System

Abstract

A system is provided.SOLUTION: This system includes an input means for allowing a user to perform a visual acuity test, a means for transmitting input visual acuity data to a cloud server, a means for analyzing data received by the server and calculating optimum screen display setting, and a means for transmitting the optimum screen display setting to a terminal of the user.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] As people with presbyopia and declining vision require regular adjustments and purchases of eyeglasses and contact lenses, losing or misplacing them is a common problem. This problem is particularly pronounced in many countries where the population is aging, resulting in increased vision-related needs. Furthermore, vision tests must be conducted at hospitals or specialty stores, which is both time-consuming and costly. Therefore, there is a need for a system that allows users to easily take a vision test and obtain an optimal visual experience based on the results. [Means for solving the problem]

[0005] This invention provides a system including an input means for a user to take a vision test, a means for transmitting the input vision data to a cloud server, a means for the server to analyze the received data and calculate optimal screen display settings, and a means for transmitting the optimal screen display settings to the user's device. The system also includes a means for the server to store the latest vision data in a database and compare it with past data, a means for providing a user interface for the user to check and adjust the screen settings based on the results of the vision test, a means for the server to periodically generate analysis results and reports and notify the user, and a means for analyzing the vision data using AI. This allows users to enjoy an optimal visual experience based on their vision without the need for contact lenses or glasses.

[0006] "User" refers to a person who utilizes the system to take a vision test and receive optimal screen display settings.

[0007] "Eye Test" refers to a test for assessing a user's eyesight, which results in visual acuity data.

[0008] "Input means" refers to the device or interface that a user uses to complete a vision test.

[0009] "Visual acuity data" refers to information about a user's visual acuity obtained as a result of a visual acuity test.

[0010] "Cloud server" refers to a server that exists on the Internet and stores and analyzes vision data and calculates optimal screen display settings.

[0011] "Database" refers to the storage within the system for storing vision data and analysis results.

[0012] "Screen display settings" refers to the text size, color, contrast, etc. displayed on the device screen, which are optimized for eyesight.

[0013] "Optimal screen display settings" refers to the screen display settings that are easiest for the user to see, calculated by AI based on the user's vision data.

[0014] "Device" refers to the device, such as a PC, smartphone, or tablet, that a user uses to access the vision test and optimized screen display.

[0015] "User interface" refers to the interface that allows a user to operate the device and check and adjust vision tests and screen settings.

[0016] "AI model" refers to an artificial intelligence algorithm that learns from large amounts of vision data and calculates optimal screen display settings.

[0017] "Regular report" refers to a report that compiles information including analysis results, changes in vision, and recommendations.

[0018] "Notifications" refers to the means by which the user is sent reminders and reports for their vision test. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] The present invention relates to a system that eliminates the inconveniences associated with poor eyesight and provides a user with a comfortable visual environment. The components of this system and their specific operations are described below.

[0041] System configuration

[0042] 1. Input means (terminal) for users to take the vision test

[0043] The terminal is a device that can connect to the Internet, such as a PC or smartphone, and has an application installed that allows users to take the vision test. Users use this application to take the vision test.

[0044] 2. Means for sending and receiving visual acuity data (terminal and server)

[0045] Once the vision test is complete, the device sends the vision data to a cloud server, which stores it in a database and uses it for analysis.

[0046] 3. Data analysis and calculation of optimal screen display settings (server)

[0047] The visual acuity data stored on the cloud server is analyzed by an AI model. The AI ​​model learns from a large amount of visual acuity data collected in the past and calculates the optimal screen display settings for each individual user. The calculated settings include font size, color, contrast, etc.

[0048] 4. Means for sending optimal screen display settings and customizing the screen (server and terminal)

[0049] The optimal screen display settings calculated by the AI ​​model are sent from the server to the user's device. The device automatically adjusts the screen display based on the received setting information, allowing the user to view a screen optimized for their eyesight.

[0050] 5. Generation and notification of regular reports (server and terminal)

[0051] The server periodically analyzes the user's vision data and generates a report based on the analysis results, which is then sent to the user via email or in-app notification, containing information about changes in vision, recommendations for screen settings, and other important information.

[0052] Specific examples

[0053] Example 1: First-time user case

[0054] 1. The user takes the eye test

[0055] Users using the system for the first time install a dedicated app on their smartphone and create an account.

[0056] The user starts the vision test and follows the instructions on the screen to perform the vision assessment.

