, converting the red label to yellow.

[0858] 4. The server returns the converted video data to the terminal and displays it on the display in real time.

[0859] 5. User A can identify the label that has been converted to yellow, making it easier for them to find the product they are looking for.

[0860] Prompt Sentence Examples

[0861] Prompt: Describe a system in which a color vision assistant transforms red labels into yellow in a brick-and-mortar store, making it easier for users to identify products.

[0862] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0863] Step 1:

[0864] A user puts on the glasses-type device and starts shopping in a physical store. The built-in camera captures the image of the user's field of view. The input is the image scene in the user's field of view, and the output is the captured image data.

[0865] Step 2:

[0866] The device temporarily stores the captured video data and sends it to the server via a communication protocol (e.g., WebSocket). The input is the captured video data, and the output is the video data sent to the server.

[0867] Step 3:

[0868] The server applies a color conversion algorithm to the received video data using Python and OpenCV. The specific data processing performed here is to convert colors into colors that are easy to recognize for people with color vision deficiency. The input is the original video data received by the server, and the output is the color-converted video data.

[0869] Step 4:

[0870] The server then sends the image data to which the color conversion algorithm has been applied back to the terminal via the communication protocol. The input is the color-converted image data, and the output is the image data sent from the server to the terminal.

[0871] Step 5:

[0872] The terminal displays the received color-converted video data on a display in real time. The input is the color-converted video data, and the output is the converted video that is visually displayed to the user.

[0873] Step 6:

[0874] The user can then use the converted image to identify the more easily distinguishable colors and search for the product. Specifically, the user identifies the red label, which has been converted to yellow, on the display of the glasses-type device and finds the product they need.

[0875] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0876] This invention combines a Color Universal Design (CUD)-compatible system using glasses-type devices to facilitate color discrimination for users with color vision variability with an emotion engine that recognizes the user's emotions. This system captures images within the field of view with a built-in camera, transmits the images to a server for color conversion, and displays the converted images in real time. The system also recognizes the user's emotions and optimizes the color conversion algorithm based on the user's emotional state, further improving the user experience.

[0877] Operation overview

[0878] The user puts on the glasses-type device and starts using it. After putting it on, the camera inside the device starts up and begins capturing images in the user's field of view in real time. The captured image data is temporarily stored in the device.

[0879] The device sends the captured video data to the server using a low-latency communication protocol, ensuring real-time transmission.

[0880] The server then runs the received video data through a color conversion algorithm, which converts specific colors into others that are easier to recognize, adjusting saturation and brightness as needed. The server then uses an emotion engine to recognize the user's emotional state. The emotion engine uses facial recognition technology and biometric sensors to analyze the user's emotions in real time.

[0881] Based on the analysis results, the parameters of the color conversion algorithm are adjusted. For example, if the user is feeling stressed, the colors are adjusted to reduce stress. By emphasizing or suppressing certain colors, the user's mental state is optimized.

[0882] The converted image data is then sent back to the device, which then displays it in real time. This allows users to more easily distinguish colors and enjoy a visual experience tailored to their emotional state.

[0883] Specific examples

[0884] For example, suppose that User A has difficulty distinguishing between red and green, and is viewing a garden full of flowers and leaves in a stressful situation. The system works as follows:

[0885] 1. User A wears the glasses-type device and observes the garden.

[0886] 2. The device's built-in camera captures footage of the garden.

[0887] 3. The device sends the captured video data to the server.

[0888] 4. The server receives the video data and applies a color conversion algorithm based on color vision characteristics. This algorithm converts red flowers to a more easily distinguishable yellow and adjusts the saturation of green leaves.

[0889] 5. The server uses the emotion engine to recognize User A's emotional state. The emotion engine uses facial recognition technology and biometric sensors to analyze User A's stress level in real time.

[0890] 6. The server adjusts the parameters of the color transformation algorithm based on the output of the emotion engine, for example, emphasizing calm colors to reduce stress.

[0891] 7. The server returns the converted video data to the terminal.

[0892] 8. The device displays the converted video data on the screen.

[0893] 9. User A can not only easily distinguish between the yellow flowers and the adjusted green leaves, but also experience visual information optimized to reduce stress.

[0894] In this way, the system of the present invention facilitates color discrimination for users with color vision variability and provides an optimal visual experience based on their emotional state. The system is highly practical because it provides advanced color correction and emotion recognition capabilities while being real-time and easy for users to operate.

[0895] The processing flow will be explained below.

[0896] Step 1:

[0897] The user wears the glasses-type device and observes the images in their field of vision. Any user input or setting changes are made at this point. The device is powered on and the camera and communication module are activated.

[0898] Step 2:

[0899] The device captures the user's field of view in real time through the built-in camera, which captures video frames at a constant frame rate and stores them in the device's internal memory.

[0900] Step 3:

[0901] The device analyzes the video data captured by the device to detect the user's face, and uses facial recognition technology to estimate the user's emotional state and generate emotion data.

[0902] Step 4:

[0903] The device divides the video data and emotion data into packets and adds timestamps and additional metadata (resolution, frame rate, emotional state, etc.).

[0904] Step 5:

[0905] The device sends the divided data packets to the server's API endpoint in real time using a low-latency communication protocol (e.g., WebSocket).

[0906] Step 6:

[0907] The server receives the data packets sent from the device, reconstructs the received packets, and decodes them into the original video frames and emotion data.

[0908] Step 7:

[0909] The server applies a color transformation algorithm to the decoded video frames, which converts certain colors into other, more easily distinguishable colors and adjusts saturation and brightness as needed.

[0910] Step 8:

[0911] The server dynamically adjusts the parameters of the color conversion algorithm based on the emotional data it recognizes. For example, if the user is feeling stressed, the colors will be changed to a more gentle tone.

[0912] Step 9:

[0913] The server re-divides the converted video data into data packets and also adds metadata about the conversion process.

[0914] Step 10:

[0915] The server then sends the converted video data packets back to the terminal, again using a low-latency communication protocol.

[0916] Step 11:

[0917] The terminal receives the converted video data packets, reconcatenates the received packets, and reconstructs the converted video frames.

[0918] Step 12:

[0919] The device then reconstructs the converted video frames and displays them in real time on the glasses-type device's display, with the display process optimized to avoid interfering with the user's visual field.

[0920] Step 13:

[0921] The user experiences visual information adapted to color vision variability in real time through the converted image. Additionally, the user's mental state is also optimized by providing optimized visual information based on their emotional state. If the user provides feedback or additional setting changes, the system accepts them appropriately and repeats the process.

[0922] Example 2

[0923] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0924] Users with color vision variability often have difficulty distinguishing certain colors in daily life, which can cause stress and anxiety in certain situations. To solve this problem, a system is needed that makes color distinction easier and provides a visual experience optimized according to the user's emotional state.

[0925] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for capturing an image in the user's field of view with a built-in camera, means for transmitting the captured image data to the server in real time, means for applying a color conversion algorithm to the received image data, means for recognizing the user's emotional state using an emotion engine, means for adjusting parameters of the color conversion algorithm based on the output of the emotion engine, means for transmitting the image data to which the color conversion algorithm has been applied back to the terminal, and means for displaying the converted image on the display of the user's eyeglass-type terminal. This not only makes it easier to distinguish colors, but also makes it possible to provide an optimal visual experience according to the user's emotional state.

[0926] The "built-in camera" is a camera device built into the glasses-type terminal, and serves to capture images within the user's field of vision in real time.

[0927] "Captured video data" refers to digital data of video captured by the built-in camera, which is temporarily stored in the terminal and then sent to the server.

[0928] "Real-time transmission" refers to the process of transmitting captured video data to a server immediately with minimal delay, primarily using low-latency communication protocols.

[0929] The "server means" refers to the function of the server that processes the received video data, and specifically includes the application of a color conversion algorithm and emotion recognition using an emotion engine.

[0930] A "color conversion algorithm" is a series of processing steps that converts a specific color into another color based on the user's color vision characteristics and adjusts the saturation and brightness.

[0931] The "emotion engine" is a system that uses facial recognition technology and biometric sensors to recognize the user's emotional state in real time, and optimizes processing based on that output.

[0932] "Emotion recognition" is the process of determining a user's current emotional state from their facial expressions and biometric information using an emotion engine.

[0933] The "display" is a display device mounted on the glasses-type terminal, and serves to allow the user to view the converted video data sent from the server.

[0934] This invention is a Color Universal Design (CUD)-compatible system that combines a glasses-type device that allows users with color vision variability to easily distinguish colors with an emotion engine that recognizes the user's emotions. The system captures images in the field of view with a built-in camera, transmits the images to a server for color conversion, and displays the converted images in real time. The system also recognizes the user's emotions and optimizes the color conversion algorithm based on the user's emotional state, further improving the user experience.

[0935] Hardware and software configuration used

[0936] 1. Glasses-type device

[0937] Built-in camera: Captures what is in the user's field of view.

[0938] Display: Shows the converted video to the user.

[0939] Communication module: Sends video data to the server and receives data from the server.

[0940] 2. Server

[0941] Color conversion algorithm: Converts the color of the video data based on the user's color vision characteristics and adjusts saturation and brightness.

[0942] Emotion Engine: Recognizes user emotions in real time and adjusts the parameters of the color conversion algorithm.

[0943] Communication protocol: Send and receive video data with low latency (e.g., WebSocket).

[0944] Data processing and calculation

[0945] 1. Capture and send

[0946] The user puts on the glasses and starts using it. The built-in camera activates and captures the user's field of view in real time. The captured video data is temporarily stored in the device and then sent to a server using a low-latency communication protocol.

[0947] 2. Applying color transformation

[0948] The server applies a color conversion algorithm to the received video data. This algorithm converts certain colors into other colors and adjusts saturation and brightness to suit the user's color vision characteristics. For example, it may change red to a more easily distinguishable yellow.

[0949] 3. Emotion Recognition and Algorithm Adjustment

[0950] The server uses an emotion engine to recognize the user's emotional state in real time. The emotion engine uses facial recognition technology and biometric sensors to analyze the user's stress level and other factors. Based on the results, it adjusts the parameters of the color conversion algorithm accordingly. In this way, colors are adjusted to provide the optimal visual experience according to the user's emotional state.

[0951] 4. Returning and displaying video data

[0952] The server then sends the converted video data back to the device, where it is displayed on the glasses in real time. This allows users to easily distinguish colors and enjoy an optimal visual experience tailored to their emotional state.

[0953] Specific examples

[0954] For example, suppose that User A, who has color vision variability, has difficulty distinguishing between red and green and is feeling stressed, and is looking at a garden. In this case, the following processing is performed:

[0955] 1. User A wears the glasses-type device and observes the garden.

[0956] 2. The device's built-in camera captures images of the garden and sends the image data to the server.

[0957] 3. The server applies a color transformation algorithm, converting red flowers to yellow and adjusting the saturation of green leaves.

[0958] 4. The server uses the emotion engine to analyze User A's stress level and adjusts the color to a calmer tone.

[0959] 5. The server returns the converted video data to the terminal, which then displays the video on its screen.

[0960] 6. User A can now easily distinguish colors and experience visual information with reduced stress.

[0961] In this way, the present invention optimizes the visual experience according to the user's color discrimination and emotional state.

[0962] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0963] Step 1:

[0964] The user puts on the glasses-type device and turns it on. The device automatically performs initial settings and the built-in camera starts up. The user's color vision characteristics information is read from the glasses-type device. This initial setting uses a profile based on the user's color vision characteristics.

[0965] Input: User's color vision characteristics information, powering on the glasses-type device

[0966] Output: Default color vision profile, camera startup

[0967] Step 2:

[0968] After the device's built-in camera is activated, it captures the image in the user's field of view in real time. The captured image data is temporarily stored in the device's memory.

[0969] Input: Camera capture of field of view

[0970] Output: Captured video data (stored in memory)

[0971] Step 3:

[0972] The device sends the captured video data to the server using a low-latency communication protocol such as WebSocket.

[0973] Input: Captured video data

[0974] Output: Video data sent to the server

[0975] Step 4:

[0976] The server then applies a color conversion algorithm to the received video data. This algorithm converts certain colors to other colors and adjusts saturation and brightness based on the user's color vision characteristics. For example, it converts red to yellow and adjusts the saturation of green.

[0977] Input: Received video data, color vision profile

[0978] Output: Color converted video data

[0979] Step 5:

[0980] The server recognizes the user's emotional state using an emotion engine, which analyzes data obtained from facial recognition technology and biometric sensors (e.g., heart rate, facial expressions) to determine the user's stress level.

[0981] Input: User's facial recognition data, biometric information

[0982] Output: User's emotional state (e.g., stress level)

[0983] Step 6:

[0984] The server readjusts the parameters of the color conversion algorithm based on the output of the emotion engine. For example, if the user is feeling high stress, the overall color will be changed to a calmer shade.

[0985] Input: Emotion engine output (user's emotional state)

[0986] Output: Re-adjusted color transformation parameters

[0987] Step 7:

[0988] The server then performs color conversion again to generate optimized video data, which is adjusted based on the user's color vision characteristics and emotional state.

[0989] Input: Retuned color transformation parameters

[0990] Output: Optimized video data

[0991] Step 8:

[0992] The server sends optimized video data to the terminal using a low-latency communication protocol.

[0993] Input: Optimized video data

[0994] Output: Video data sent to the device

[0995] Step 9:

[0996] The optimized video data received by the terminal is displayed on the display.

[0997] Input: Optimized video data

[0998] Output: Image displayed on the display

[0999] Step 10:

[1000] By viewing optimized images through the display, users can easily distinguish colors and enjoy an optimal visual experience according to their emotional state.

[1001] Input: Image displayed on the display

[1002] Output: Improved user visual experience, reduced stress

[1003] (Application example 2)

[1004] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1005] Users with color vision variability have difficulty distinguishing colors when selecting products in physical stores, and the experience is further exacerbated by added stress and confusion. Current color vision correction systems also lack real-time performance and are not optimized to take the user's emotional state into account, limiting the user experience. Therefore, a new system that simultaneously considers color vision correction and the user's emotional state is needed.

[1006] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for capturing an image in the user's field of view with a built-in camera, means for transmitting the captured image data to the server in real time, means for applying a color conversion algorithm to the received image data, means for transmitting the image data to which the color conversion algorithm has been applied back to the terminal, and means for optimizing the color conversion algorithm using an emotion engine that recognizes the user's emotion. This makes it easier for users with visual color vision variability to identify products and also makes it possible to provide visual information that is optimized based on the user's emotional state.

[1007] The "built-in camera" is a camera device built into the glasses-type terminal, and is a device for capturing images within the user's field of vision in real time.

[1008] "Video Data" means data containing visual information captured by the built-in camera that is processed and transmitted to enhance the user experience.

[1009] A "server" is a computer system that receives captured video data, applies color transformation algorithms, and further optimizes the data using an emotion engine.

[1010] A "color conversion algorithm" is a program process for converting specific colors in video data into other colors, taking into account color vision variability to make color identification easier.

[1011] The "emotion engine" is a software component that recognizes the user's emotions using facial recognition technology and biometric sensors, and optimizes the system's operation based on that emotional state.

[1012] A "glasses-type terminal" is a device in the shape of glasses that is worn by a user, and is equipped with a built-in camera and a display device for displaying video data in real time.