[0057] The test results are sent from the terminal to the server.

[0058] 2. The server analyzes the data

[0059] The server stores the received vision data in a database and analyzes it using an AI model.

[0060] The AI ​​model calculates the optimal screen display settings and sends that information to the device.

[0061] 3. The device optimizes the screen

[0062] The system automatically adjusts the font size, color, and contrast on the screen based on the settings information received by the user's smartphone.

[0063] The user can check the adjusted screen and make fine adjustments as necessary.

[0064] Example 2: Regular User Case

[0065] 1. Get reminders for your eye exams

[0066] Regular reminders for vision tests are sent to the user's device.

[0067] The user confirms the notification and takes the vision test.

[0068] 2. The server analyzes the new data

[0069] The device sends the new visual acuity data to the server, which stores it in a database.

[0070] The AI ​​model recalculates the optimal screen display settings based on the latest visual acuity data and sends them to the device.

[0071] 3. Update screen settings and generate reports

[0072] The device will reflect the new settings and adjust the screen display.

[0073] The server generates a report containing the results of the periodic analysis and sends it to the user.

[0074] As a result, the present invention allows users to easily take visual acuity tests and provides a screen display that is always optimized for their visual acuity. This system also monitors changes in visual acuity and adjusts the visual environment as needed, which is expected to help prevent the user's visual acuity from declining.

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] The user installs a dedicated application on their PC or smartphone. They then launch the app and create an account by entering basic information such as their name, age, email address, and vision correction method. The information they entered is sent from the device to a server, which then stores it in a database.

[0078] Step 2:

[0079] The user follows the app's instructions to begin the vision test. The test involves reading letters and symbols displayed on the screen, and the device obtains the user's vision data (e.g., 0.8 for the right eye, 0.7 for the left eye).

[0080] Step 3:

[0081] Once the vision test is complete, the device sends the acquired vision data to a cloud server, which stores the received data in a database.

[0082] Step 4:

[0083] The server inputs the visual acuity data stored in the database into an AI model for analysis, which then calculates optimal screen display settings (text size, color, contrast) based on the user's visual acuity data.

[0084] Step 5:

[0085] The server calculates the optimal screen display settings and sends them to the user's device. The settings information sent includes details about font size, color, and contrast.

[0086] Step 6:

[0087] The display screen is automatically adjusted based on the screen setting information received by the device. The user can then check the adjusted screen and make fine adjustments as necessary.

[0088] Step 7:

[0089] The server periodically analyzes the vision data and generates a report based on the results (including changes in vision and recommendations for optimal screen settings), which is then sent to the user via email or in-app notification.

[0090] Step 8:

[0091] The user receives a reminder to periodically take a vision test. When the user takes the test according to the reminder, the device sends new vision data to the server. The server stores the latest data and updates the screen display settings based on it.

[0092] Example 1

[0093] 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."

[0094] Many users experience the inconvenience of declining eyesight, but do not take appropriate measures to avoid the hassle and cost of taking appropriate measures. Furthermore, conventional vision assistance systems are unable to adaptively respond to changes in a user's eyesight, making it difficult to provide a screen display optimized for each individual's eyesight.

[0095] 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.

[0096] In this invention, the server includes a means for storing visual acuity data in a database, a means for analyzing the stored visual acuity data using an AI model and calculating optimal screen display settings, and a means for transmitting the optimal screen display settings to the user's device. This allows the user to easily take a visual acuity test and always provide a screen display optimized for the user's visual acuity. In addition, by continuously monitoring changes in the user's visual acuity and adjusting the visual environment as needed, it is expected that the effect of preventing the user's visual acuity from declining can also be expected.

[0097] "User" refers to the person or entity administering the vision test.

[0098] "Input means" refers to an interface for inputting data for a vision test into an endpoint device.

[0099] "Cloud server" refers to remote computing resources and data storage accessible over the Internet.

[0100] "Visual acuity data" refers to information about a user's visual acuity obtained through a visual acuity test.

[0101] "Database" refers to a system for efficiently storing and retrieving structured or unstructured data.

[0102] An "AI model" refers to a program that uses machine learning algorithms to analyze vision data and make predictions and classifications.

[0103] "Screen display settings" refers to parameters for adjusting the device screen, such as character size, background color, and contrast, to make it easier for the user to visually recognize.

[0104] "Transmission means" refers to a mechanism for transferring data or information from one system or device to another.