[1013] "Optimizing" means adjusting system parameters and settings according to emotional state and environment to improve the user experience.

[1014] This invention combines a Color Universal Design (CUD) compatible system using glasses-type terminals to make it easier for users with color vision variability to distinguish colors with an emotion engine that recognizes the user's emotions. Specific embodiments of this invention are described below.

[1015] When a user puts on the glasses and starts using them, the built-in camera activates and begins capturing images of the user's field of vision in real time. The captured image data is temporarily stored on the device and then sent to a server via a low-latency communication protocol.

[1016] The server applies a color conversion algorithm to the received video data, converting specific colors to other colors. This algorithm includes hue conversion, saturation adjustment, and brightness correction, making it easier for users with color vision deficiency to distinguish color differences. The server also uses an emotion engine to recognize the user's emotional state in real time. The emotion engine uses facial recognition technology and biometric sensors to analyze the user's emotions and optimizes the parameters of the color conversion algorithm based on the results.

[1017] As a concrete example, consider a scenario where User A is buying vegetables at a supermarket. User A has difficulty distinguishing between red and green, and is also feeling stressed while shopping. In this case, the system operates as follows:

[1018] 1. User A wears the glasses-type device and observes the vegetable section.

[1019] 2. The device's built-in camera captures video of the sales floor.

[1020] 3. The device sends the captured video data to the server.

[1021] 4. The server receives the video data and applies a color transformation algorithm, for example, converting red vegetables to yellow to make them more recognizable and adjusting the saturation of green vegetables.

[1022] 5. The server uses the emotion engine to recognize User A's emotional state. The emotion engine uses facial recognition technology and biometric sensors to analyze User A's stress level.

[1023] 6. The server optimizes the parameters of the color conversion algorithm based on the output of the emotion engine, for example, converting colors to a gentler tone to reduce stress.

[1024] 7. The server returns the converted video data to the terminal.

[1025] 8. The device displays the converted video data on the screen.

[1026] 9. User A can not only easily distinguish between the highlighted yellow vegetables and the adjusted green vegetables, but also experience optimized visual information.

[1027] The system uses the following main hardware and software:

[1028] Hardware: Smart glasses (e.g., Google Glass)

[1029] Software: OpenCV (face recognition), EmotionRecognition (emotion recognition), daltonize (color conversion)

[1030] As an example of a prompt, the following format can be fed into a generative AI model to provide detailed instructions for a specific scenario:

[1031] Imagine a scenario where a user goes to the supermarket and is stressed and has difficulty distinguishing colors. The user is wearing smart glasses, a camera captures the image, an emotion engine recognizes the user's emotions, and a color transformation algorithm optimizes the user's visual experience. Explain in detail how the smart glasses work and what the user's visual experience is.

[1032] In this way, the system of the present invention can provide users with color vision variability with easy color discrimination and an optimal visual experience tailored to their emotional state. The system is highly practical because it provides advanced color correction and emotion recognition functions while being real-time and easy for users to operate.

[1033] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1034] Step 1:

[1035] The user puts on the glasses-type device and begins to use it. The device's built-in camera starts up and begins capturing images in the user's field of vision in real time. The input is the image in the user's field of vision, and the output is the image data captured by the camera. Specifically, the camera captures high-resolution still images or videos and saves them as digital data.

[1036] Step 2:

[1037] The device sends the captured video data to the server. Here, a low-latency communication protocol (e.g., WebSocket or RTSP) is used to send the data quickly. The input is the captured video data, and the output is the video data received by the server. Specifically, the captured data is divided into packets of a fixed size and sent.

[1038] Step 3:

[1039] The server applies a color conversion algorithm to the received video data. The input is the received video data, and the output is the video data with the color conversion applied. Specific data processing involves converting specific colors to other colors that are easier to distinguish, and adjusting saturation and brightness as needed. For example, converting red to yellow and reducing the saturation of green.

[1040] Step 4:

[1041] The server uses an emotion engine to recognize the user's emotional state. The input is the user's facial image and biometric information, and the output is data indicating the user's emotional state (e.g., stress level). Specifically, it acquires data from facial recognition technology and biometric sensors, and estimates emotions using an emotion recognition model (e.g., OpenCV or EmotionRecognition).

[1042] Step 5:

[1043] The server optimizes the parameters of the color conversion algorithm based on the output of the emotion engine. The input is the user's emotional state data and the video data before conversion, and the output is the optimized color conversion parameters. Specifically, the server changes the parameters, such as adjusting the colors more gently when the stress level is high.

[1044] Step 6:

[1045] The server then returns the converted video data to the device. The input is the video data that has been color converted and optimized, and the output is the video data received by the device. Specifically, the data is sent again using a low-latency communication protocol.

[1046] Step 7:

[1047] The terminal displays the received converted video data on the display. The input is the video data received from the server, and the output is the video displayed on the display. In concrete terms, the display device displays the video data, allowing the user to experience visual information.

[1048] Step 8:

[1049] Users can easily see products through images that are color-corrected based on their color vision characteristics, and enjoy a visual experience optimized according to their emotional state. The input is the image displayed on the screen, and the output is the user's visual experience. Specifically, this system makes the process of selecting products in a physical store smoother and reduces stress.

[1050] In this way, the system can help users with color vision variability distinguish colors and provide an optimal visual experience according to the user's emotional state.

[1051] 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.

[1052] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1053] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1054] [Fourth embodiment]

[1055] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1056] 7, a 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.

[1057] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1058] 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.

[1059] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1060] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1061] 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. 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.

[1062] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1063] 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.

[1064] 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 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.

[1065] 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.

[1066] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1067] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1068] This invention is a Color Universal Design (CUD) compatible system using glasses-type devices to facilitate color discrimination for users with color vision variability. This system is realized by capturing images in the field of view with a built-in camera, sending the images to a server for color conversion, and displaying the converted images in real time.

[1069] Operation overview

[1070] The user puts on the glasses-type device and starts using it. After putting it on, the camera inside the device starts up and begins capturing images in the user's field of view in real time. The captured image data is temporarily stored in the device.

[1071] The device sends the captured video data to the server using a low-latency communication protocol, enabling real-time processing.

[1072] The server then applies a color conversion algorithm to the received video data. This algorithm converts certain colors into other colors that are easier to distinguish based on the user's color vision characteristics. For example, if a user has difficulty distinguishing between red and green, the algorithm converts red into yellow. It also adjusts saturation and brightness to create an image that is easier to distinguish visually.

[1073] The converted image data is sent back to the terminal from the server, and the terminal displays the received converted image data on the display in real time. Through this converted image, the user can easily distinguish colors and perform daily life and specific tasks comfortably.

[1074] Specific examples

[1075] For example, suppose user A has difficulty distinguishing between red and green and is looking at a garden full of flowers and leaves. The system works as follows:

[1076] 1. User A wears the glasses-type device and observes the garden.

[1077] 2. The device's built-in camera captures footage of the garden.

[1078] 3. The device sends the captured video data to the server.

[1079] 4. The server receives the video data and applies a color transformation algorithm based on color vision characteristics, converting red flowers to a more easily distinguishable yellow and adjusting the saturation of green leaves.

[1080] 5. The server returns the converted video data to the device.

[1081] 6. The device displays the converted video data on the screen.

[1082] 7. User A can now clearly distinguish the image of red flowers converted to yellow from green leaves.

[1083] In this way, the system of the present invention makes it easier for users with color vision variability to distinguish colors and improves convenience in their daily lives. The system is highly practical because it is capable of real-time operation, provides advanced color correction functions while being easy for users to operate.

[1084] The processing flow will be explained below.

[1085] Step 1:

[1086] The user wears the glasses-type device and observes the images in their field of vision. Any user input or setting changes are made at this point. The device is powered on and the camera and communication module are activated.

[1087] Step 2:

[1088] The device captures the user's field of view in real time through the built-in camera, which captures video frames at a constant frame rate and stores them in the device's internal memory.

[1089] Step 3:

[1090] The device splits the captured video data into small data packets over an internet connection, which contain timestamps and camera metadata (resolution, frame rate, etc.).

[1091] Step 4:

[1092] The device sends the divided data packets to the server's API endpoint, using a low-latency communication protocol (e.g., WebSocket) to maintain real-time performance.

[1093] Step 5:

[1094] The server receives the data packets sent from the terminal, reconstructs the received packets, and decodes them into the original video frames.

[1095] Step 6:

[1096] The server applies a color transformation algorithm to the decoded video frames, which converts certain colors into other, more easily distinguishable colors and adjusts saturation and brightness as needed.

[1097] Step 7:

[1098] The server then divides the transformed video frames, which are generated as a result of the color transformation algorithm, into data packets again, along with metadata about the transformation process.

[1099] Step 8:

[1100] The server then sends the converted video data packets back to the terminal, again using a low-latency communication protocol.

[1101] Step 9:

[1102] The terminal receives the converted video data packets, reconcatenates the received packets, and reconstructs the converted video frames.

[1103] Step 10:

[1104] The device then reconstructs the converted video frames and displays them in real time on the glasses-type device's display, with the display process optimized to avoid interfering with the user's visual field.

[1105] Step 11:

[1106] The user experiences color vision-compatible visual information in real time through the converted image. If the user provides feedback or changes to settings, the system will accept them appropriately and repeat the process.

[1107] Example 1

[1108] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1109] Current visual aids and software lack effective means for users with color vision variability to easily distinguish colors in their daily lives. In particular, conventional methods have difficulty converting and displaying colors in real time. Therefore, in order to improve the quality of life for users with color vision variability, the development of a new system that can convert and display colors clearly in real time is required.

[1110] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1111] In this invention, the server includes: a means for a user wearing a glasses-type terminal and capturing an image in the user's field of view in real time with a built-in camera; a means for transmitting the captured image data to the server using a low-latency communication protocol; a server means for applying a color conversion algorithm based on the user's color vision characteristics to the received image data; a means for compressing the image data to which the color conversion algorithm has been applied with a high-speed encoder and transmitting it back to the terminal in real time; and a means for decoding the converted image data and displaying it on the display of the user's glasses-type terminal. This enables a system in which users with visual color vision variability can convert and display colors in real time, making it easy to distinguish colors.

[1112] "User" refers to any individual who uses the system, and is particularly intended for people with visual color vision variability.

[1113] A "glasses-type terminal" is a glasses-type device worn by a user, which has a built-in camera and a display, and is a device for capturing and displaying visual information.

[1114] The "built-in camera" is a camera built into the glasses-type device, and serves to capture images within the user's field of vision.

[1115] "Capture" refers to the action of capturing images within the field of view using the built-in camera and saving them as data.

[1116] "Video Data" refers to data that digitally represents the image of the field of view captured by the built-in camera.

[1117] A "low-latency communication protocol" is a communication method for quickly sending and receiving data, and is used to achieve real-time processing.

[1118] A "server" is a computer system installed on a network that receives and processes data sent from multiple clients.

[1119] A "color conversion algorithm" is a calculation method for converting color information contained in video data to make it easier for users with specific color vision characteristics to distinguish.

[1120] A "high-speed encoder" is a device or software for compressing digital data that can perform data compression at high speeds.

[1121] The "display" is an image display device mounted on the glasses-type terminal, and serves to visually present color-converted image data to the user.

[1122] "Decoding" refers to the process of restoring compressed data to its original form.

[1123] "Color vision characteristics" refers to the color discrimination ability and type of color vision deficiency of each individual user, and is the information that forms the basis of the color conversion applied by the system.

[1124] This invention is a Color Universal Design (CUD) compatible system using glasses-type devices that allows users with color vision variability to easily distinguish colors so that they can comfortably perform daily activities and specific tasks. This system captures images in the field of view with a built-in camera, sends the images to a server for color conversion, and displays the converted images in real time, making it easier for users to recognize color differences.

[1125] Hardware and software used

[1126] Glasses: A device with a built-in camera and display that is worn by the user and captures video of the user's field of vision.

[1127] Built-in camera: A camera installed in the glasses-type device that captures images of the field of view in real time.

[1128] Server: A computer system for receiving video data and applying color transformation algorithms.

[1129] Communication protocol: A low-latency communication protocol (e.g., WebSocket, HTTP / 2) is used to send and receive video data in real time.

[1130] Color conversion algorithm: A calculation method for converting the color information contained in video data and converting specific colors into other, more easily distinguishable colors.

[1131] High-Speed ​​Encoder: A device or software for compressing digital data at high speed.

[1132] Display: A device installed in the glasses-type device for displaying the converted video data.

[1133] Decoder: A device or software that converts compressed data back into its original form.

[1134] Operational Overview

[1135] The user puts on the glasses and starts using them. The camera inside the device activates and captures the user's field of view in real time. The captured video data is temporarily stored in the device and then sent to a server using a low-latency communication protocol.

[1136] The server temporarily stores the received video data in a buffer and applies a color conversion algorithm based on the user's color vision characteristics. This algorithm converts certain colors into more easily distinguishable colors and adjusts saturation and brightness to create a visually more easily distinguishable image. The converted video data is then compressed using a high-speed encoder and sent back to the device in real time.

[1137] The device decodes the compressed video data and displays it on the built-in display. This allows users to more easily distinguish colors through the converted video. For example, for users who have difficulty distinguishing between red and green, red flowers will be converted to yellow and the saturation of green leaves will be enhanced, making them easier to distinguish.

[1138] Specific examples

[1139] For example, suppose that User A has difficulty distinguishing between red and green and is observing a garden. In this case, the system operates as follows:

[1140] 1. User A wears the glasses-type device and observes the garden.

[1141] 2. The camera inside the device captures images of the garden in real time.

[1142] 3. The captured video data is sent to the server using a low-latency communication protocol.

[1143] 4. The server receives the video data and applies a color transformation algorithm based on User A's color vision characteristics, converting red flowers to yellow and adjusting the saturation of green leaves.

[1144] 5. The server compresses the converted video data using a high-speed encoder and sends it back to the terminal.

[1145] 6. The device decodes the compressed video data and displays it on the screen.

[1146] 7. User A can easily distinguish the red flowers (converted to yellow) and green leaves through the converted image.

[1147] Example prompts for generative AI models

[1148] "User A, who has difficulty distinguishing between red and green, is wearing a glasses-type device and observing a garden. Please explain in detail the steps involved in capturing video data, sending it to the server, converting the color, retransmitting it to the device, and displaying it."

[1149] By inputting this prompt into a generative AI model, detailed operational steps are explained.

[1150] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1151] Step 1:

[1152] The user puts on the glasses and activates the built-in camera. The camera captures the image of the user's field of vision in real time. The image data is captured at a rate of 30 frames per second. The input is the scene coming into the user's field of vision, and the output is digital image data.

[1153] Step 2:

[1154] The device temporarily stores the captured video data in its internal memory. At the same time, it transmits the captured video data to the server in real time using a low-latency communication protocol (e.g., WebSocket). The input is the captured video data, and the output is the video data as communication packets.

[1155] Step 3:

[1156] The server receives video data sent from the device. The server temporarily stores the received video data in a buffer and applies a color conversion algorithm based on the user's color vision characteristics. Specifically, if it is difficult to distinguish between red and green, the server converts red to yellow and emphasizes the saturation of green. The input is unconverted video data, and the output is color-converted video data.

[1157] Step 4:

[1158] The server compresses the color-converted video data using a high-speed encoder. Compression reduces the amount of data and improves communication speed. The input is color-converted video data, and the output is compressed data.