[0105] "Regular report" refers to written or electronic data that analyzes visual acuity data and periodically notifies the user of the results.

[0106] "User interface" refers to the interactive screens and controls that allow a user to interact with a system and input or receive information.

[0107] The present invention relates to a vision assistance system that eliminates the inconveniences associated with poor eyesight and provides users with a comfortable visual environment. This system is designed to optimize screen display settings based on the user's visual acuity data. Each component of this system and its specific operation are described below.

[0108] System configuration

[0109] 1. Input means (terminal) for users to take the vision test

[0110] The terminal is a device that can connect to the Internet, such as a PC or smartphone, and has an application installed that allows users to take the vision test. The user uses this application to take the vision test. Input means include a touch screen, keyboard, and mouse.

[0111] 2. Means of transmitting visual acuity data (terminal and server)

[0112] Once the vision test is complete, the device sends the vision data to a cloud server. The device encrypts the data using SSL / TLS protocol to ensure security. The server then stores the received vision data in a database.

[0113] 3. Data analysis method (server)

[0114] The visual acuity data stored on the cloud server is analyzed using an AI model. The AI ​​model is built using machine learning libraries such as TensorFlow and PyTorch. This AI model learns from a large amount of visual acuity data collected in the past and calculates the optimal screen display settings for each individual user. The visual acuity data analyzed includes text size, color, contrast, etc.

[0115] 4. Means of sending optimal screen display settings (server and device)

[0116] The optimal screen display settings calculated by the AI ​​model are sent from the server to the user's device. The device then automatically adjusts the screen display based on the received setting information. During this process, for example, the smartphone's settings menu is automatically changed to adjust the font size and background color.

[0117] 5. Method for generating scheduled reports (server)

[0118] The server periodically analyzes the user's vision data and generates a report based on the analysis, which is then sent to the user via email or in-app notification, containing information about changes in vision, recommendations for screen settings, and other important information.

[0119] Specific examples

[0120] Example 1: First-time user case

[0121] 1. The user takes the eye test

[0122] A user using the system for the first time installs the dedicated app on their smartphone and creates an account. The user starts the vision test and follows the instructions displayed on the screen to evaluate their vision. The test results are sent from the device to the server.

[0123] 2. The server analyzes the data

[0124] The server stores the received visual acuity data in a database and analyzes it using an AI model, which then calculates the optimal screen display settings and sends that information to the device.

[0125] 3. The device optimizes the screen

[0126] The system automatically adjusts the font size, color, and contrast on the screen based on the settings information received by the user's smartphone. The user can then check the adjusted screen and make fine adjustments as necessary.

[0127] Example 2: Regular User Case

[0128] 1. Get reminders for your eye exams

[0129] The user will be notified of periodic reminders for vision tests on their device. The user will check the notifications and take the vision test.

[0130] 2. The server analyzes the new data

[0131] The device sends the new visual acuity data to the server, which stores it in a database. The AI ​​model then recalculates the optimal screen display settings based on the latest visual acuity data and sends them to the device.

[0132] 3. Update screen settings and generate reports

[0133] The device reflects the new setting information and adjusts the screen display. The server generates a report containing the results of the periodic analysis and sends it to the user.

[0134] Prompt Sentence Examples

[0135] The first-time vision test prompt is as follows:

[0136] "Install the app for the first time and take the eye test. Select the direction in which you see the Landolt ring and submit your results. Our server will analyze the results and let you know the optimal screen settings."

[0137] The prompt for the regular vision test is as follows:

[0138] "Please take a vision test once every three months. We will send your latest vision data to our server and you will receive new screen settings. Once you receive the notification, please start the vision test."

[0139] As a result, the present invention allows users to easily conduct visual acuity tests and provides a screen display that is always optimized for their visual acuity. Furthermore, by continuously monitoring changes in visual acuity, it is expected to have the effect of preventing the user's visual acuity from declining.

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1:

[0142] The user installs a dedicated application on their device and creates an account. When the user starts the vision test, the app displays the vision test instructions. The user then performs a vision test such as a Landolt ring test and enters the results into the app. The input for this step is the vision test data, and the output is to temporarily store the vision test results on the device.

[0143] Step 2:

[0144] Once the vision test is complete, the device sends the vision data to a cloud server. The data is encrypted using SSL / TLS protocol for security. The input of this step is the vision test result, and the output is data transmission to the cloud server.