[1159] Step 5:

[1160] The server then sends the compressed video data back to the terminal using a low-latency communication protocol. The input is compressed video data, and the output is compressed data as communication packets.

[1161] Step 6:

[1162] The terminal receives the compressed data sent from the server. The terminal uses a decoder to decode the compressed data and return it to its original format. The input is the compressed video data, and the output is the decoded video data.

[1163] Step 7:

[1164] The device displays the decoded video data on the built-in display. The real-time converted video allows the user to easily distinguish colors and clearly recognize objects and colors within their field of view. The input is the decoded video data, and the output is the video displayed on the display.

[1165] (Application example 1)

[1166] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1167] Users with color vision variability often have difficulty identifying products while shopping in physical stores. This is particularly inconvenient when the color of product labels or packaging contains important information. To solve this problem, a system is needed that can correct color vision in real time to compensate for visual impairments.

[1168] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1169] In this invention, the server includes means for capturing an image in the user's field of view with a built-in camera, means for transmitting the captured image data to the server in real time, means for applying a color conversion algorithm to the received image data, means for transmitting the image data to which the color conversion algorithm has been applied back to the terminal, means for displaying the converted image on the display of the user's eyeglass-type terminal, and means for supporting shopping in a physical store using a communication protocol, thereby enabling users with visual color vision variability to easily identify products in a physical store.

[1170] The "built-in camera" is a camera device built into the glasses-type terminal, which captures images within the user's field of vision.

[1171] "Captured video data" refers to visual information captured by the built-in camera that is saved or temporarily stored as digital data.

[1172] "Means for transmitting to a server in real time" refers to a communication method for transmitting video data captured by the built-in camera to a server without delay.

[1173] The "server means" is a central processing unit that processes received video data using various algorithms to generate converted data.

[1174] "Color conversion algorithm" refers to a calculation method and program for converting a specific color into another color so that users with color vision deficiency can easily recognize the color difference.

[1175] The "means for sending back to the terminal" is a communication method for returning data processed by the server back to the glasses-type terminal.

[1176] "Converted image" refers to image data that has undergone color correction through the application of a color conversion algorithm.

[1177] An "eyeglasses-type terminal" is an eyeglass-type electronic device that has a display and a built-in camera and provides visual assistance functions when worn by the user.

[1178] A "communication protocol" is a protocol that defines the rules and procedures for data communication, and in this case refers to a protocol that achieves low-latency communication.

[1179] "Means to support shopping in physical stores" is a visual aid system that makes it easier for users to select products in physical stores.

[1180] "Python" is a high-level programming language used to implement color conversion algorithms on the server.

[1181] "OpenCV" is an open source library for efficient image processing and is used for color conversion algorithms.

[1182] This invention is a Color Universal Design (CUD)-compliant system that allows users with color vision variability to easily distinguish colors. This system mainly utilizes glasses-type terminals and a server, and aims to improve the shopping experience in physical stores.

[1183] Operation overview

[1184] 1. User Action:

[1185] A user puts on the glasses-type device in a physical store where they are shopping, and the device begins capturing images of what is in the user's field of view using its built-in camera.

[1186] 2. Terminal processing:

[1187] The built-in camera in the glasses captures video and temporarily stores the data in real time. The device then sends the stored video data to a server via a communication protocol. The use of a low-latency communication protocol (e.g., WebSocket) enables real-time data transfer.

[1188] 3. Server processing:

[1189] The server then applies a color conversion algorithm using Python and OpenCV to the received video data. This algorithm converts certain colors into other colors that are easier to distinguish depending on the user's color vision characteristics. For example, if a user has difficulty distinguishing between red and green, red will be converted to yellow.

[1190] 4. Return to device:

[1191] The color-converted video data is sent back from the server to the terminal and displayed in real time on the display of the glasses-type terminal.

[1192] Hardware and Software

[1193] Glasses-type device: Equipped with a built-in camera, display, and communication module.

[1194] Server: A central processing unit that processes video data and applies color transformation algorithms.

[1195] Communication protocol: Uses low-latency WebSocket for data transfer.

[1196] Software: Python and OpenCV are used for video processing on the server.

[1197] Specific examples

[1198] For example, assume that user A has color vision that makes it difficult to distinguish between red and green, and is looking for a red product label in a physical store.

[1199] 1. User A puts on the glasses-type device and looks at the product shelves.

[1200] 2. The device's built-in camera captures images of the shelves and sends the data to the server.

[1201] 3. The server applies a color conversion algorithm to the received video data, converting the red label to yellow.

[1202] 4. The server returns the converted video data to the terminal and displays it on the display in real time.

[1203] 5. User A can identify the label that has been converted to yellow, making it easier for them to find the product they are looking for.

[1204] Prompt Sentence Examples

[1205] Prompt: Describe a system in which a color vision assistant transforms red labels into yellow in a brick-and-mortar store, making it easier for users to identify products.

[1206] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1207] Step 1:

[1208] A user puts on the glasses-type device and starts shopping in a physical store. The built-in camera captures the image of the user's field of view. The input is the image scene in the user's field of view, and the output is the captured image data.

[1209] Step 2:

[1210] The device temporarily stores the captured video data and sends it to the server via a communication protocol (e.g., WebSocket). The input is the captured video data, and the output is the video data sent to the server.

[1211] Step 3:

[1212] The server applies a color conversion algorithm to the received video data using Python and OpenCV. The specific data processing performed here is to convert colors into colors that are easy to recognize for people with color vision deficiency. The input is the original video data received by the server, and the output is the color-converted video data.

[1213] Step 4:

[1214] The server then sends the image data to which the color conversion algorithm has been applied back to the terminal via the communication protocol. The input is the color-converted image data, and the output is the image data sent from the server to the terminal.

[1215] Step 5:

[1216] The terminal displays the received color-converted video data on a display in real time. The input is the color-converted video data, and the output is the converted video that is visually displayed to the user.

[1217] Step 6:

[1218] The user can then use the converted image to identify the more easily distinguishable colors and search for the product. Specifically, the user identifies the red label, which has been converted to yellow, on the display of the glasses-type device and finds the product they need.

[1219] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1220] This invention combines a Color Universal Design (CUD)-compatible system using glasses-type devices to facilitate color discrimination for users with color vision variability with an emotion engine that recognizes the user's emotions. This system captures images within the field of view with a built-in camera, transmits the images to a server for color conversion, and displays the converted images in real time. The system also recognizes the user's emotions and optimizes the color conversion algorithm based on the user's emotional state, further improving the user experience.

[1221] Operation overview

[1222] The user puts on the glasses-type device and starts using it. After putting it on, the camera inside the device starts up and begins capturing images in the user's field of view in real time. The captured image data is temporarily stored in the device.

[1223] The device sends the captured video data to the server using a low-latency communication protocol, ensuring real-time transmission.

[1224] The server then runs the received video data through a color conversion algorithm, which converts specific colors into others that are easier to recognize, adjusting saturation and brightness as needed. The server then uses an emotion engine to recognize the user's emotional state. The emotion engine uses facial recognition technology and biometric sensors to analyze the user's emotions in real time.

[1225] Based on the analysis results, the parameters of the color conversion algorithm are adjusted. For example, if the user is feeling stressed, the colors are adjusted to reduce stress. By emphasizing or suppressing certain colors, the user's mental state is optimized.

[1226] The converted image data is then sent back to the device, which then displays it in real time. This allows users to more easily distinguish colors and enjoy a visual experience tailored to their emotional state.

[1227] Specific examples

[1228] For example, suppose that User A has difficulty distinguishing between red and green, and is viewing a garden full of flowers and leaves in a stressful situation. The system works as follows:

[1229] 1. User A wears the glasses-type device and observes the garden.

[1230] 2. The device's built-in camera captures footage of the garden.

[1231] 3. The device sends the captured video data to the server.

[1232] 4. The server receives the video data and applies a color conversion algorithm based on color vision characteristics. This algorithm converts red flowers to a more easily distinguishable yellow and adjusts the saturation of green leaves.

[1233] 5. The server uses the emotion engine to recognize User A's emotional state. The emotion engine uses facial recognition technology and biometric sensors to analyze User A's stress level in real time.

[1234] 6. The server adjusts the parameters of the color transformation algorithm based on the output of the emotion engine, for example, emphasizing calm colors to reduce stress.

[1235] 7. The server returns the converted video data to the terminal.

[1236] 8. The device displays the converted video data on the screen.

[1237] 9. User A can not only easily distinguish between the yellow flowers and the adjusted green leaves, but also experience visual information optimized to reduce stress.

[1238] In this way, the system of the present invention facilitates color discrimination for users with color vision variability and provides an optimal visual experience based on their emotional state. The system is highly practical because it provides advanced color correction and emotion recognition capabilities while being real-time and easy for users to operate.

[1239] The processing flow will be explained below.

[1240] Step 1:

[1241] The user wears the glasses-type device and observes the images in their field of vision. Any user input or setting changes are made at this point. The device is powered on and the camera and communication module are activated.

[1242] Step 2:

[1243] The device captures the user's field of view in real time through the built-in camera, which captures video frames at a constant frame rate and stores them in the device's internal memory.

[1244] Step 3:

[1245] The device analyzes the video data captured by the device to detect the user's face, and uses facial recognition technology to estimate the user's emotional state and generate emotion data.

[1246] Step 4:

[1247] The device divides the video data and emotion data into packets and adds timestamps and additional metadata (resolution, frame rate, emotional state, etc.).

[1248] Step 5:

[1249] The device sends the divided data packets to the server's API endpoint in real time using a low-latency communication protocol (e.g., WebSocket).

[1250] Step 6:

[1251] The server receives the data packets sent from the device, reconstructs the received packets, and decodes them into the original video frames and emotion data.

[1252] Step 7:

[1253] The server applies a color transformation algorithm to the decoded video frames, which converts certain colors into other, more easily distinguishable colors and adjusts saturation and brightness as needed.

[1254] Step 8:

[1255] The server dynamically adjusts the parameters of the color conversion algorithm based on the emotional data it recognizes. For example, if the user is feeling stressed, the colors will be changed to a more gentle tone.

[1256] Step 9:

[1257] The server re-divides the converted video data into data packets and also adds metadata about the conversion process.

[1258] Step 10:

[1259] The server then sends the converted video data packets back to the terminal, again using a low-latency communication protocol.

[1260] Step 11:

[1261] The terminal receives the converted video data packets, reconcatenates the received packets, and reconstructs the converted video frames.

[1262] Step 12:

[1263] The device then reconstructs the converted video frames and displays them in real time on the glasses-type device's display, with the display process optimized to avoid interfering with the user's visual field.

[1264] Step 13:

[1265] The user experiences visual information adapted to color vision variability in real time through the converted image. Additionally, the user's mental state is also optimized by providing optimized visual information based on their emotional state. If the user provides feedback or additional setting changes, the system accepts them appropriately and repeats the process.

[1266] Example 2

[1267] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1268] Users with color vision variability often have difficulty distinguishing certain colors in daily life, which can cause stress and anxiety in certain situations. To solve this problem, a system is needed that makes color distinction easier and provides a visual experience optimized according to the user's emotional state.

[1269] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for capturing an image in the user's field of view with a built-in camera, means for transmitting the captured image data to the server in real time, means for applying a color conversion algorithm to the received image data, means for recognizing the user's emotional state using an emotion engine, means for adjusting parameters of the color conversion algorithm based on the output of the emotion engine, means for transmitting the image data to which the color conversion algorithm has been applied back to the terminal, and means for displaying the converted image on the display of the user's eyeglass-type terminal. This not only makes it easier to distinguish colors, but also makes it possible to provide an optimal visual experience according to the user's emotional state.

[1270] The "built-in camera" is a camera device built into the glasses-type terminal, and serves to capture images within the user's field of vision in real time.

[1271] "Captured video data" refers to digital data of video captured by the built-in camera, which is temporarily stored in the terminal and then sent to the server.

[1272] "Real-time transmission" refers to the process of transmitting captured video data to a server immediately with minimal delay, primarily using low-latency communication protocols.

[1273] The "server means" refers to the function of the server that processes the received video data, and specifically includes the application of a color conversion algorithm and emotion recognition using an emotion engine.

[1274] A "color conversion algorithm" is a series of processing steps that converts a specific color into another color based on the user's color vision characteristics and adjusts the saturation and brightness.

[1275] The "emotion engine" is a system that uses facial recognition technology and biometric sensors to recognize the user's emotional state in real time, and optimizes processing based on that output.

[1276] "Emotion recognition" is the process of determining a user's current emotional state from their facial expressions and biometric information using an emotion engine.

[1277] The "display" is a display device mounted on the glasses-type terminal, and serves to allow the user to view the converted video data sent from the server.

[1278] This invention is a Color Universal Design (CUD)-compatible system that combines a glasses-type device that allows users with color vision variability to easily distinguish colors with an emotion engine that recognizes the user's emotions. The system captures images in the field of view with a built-in camera, transmits the images to a server for color conversion, and displays the converted images in real time. The system also recognizes the user's emotions and optimizes the color conversion algorithm based on the user's emotional state, further improving the user experience.

[1279] Hardware and software configuration used

[1280] 1. Glasses-type device

[1281] Built-in camera: Captures what is in the user's field of view.

[1282] Display: Shows the converted video to the user.

[1283] Communication module: Sends video data to the server and receives data from the server.

[1284] 2. Server

[1285] Color conversion algorithm: Converts the color of the video data based on the user's color vision characteristics and adjusts saturation and brightness.

[1286] Emotion Engine: Recognizes user emotions in real time and adjusts the parameters of the color conversion algorithm.

[1287] Communication protocol: Send and receive video data with low latency (e.g., WebSocket).

[1288] Data processing and calculation

[1289] 1. Capture and send

[1290] The user puts on the glasses and starts using it. The built-in camera activates and captures the user's field of view in real time. The captured video data is temporarily stored in the device and then sent to a server using a low-latency communication protocol.

[1291] 2. Applying color transformation

[1292] The server applies a color conversion algorithm to the received video data. This algorithm converts certain colors into other colors and adjusts saturation and brightness to suit the user's color vision characteristics. For example, it may change red to a more easily distinguishable yellow.

[1293] 3. Emotion Recognition and Algorithm Adjustment

[1294] The server uses an emotion engine to recognize the user's emotional state in real time. The emotion engine uses facial recognition technology and biometric sensors to analyze the user's stress level and other factors. Based on the results, it adjusts the parameters of the color conversion algorithm accordingly. In this way, colors are adjusted to provide the optimal visual experience according to the user's emotional state.

[1295] 4. Returning and displaying video data

[1296] The server then sends the converted video data back to the device, where it is displayed on the glasses in real time. This allows users to easily distinguish colors and enjoy an optimal visual experience tailored to their emotional state.

[1297] Specific examples

[1298] For example, suppose that User A, who has color vision variability, has difficulty distinguishing between red and green and is feeling stressed, and is looking at a garden. In this case, the following processing is performed:

[1299] 1. User A wears the glasses-type device and observes the garden.

[1300] 2. The device's built-in camera captures images of the garden and sends the image data to the server.

[1301] 3. The server applies a color transformation algorithm, converting red flowers to yellow and adjusting the saturation of green leaves.

[1302] 4. The server uses the emotion engine to analyze User A's stress level and adjusts the color to a calmer tone.