[0145] Step 3:

[0146] The server receives the visual acuity data and stores it in a database. The database can be MySQL or PostgreSQL. The input of this step is the visual acuity data sent to the cloud server, and the output is storage in the database.

[0147] Step 4:

[0148] The server analyzes the visual acuity data using an AI model. The AI ​​model is built using TensorFlow and PyTorch. Based on past data, visual acuity data is used as input, and optimal screen display settings (text size, color, contrast) are calculated as output. This is the step where data analysis takes place.

[0149] Step 5:

[0150] The server sends the optimal screen display settings to the user's device. This can be done using REST API or WebSocket. The input to this step is the screen display settings calculated by the server, and the output is the settings sent to the device.

[0151] Step 6:

[0152] The device automatically adjusts the screen display based on the setting information received. For example, on an Android device, you can adjust the font size or background color by directly changing the settings menu. The input for this step is the screen display setting received from the server, and the output is the adjusted screen display.

[0153] Step 7:

[0154] The server periodically re-analyzes the visual acuity data and generates a report based on the results, including any changes in visual acuity and recommendations for new screen settings. The input for this step is the periodically collected visual acuity data, and the output is the generated report.

[0155] Step 8:

[0156] The generated report is sent to the user via email or in-app notification, and the user can check their own vision status based on the report contents. The input of this step is the generated report, and the output is a notification to the user.

[0157] Through the above processing steps, the system provides a screen display optimized for the user's eyesight, and by continuously monitoring changes in eyesight, it is expected to be effective in preventing the user's eyesight from deteriorating.

[0158] (Application example 1)

[0159] 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."

[0160] Conventional vision assistance systems have limited themselves to adjusting the screen's text size and contrast, making it difficult for users with poor eyesight to comfortably use online services such as online shopping sites. Furthermore, because appropriate adjustments are not made in response to changes in vision, there is a risk of vision deterioration progressing. Furthermore, the process of users undergoing a vision test and then applying the optimal settings based on the results is complicated. The present invention aims to resolve these issues and provide optimal product display according to visual acuity.

[0161] 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.

[0162] In this invention, the server includes an input means for a user to take a visual acuity test, a means for transmitting the input visual acuity data to the cloud server, a means for analyzing the data received by the server and calculating optimal screen display settings, a means for transmitting the optimal screen display settings to the user's terminal, a means for optimizing the display of product pages based on the analysis results, and a means for displaying product information according to the user's visual acuity. This allows users with poor eyesight to comfortably use online shopping sites, and since appropriate adjustments are automatically made in response to changes in visual acuity, the risk of vision deterioration can be reduced.

[0163] "Input means for a user to take a vision test" refers to the device or software interface that a user uses to take a vision test.

[0164] A "cloud server" is a remote server used to store and process data over the Internet.

[0165] "Visual acuity data" refers to the visual acuity measurement results obtained by a user through a visual acuity test.

[0166] "Optimal screen display settings" refers to settings that adjust the screen's character size, color, contrast, etc. based on the user's eyesight.

[0167] A "user interface" is a screen or operation panel that a user uses to input information and check results.

[0168] A "product page" is a web page on an online shopping site that displays product information, images, prices, etc.

[0169] "Product information" refers to detailed information such as the name, description, and price of a product sold on an online shopping site.

[0170] "Analysis results" refer to evaluations and judgments based on vision test data.

[0171] "Optimization" means to set or adjust something so that it is most effective under certain conditions.

[0172] "Visual acuity-appropriate display" refers to a display method that is customized based on the user's visual acuity.

[0173] The present invention aims to realize a system that provides optimal product display according to visual acuity. Each component of this system and its specific operation will be described below.

[0174] System configuration and operation

[0175] Input means (terminal) for users to take the vision test

[0176] This system uses internet-connected devices such as smartphones and tablets as an input means for users to take the vision test. Users take the vision test using a smartphone or other device with a dedicated application installed. During the vision test, their eyesight is evaluated by following the instructions displayed on the screen.

[0177] Means for sending and receiving visual acuity data (terminal and server)

[0178] Once the vision test is complete, the device sends the acquired vision data to a cloud server, where it is received and stored in a database.

[0179] Data analysis and calculation of optimal screen display settings (server)

[0180] The visual acuity data stored on the cloud server is analyzed using a generative AI model. The AI ​​model is trained based on a large amount of previously collected visual acuity data and calculates the optimal screen display settings for each individual user. These calculated settings include text size, color, and contrast.