[1303] 5. The server returns the converted video data to the terminal, which then displays the video on its screen.

[1304] 6. User A can now easily distinguish colors and experience visual information with reduced stress.

[1305] In this way, the present invention optimizes the visual experience according to the user's color discrimination and emotional state.

[1306] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1307] Step 1:

[1308] The user puts on the glasses-type device and turns it on. The device automatically performs initial settings and the built-in camera starts up. The user's color vision characteristics information is read from the glasses-type device. This initial setting uses a profile based on the user's color vision characteristics.

[1309] Input: User's color vision characteristics information, powering on the glasses-type device

[1310] Output: Default color vision profile, camera startup

[1311] Step 2:

[1312] After the device's built-in camera is activated, it captures the image in the user's field of view in real time. The captured image data is temporarily stored in the device's memory.

[1313] Input: Camera capture of field of view

[1314] Output: Captured video data (stored in memory)

[1315] Step 3:

[1316] The device sends the captured video data to the server using a low-latency communication protocol such as WebSocket.

[1317] Input: Captured video data

[1318] Output: Video data sent to the server

[1319] Step 4:

[1320] The server then applies a color conversion algorithm to the received video data. This algorithm converts certain colors to other colors and adjusts saturation and brightness based on the user's color vision characteristics. For example, it converts red to yellow and adjusts the saturation of green.

[1321] Input: Received video data, color vision profile

[1322] Output: Color converted video data

[1323] Step 5:

[1324] The server recognizes the user's emotional state using an emotion engine, which analyzes data obtained from facial recognition technology and biometric sensors (e.g., heart rate, facial expressions) to determine the user's stress level.

[1325] Input: User's facial recognition data, biometric information

[1326] Output: User's emotional state (e.g., stress level)

[1327] Step 6:

[1328] The server readjusts the parameters of the color conversion algorithm based on the output of the emotion engine. For example, if the user is feeling high stress, the overall color will be changed to a calmer shade.

[1329] Input: Emotion engine output (user's emotional state)

[1330] Output: Re-adjusted color transformation parameters

[1331] Step 7:

[1332] The server then performs color conversion again to generate optimized video data, which is adjusted based on the user's color vision characteristics and emotional state.

[1333] Input: Retuned color transformation parameters

[1334] Output: Optimized video data

[1335] Step 8:

[1336] The server sends optimized video data to the terminal using a low-latency communication protocol.

[1337] Input: Optimized video data

[1338] Output: Video data sent to the device

[1339] Step 9:

[1340] The optimized video data received by the terminal is displayed on the display.

[1341] Input: Optimized video data

[1342] Output: Image displayed on the display

[1343] Step 10:

[1344] By viewing optimized images through the display, users can easily distinguish colors and enjoy an optimal visual experience according to their emotional state.

[1345] Input: Image displayed on the display

[1346] Output: Improved user visual experience, reduced stress

[1347] (Application example 2)

[1348] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1349] Users with color vision variability have difficulty distinguishing colors when selecting products in physical stores, and the experience is further exacerbated by added stress and confusion. Current color vision correction systems also lack real-time performance and are not optimized to take the user's emotional state into account, limiting the user experience. Therefore, a new system that simultaneously considers color vision correction and the user's emotional state is needed.

[1350] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for capturing an image in the user's field of view with a built-in camera, means for transmitting the captured image data to the server in real time, means for applying a color conversion algorithm to the received image data, means for transmitting the image data to which the color conversion algorithm has been applied back to the terminal, and means for optimizing the color conversion algorithm using an emotion engine that recognizes the user's emotion. This makes it easier for users with visual color vision variability to identify products and also makes it possible to provide visual information that is optimized based on the user's emotional state.

[1351] The "built-in camera" is a camera device built into the glasses-type terminal, and is a device for capturing images within the user's field of vision in real time.

[1352] "Video Data" means data containing visual information captured by the built-in camera that is processed and transmitted to enhance the user experience.

[1353] A "server" is a computer system that receives captured video data, applies color transformation algorithms, and further optimizes the data using an emotion engine.

[1354] A "color conversion algorithm" is a program process for converting specific colors in video data into other colors, taking into account color vision variability to make color identification easier.

[1355] The "emotion engine" is a software component that recognizes the user's emotions using facial recognition technology and biometric sensors, and optimizes the system's operation based on that emotional state.

[1356] A "glasses-type terminal" is a device in the shape of glasses that is worn by a user, and is equipped with a built-in camera and a display device for displaying video data in real time.

[1357] "Optimizing" means adjusting system parameters and settings according to emotional state and environment to improve the user experience.

[1358] This invention combines a Color Universal Design (CUD) compatible system using glasses-type terminals to make it easier for users with color vision variability to distinguish colors with an emotion engine that recognizes the user's emotions. Specific embodiments of this invention are described below.

[1359] When a user puts on the glasses and starts using them, the built-in camera activates and begins capturing images of the user's field of vision in real time. The captured image data is temporarily stored on the device and then sent to a server via a low-latency communication protocol.

[1360] The server applies a color conversion algorithm to the received video data, converting specific colors to other colors. This algorithm includes hue conversion, saturation adjustment, and brightness correction, making it easier for users with color vision deficiency to distinguish color differences. The server also uses an emotion engine to recognize the user's emotional state in real time. The emotion engine uses facial recognition technology and biometric sensors to analyze the user's emotions and optimizes the parameters of the color conversion algorithm based on the results.

[1361] As a concrete example, consider a scenario where User A is buying vegetables at a supermarket. User A has difficulty distinguishing between red and green, and is also feeling stressed while shopping. In this case, the system operates as follows:

[1362] 1. User A wears the glasses-type device and observes the vegetable section.

[1363] 2. The device's built-in camera captures video of the sales floor.

[1364] 3. The device sends the captured video data to the server.

[1365] 4. The server receives the video data and applies a color transformation algorithm, for example, converting red vegetables to yellow to make them more recognizable and adjusting the saturation of green vegetables.

[1366] 5. The server uses the emotion engine to recognize User A's emotional state. The emotion engine uses facial recognition technology and biometric sensors to analyze User A's stress level.

[1367] 6. The server optimizes the parameters of the color conversion algorithm based on the output of the emotion engine, for example, converting colors to a gentler tone to reduce stress.

[1368] 7. The server returns the converted video data to the terminal.

[1369] 8. The device displays the converted video data on the screen.

[1370] 9. User A can not only easily distinguish between the highlighted yellow vegetables and the adjusted green vegetables, but also experience optimized visual information.

[1371] The system uses the following main hardware and software:

[1372] Hardware: Smart glasses (e.g., Google Glass)

[1373] Software: OpenCV (face recognition), EmotionRecognition (emotion recognition), daltonize (color conversion)

[1374] As an example of a prompt, the following format can be fed into a generative AI model to provide detailed instructions for a specific scenario:

[1375] Imagine a scenario where a user goes to the supermarket and is stressed and has difficulty distinguishing colors. The user is wearing smart glasses, a camera captures the image, an emotion engine recognizes the user's emotions, and a color transformation algorithm optimizes the user's visual experience. Explain in detail how the smart glasses work and what the user's visual experience is.

[1376] In this way, the system of the present invention can provide users with color vision variability with easy color discrimination and an optimal visual experience tailored to their emotional state. The system is highly practical because it provides advanced color correction and emotion recognition functions while being real-time and easy for users to operate.

[1377] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1378] Step 1:

[1379] The user puts on the glasses-type device and begins to use it. The device's built-in camera starts up and begins capturing images in the user's field of vision in real time. The input is the image in the user's field of vision, and the output is the image data captured by the camera. Specifically, the camera captures high-resolution still images or videos and saves them as digital data.

[1380] Step 2:

[1381] The device sends the captured video data to the server. Here, a low-latency communication protocol (e.g., WebSocket or RTSP) is used to send the data quickly. The input is the captured video data, and the output is the video data received by the server. Specifically, the captured data is divided into packets of a fixed size and sent.

[1382] Step 3:

[1383] The server applies a color conversion algorithm to the received video data. The input is the received video data, and the output is the video data with the color conversion applied. Specific data processing involves converting specific colors to other colors that are easier to distinguish, and adjusting saturation and brightness as needed. For example, converting red to yellow and reducing the saturation of green.

[1384] Step 4:

[1385] The server uses an emotion engine to recognize the user's emotional state. The input is the user's facial image and biometric information, and the output is data indicating the user's emotional state (e.g., stress level). Specifically, it acquires data from facial recognition technology and biometric sensors, and estimates emotions using an emotion recognition model (e.g., OpenCV or EmotionRecognition).

[1386] Step 5:

[1387] The server optimizes the parameters of the color conversion algorithm based on the output of the emotion engine. The input is the user's emotional state data and the video data before conversion, and the output is the optimized color conversion parameters. Specifically, the server changes the parameters, such as adjusting the colors more gently when the stress level is high.

[1388] Step 6:

[1389] The server then returns the converted video data to the device. The input is the video data that has been color converted and optimized, and the output is the video data received by the device. Specifically, the data is sent again using a low-latency communication protocol.

[1390] Step 7:

[1391] The terminal displays the received converted video data on the display. The input is the video data received from the server, and the output is the video displayed on the display. In concrete terms, the display device displays the video data, allowing the user to experience visual information.

[1392] Step 8:

[1393] Users can easily see products through images that are color-corrected based on their color vision characteristics, and enjoy a visual experience optimized according to their emotional state. The input is the image displayed on the screen, and the output is the user's visual experience. Specifically, this system makes the process of selecting products in a physical store smoother and reduces stress.

[1394] In this way, the system can help users with color vision variability distinguish colors and provide an optimal visual experience according to the user's emotional state.

[1395] 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.

[1396] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1397] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1398] 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.

[1399] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1400] 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.

[1401] 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).

[1402] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1403] 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."

[1404] 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.

[1405] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1406] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1407] 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.

[1408] 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.

[1409] 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.

[1410] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1411] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1412] 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.

[1413] 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.

[1414] 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.

[1415] 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.

[1416] The following is further disclosed regarding the above embodiment.

[1417] (Claim 1)

[1418] a means for capturing an image of a user's field of view with an integrated camera;

[1419] means for transmitting the captured video data to a server in real time;

[1420] server means for applying a color transformation algorithm to the received video data;

[1421] means for transmitting the video data to which the color transformation algorithm has been applied back to the terminal;

[1422] means for displaying the converted image on a display of the user's glasses-type terminal;

[1423] A system including:

[1424] (Claim 2)

[1425] 10. The system of claim 1, wherein the color transformation algorithm transforms specific colors into other colors to make it easier for users with color vision variability to recognize color differences.

[1426] (Claim 3)

[1427] 10. The system of claim 1, wherein the color transformation algorithm includes hue transformation, saturation adjustment, and brightness correction.

[1428] "Example 1"

[1429] (Claim 1)

[1430] A means for a user to wear a glasses-type terminal and capture images in the user's field of view in real time using a built-in camera;

[1431] means for transmitting the captured video data to a server using a low latency communication protocol;

[1432] a server means for applying a color conversion algorithm based on the color vision characteristics of the user to the received video data;

[1433] A means for compressing the video data to which the color conversion algorithm has been applied using a high-speed encoder and sending it back to the terminal in real time;

[1434] means for decoding the converted video data and displaying it on a display of the user's glasses-type terminal;

[1435] A system including:

[1436] (Claim 2)

[1437] 10. The system of claim 1, wherein the color conversion algorithm converts specific colors into other, more easily distinguishable colors so that users with color vision variability can more easily recognize color differences.

[1438] (Claim 3)

[1439] 10. The system of claim 1, wherein the color transformation algorithm includes hue transformation, saturation adjustment, and brightness correction.

[1440] "Application Example 1"

[1441] extraction point

[1442] Considering user usage in physical stores

[1443] Use of communication protocol (WebSocket)

[1444] Color conversion algorithms are applied on the server using Python and OpenCV

[1445] New invention details

[1446] This system allows users to view color-converted images in real time while shopping in a physical store. It uses a low-latency communication protocol and applies color conversion algorithms on the server.

[1447] New Claims

[1448] (Claim 1)

[1449] a means for capturing an image of a user's field of view with an integrated camera;

[1450] means for transmitting the captured video data to a server in real time;

[1451] server means for applying a color transformation algorithm to the received video data;

[1452] means for transmitting the video data to which the color transformation algorithm has been applied back to the terminal;

[1453] means for displaying the converted image on a display of the user's glasses-type terminal;

[1454] A means to support shopping in physical stores using communication protocols;

[1455] A system including:

[1456] (Claim 2)

[1457] The system of claim 1, wherein the color conversion algorithm uses Python and OpenCV to convert specific colors into other colors so that users with color vision variability can easily recognize color differences.

[1458] (Claim 3)

[1459] 10. The system of claim 1, wherein the color transformation algorithm includes hue transformation, saturation adjustment, and brightness correction.

[1460]

[1461] "Example 2: Combining Emotion Engines"

[1462] (Claim 1)

[1463] a means for capturing an image of a user's field of view with an integrated camera;

[1464] means for transmitting the captured video data to a server in real time;

[1465] server means for applying a color transformation algorithm to the received video data;

[1466] means for recognizing an emotional state of a user using an emotion engine;

[1467] means for adjusting parameters of a color transformation algorithm based on the output of the emotion engine;

[1468] means for transmitting the video data to which the color transformation algorithm has been applied back to the terminal;

[1469] means for displaying the converted image on a display of the user's glasses-type terminal;

[1470] A system including:

[1471] (Claim 2)

[1472] 10. The system of claim 1, wherein the color transformation algorithm transforms specific colors into other colors to make it easier for users with color vision variability to recognize color differences.

[1473] (Claim 3)

[1474] 10. The system of claim 1, wherein the color transformation algorithm includes hue transformation, saturation adjustment, and brightness correction.

[1475] "Application example 2 when combining emotion engines"

[1476] (Claim 1)

[1477] a means for capturing an image of a user's field of view with an integrated camera;

[1478] means for transmitting the captured video data to a server in real time;

[1479] server means for applying a color transformation algorithm to the received video data;

[1480] means for transmitting the video data to which the color transformation algorithm has been applied back to the terminal;

[1481] means for displaying the converted image on a display of the user's glasses-type terminal;

[1482] a means for optimizing a color transformation algorithm using an emotion engine that recognizes user emotions;

[1483] A system including:

[1484] (Claim 2)

[1485] 10. The system of claim 1, wherein the color transformation algorithm transforms certain colors into other colors to help users with color vision variability perceive color differences, or adjusts colors based on emotional state.

[1486] (Claim 3)

[1487] 10. The system of claim 1, wherein the color transformation algorithm includes hue transformation, saturation adjustment, and brightness correction, and is adaptively adjusted based on the user's emotional state. [Explanation of symbols]

[1488] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for capturing an image of a user's field of view with an integrated camera; means for transmitting the captured video data to a server in real time; server means for applying a color transformation algorithm to the received video data; means for transmitting the video data to which the color transformation algorithm has been applied back to the terminal; means for displaying the converted image on a display of the user's glasses-type terminal; A system including:

2. 10. The system of claim 1, wherein the color transformation algorithm transforms a particular color into another color so that a user with color vision variability can more easily recognize the color difference.

3. 10. The system of claim 1, wherein the color transformation algorithm includes hue transformation, saturation adjustment, and brightness correction.

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