[0181] Sending optimal screen display settings and screen customization methods (server and terminal)

[0182] The optimal screen display settings calculated by the AI ​​model are sent from the server to the user's device. The device automatically adjusts the display of the product page based on the received settings, allowing the user to view product information optimized for their eyesight.

[0183] Scheduled report generation and notification method (server and terminal)

[0184] The user's device will periodically receive reminders for vision tests. The user will check the notifications and take the test again. The new vision data will be sent to the server, which will store it in a database. The AI ​​model will then recalculate the optimal screen display settings based on the latest vision data and send it to the device, providing product displays that match the user's latest vision.

[0185] Specific examples

[0186] For first-time users

[0187] 1. Conducting a vision test

[0188] Users install the dedicated app on their smartphone and create an account.

[0189] Begin the vision test and perform the vision assessment as directed.

[0190] The visual acuity data is transmitted to a server.

[0191] 2. Data Analysis and Set Calculations

[0192] The server stores the received visual acuity data in a database and analyzes it using a generative AI model.

[0193] The AI ​​model calculates the optimal screen display settings and sends that information to the device.

[0194] 3. Screen display optimization

[0195] The display of the product page is automatically adjusted based on the setting information received by the user's smartphone.

[0196] The user can check the screen after adjustment and make fine adjustments as necessary.

[0197] For regular users

[0198] 1. Vision test reminder

[0199] Regular reminders for vision tests are sent to the user's device.

[0200] The user confirms the notification and takes the vision test.

[0201] 2. Analysis of new data and reconfiguration

[0202] The device sends the new visual acuity data to the server, which stores it in a database.

[0203] The AI ​​model recalculates the optimal screen display settings based on the latest visual acuity data and sends them to the device.

[0204] 3. Update the display and generate a report

[0205] The device will reflect the new settings and adjust the display of the product page.

[0206] Based on the results of the periodic analysis, the server generates a report and sends it to the user.

[0207] Adding specific examples

[0208] For example, if a user receives the results of a visual acuity test that show "right eye 0.7, left eye 0.8" and wants to send this along with past data to a cloud server, the following could be an example of a prompt sentence:

[0209] Prompt Sentence Examples

[0210] User ID: user_123

[0211] Right eye visual acuity: 0.7

[0212] Left eye visual acuity: 0.8

[0213] Based on this, the present invention can provide optimal product display according to the visual acuity, allowing users to enjoy online shopping in an optimal visual environment that adapts to changes in visual acuity.

[0214] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0215] Step 1:

[0216] Conducting a vision test

[0217] Users install a dedicated app on their smartphone and create an account. After creating an account, they start the vision test via the application. During the vision test, they follow the instructions displayed on the screen to tap and swipe to evaluate their vision. The results of this vision test are obtained as input data. The input data includes the visual acuity value for each eye (e.g., 0.7 for the right eye, 0.8 for the left eye).

[0218] Step 2:

[0219] Submitting visual acuity data

[0220] The device sends the acquired visual acuity data to the cloud server. At this stage, the visual acuity data is sent to the server via the network, and processing on the server side begins. The input data is the results of the visual acuity test (visual acuity value), and the output data is saved in a visual acuity database on the cloud.

[0221] Step 3:

[0222] Data analysis and calculation of optimal display settings

[0223] The server stores the received visual acuity data in a database and analyzes the data using a generative AI model. As a result of the analysis, the optimal screen display settings for each user's visual acuity are calculated. These settings include text size, color, contrast, etc. The input data is visual acuity data and past data, and the optimal screen display settings are obtained as output data.

[0224] Step 4:

[0225] Sending optimal screen display settings and customizing the screen

[0226] The server sends the calculated optimal screen display settings to the user's device. The device reflects the received setting information and automatically adjusts the display of the product page. This allows the user to see product information optimized for their eyesight. The input data are the optimal screen display settings, and the output data is the adjusted product page.

[0227] Step 5:

[0228] Scheduled report generation and notification

[0229] The server periodically analyzes the visual acuity data and generates a report based on the results. The generated report is sent to the user via email or in-app notification. The report includes changes in visual acuity and recommendations for screen settings. The input data is the visual acuity data history, and the generated analysis report is the output data.

[0230] Step 6:

[0231] Eye test reminder notifications

[0232] A visual acuity test reminder is sent to the user's device periodically. The user checks the notification and takes the visual acuity test again, and the latest visual acuity data is sent to the server. The input data is the reminder notification and the user's visual acuity test results, and the output data is updated visual acuity data.

[0233] Through the above process, the system can provide optimal product display tailored to the user's eyesight and automatically adjust according to changes in eyesight.

[0234] 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.

[0235] This invention is a system that combines visual acuity testing and user emotion recognition to provide the user with an optimal visual environment. This system uses an emotion engine to acquire user emotion data and analyzes it together with visual acuity data to achieve more advanced optimization of screen display settings. Below, we will explain each component of this system and its specific operation.

[0236] System configuration

[0237] 1. Input means (terminal) for users to take the vision test

[0238] The terminal is a device that can connect to the internet, such as a PC or smartphone, and has a dedicated application installed. The user uses this application to take the vision test.

[0239] 2. Means for sending and receiving visual acuity data (terminal and server)

[0240] Once the vision test is complete, the device sends the vision data to a cloud server, which stores it in a database and uses it for analysis.

[0241] 3. Data analysis and calculation of optimal screen display settings (server)

[0242] The vision data stored on the cloud server is analyzed by an AI model. The AI ​​model learns from a large amount of previously collected vision and emotional data and calculates the optimal screen display settings for each user's vision and emotional state. The calculated settings include font size, color, contrast, etc.

[0243] 4. Emotion Engine (Terminal)

[0244] The device is equipped with an emotion engine that recognizes the user's emotions. The emotion engine uses sensors such as a camera and microphone to analyze the user's facial expressions and voice to obtain emotional data.

[0245] 5. Means for sending and receiving emotion data (terminal and server)

[0246] The emotion data acquired by the emotion engine is sent to a cloud server along with the visual acuity data. The server stores the received emotion data in a database and uses it for analysis along with the visual acuity data.

[0247] 6. Transmission of optimal screen display settings and screen customization means (server and terminal)

[0248] The optimal screen display settings calculated by the AI ​​model are sent from the server to the user's device. The device automatically adjusts the screen display based on the received setting information, allowing the user to view a screen optimized for their visual acuity and emotional state.

[0249] 7. Generation and notification of regular reports (server and terminal)

[0250] The server periodically analyzes the user's visual acuity and emotional data and generates a report based on the analysis results. The report is sent to the user via email or in-app notification. The report includes changes in visual acuity, emotional state, and recommendations for screen settings.

[0251] Specific examples

[0252] Example 1: First-time user case

[0253] 1. The user performs the visual acuity test and emotion recognition

[0254] Users using the system for the first time install a dedicated app on their smartphone and create an account.

[0255] The user follows the app's instructions to take a vision test, and the device collects vision data.

[0256] The emotion engine analyzes the user's facial expressions and voice to obtain emotional data.

[0257] The terminal transmits the acquired visual acuity data and emotion data to a cloud server.

[0258] 2. The server analyzes the data

[0259] The server stores the received data in a database and analyzes it using an AI model.

[0260] The AI ​​model calculates the optimal screen display settings and sends that information to the device.

[0261] 3. The device optimizes the screen

[0262] The system automatically adjusts the font size, color, and contrast on the screen based on the settings information received by the user's smartphone.

[0263] The user can check the adjusted screen and make fine adjustments as necessary.

[0264] Example 2: Regular User Case

[0265] 1. Receive emotion recognition and eye test reminders

[0266] Periodic reminders for emotion recognition and vision tests are sent to the user's device.

[0267] The user checks the notification and performs a vision test and emotion recognition.

[0268] 2. The server analyzes the new data

[0269] The device sends new data to the server, which stores it in a database.

[0270] The AI ​​model recalculates the optimal screen display settings based on the latest visual acuity and emotional data and sends them to the device.

[0271] 3. Update screen settings and generate reports

[0272] The device will reflect the new settings and adjust the screen display.

[0273] The server generates a report containing the periodic analysis results and sends it to the user.

[0274] As a result, the present invention can provide an optimal visual environment that takes into account the user's visual acuity and emotional state. Furthermore, the system continuously monitors emotional changes, contributing to reducing user stress and providing a comfortable environment.

[0275] The processing flow will be explained below.

[0276] Step 1:

[0277] The user installs a dedicated application on their PC or smartphone. They then launch the app and create an account by entering basic information such as their name, age, email address, and vision correction method. The entered information is sent from the device to the server, which then stores it in a database.

[0278] Step 2:

[0279] The user follows the instructions in the app to begin the vision test. The test involves reading the letters and symbols displayed on the screen, and the device obtains the user's visual acuity data (e.g., 0.8 for the right eye, 0.7 for the left eye).

[0280] Step 3:

[0281] The device's built-in emotion engine analyzes the user's facial expressions and voice to obtain current emotional data, thereby collecting emotional information such as stress and fatigue felt by the user during the test.

[0282] Step 4:

[0283] After the vision test is completed, the device sends the acquired vision data and emotion data to a cloud server, which stores the received data in a database.

[0284] Step 5:

[0285] The server inputs the visual acuity and emotional data stored in the database into an AI model for analysis. The AI ​​model calculates optimal screen display settings (text size, color, contrast) based on the user's visual acuity and emotional data.

[0286] Step 6:

[0287] The server calculates the optimal screen display settings and sends them to the user's device. The settings information sent includes details about font size, color, and contrast.

[0288] Step 7:

[0289] The display screen is automatically adjusted based on the screen setting information received by the device. The user can then check the adjusted screen and make fine adjustments as necessary.

[0290] Step 8:

[0291] The server periodically analyzes the user's visual acuity and emotional data and generates a report based on the results. The report is sent to the user via email or in-app notification. The report includes changes in visual acuity, emotional state, and recommended screen settings.

[0292] Step 9:

[0293] The user receives a reminder to periodically take a vision test. The user then takes the test according to the reminder, and new vision and emotion data are sent from the device to the server. The server stores the latest data and updates the screen display settings based on it.

[0294] Through this series of steps, the user can obtain a visual experience optimized for their visual acuity and emotional state.

[0295] Example 2

[0296] 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."

[0297] While conventional vision testing systems can provide screen display settings based on a user's visual acuity, they are unable to consider the user's emotional state, making it difficult to alleviate visual fatigue and stress. It is also difficult to respond appropriately to periodic changes in visual acuity or fluctuations in emotional state. Furthermore, they lack an intuitive user interface for users to check and fine-tune their own screen settings.

[0298] The identification process 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 an input means for the user to perform a visual acuity test and emotion recognition, a means for transmitting the input visual acuity data and emotion data to the cloud server, a means for the server to analyze the received visual acuity data and emotion data and calculate optimal screen display settings, a means for transmitting the optimal screen display settings to the user's terminal and the terminal automatically adjusting the screen display, a means for providing a user interface for the user to fine-tune the settings, and a means for the server to generate periodic reports based on the analysis results of the visual acuity data and emotion data and notify the user. This allows the user to obtain screen displays optimized for their visual acuity and emotion status, thereby alleviating visual fatigue and stress. Furthermore, the periodic reports allow the user to understand fluctuations in visual acuity and emotion and take appropriate measures.

[0299]

[0300] A "vision test" is a series of procedures for measuring a user's vision condition.

[0301] "Emotion recognition" is a technology that analyzes data such as a user's facial expressions and voice to identify their emotional state.

[0302] "Input means" refers to a device or part thereof that allows a user to perform vision testing and emotion recognition.

[0303] "Visual acuity data" is information about the user's visual acuity obtained through a visual acuity test.

[0304] "Emotion data" is information about the user's emotional state obtained through emotion recognition.

[0305] A "cloud server" refers to a server located in a remote location via the Internet that has the ability to store and analyze large amounts of data.

[0306] A "database" is a collection of information that stores received data in an organized manner and can be used for later analysis and retrieval.

[0307] The "AI model" is an algorithm that learns from large amounts of data, analyzes visual acuity data and emotional data, and calculates optimal screen display settings.

[0308] "Screen display settings" are settings that adjust the character size, color, contrast, etc. on the terminal screen based on the user's eyesight and emotional state.

[0309] A "user interface" refers to an operation screen and means for a user to directly operate and check and adjust screen settings.

[0310] A "regular report" is a report created based on the results of analyzing visual acuity data and emotional data, and includes information about changes in visual acuity and emotional state.

[0311] "Notifications" are messages or alerts that notify the user when new information or settings are created.

[0312]

[0313] This invention is a system that combines visual acuity testing and user emotion recognition to provide the optimal visual environment for the user. This system uses an emotion engine to acquire user emotion data and analyzes it together with visual acuity data to achieve more advanced optimization of screen display settings.

[0314] System configuration

[0315] 1. Input means (terminal) for users to take the vision test

[0316] The terminal is a device that can connect to the Internet, such as a PC or smartphone, and has a dedicated application installed. The user uses this application to take the vision test. The vision test is carried out by following the charts and instructions displayed during the test, and vision data is collected.

[0317] 2. Emotion engine (terminal) for emotion recognition

[0318] The device is equipped with an emotion engine that recognizes the user's emotions. The emotion engine uses sensors such as a camera and microphone to analyze the user's facial expressions and voice to obtain emotion data. For example, while the user is taking an eye test, a camera captures the user's face, and the emotion engine identifies smiling, serious expressions, etc. The microphone is also used to analyze the tone and volume of the user's voice to evaluate the user's emotional state.

[0319] 3. Data transmission and storage (devices and servers)

[0320] After completing the vision test and emotion recognition, the device sends the data to a cloud server. The data is encrypted and securely transmitted. The vision and emotion data received by the cloud server is stored in a database, making it available for later analysis.

[0321] 4. Data analysis and generation of optimization settings (server)

[0322] The cloud server provides the AI ​​model with the visual acuity and emotional data stored in the database. The AI ​​model learns from a large amount of previously collected data and calculates the optimal screen display settings for each user's visual acuity and emotional state. The calculated settings include text size, color, and contrast. For example, if a user shows signs of poor visual acuity and is in a state of high stress, the AI ​​model suggests increasing the text size and changing the color tone to a more subdued one.

[0323] 5. Sending and applying optimization settings (server and terminal)

[0324] The optimal screen display settings calculated by the server are sent to the user's device. The device automatically adjusts the screen display based on the received setting information. The user can then view the optimized screen. Furthermore, the user can make fine adjustments to suit their preferences through the user interface.

[0325] 6. Scheduled report generation and notification (server and terminal)

[0326] The server periodically re-analyzes the user's visual acuity and emotional data and generates a report based on the results. The report includes changes in visual acuity, fluctuations in emotional state, and recommendations for further screen settings. The generated report is sent to the user via email or in-app notification. This allows the user to constantly monitor fluctuations in their visual acuity and emotional state and take appropriate action.

[0327] Specific examples

[0328] Example 1: First-time user case

[0329] 1. The user performs the visual acuity test and emotion recognition

[0330] Users install a dedicated app on their smartphone and create an account.

[0331] Launch the app and follow the instructions to take the vision test. For example, you might be prompted to "Choose the smallest letter you can see."

[0332] The device's camera and microphone are used to record the user's facial expressions and voice, and obtain emotional data.

[0333] The terminal transmits the visual acuity data and the emotion data to the cloud server.

[0334] 2. The server analyzes the data

[0335] The server stores the data in a database and analyzes it using an AI model.

[0336] The AI ​​model calculates the optimal screen display settings and sends that information to the device.

[0337] 3. The device optimizes the screen

[0338] The device automatically adjusts the text size, color, and contrast on the screen based on the settings information it receives.

[0339] The user can check the screen after adjustment and make fine adjustments as necessary.

[0340] Example prompt:

[0341] The user installs a dedicated app and takes a vision test. The device acquires vision and emotion data and sends it to a cloud server. The server analyzes the data and sends optimized screen display settings to the device. The device applies the settings, and the user checks the screen.

[0342] Example 2: Regular User Case

[0343] 1. Receive emotion recognition and eye test reminders

[0344] Periodic reminders for emotion recognition and vision tests are sent to the user's device.

[0345] The user checks the notification and performs a vision test and emotion recognition.

[0346] 2. The server analyzes the new data

[0347] The device sends new data to the server, which stores it in a database.

[0348] The AI ​​model recalculates the optimal screen display settings based on the latest visual acuity and emotional data and sends them to the device.

[0349] 3. Update screen settings and generate reports

[0350] The device will reflect the new settings and adjust the screen display.

[0351] The server generates a report containing the periodic analysis results and sends it to the user.

[0352] Example prompt:

[0353] The user receives periodic reminders to perform vision tests and emotion recognition. The device sends the data to the server. The server analyzes the data and sends the optimal settings to the device. The device refreshes the screen. The server generates a report and sends it to the user.

[0354] As a result, the present invention provides an optimal visual environment that takes into account the user's visual acuity and emotional state. Continuous monitoring of emotional changes contributes to reducing stress and providing a comfortable user environment. This system is a comprehensive solution that allows users to better manage their visual acuity and emotional state and enjoy an optimal visual environment.

[0355] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0356]

[0357] Step 1: The device performs a vision test

[0358] The user launches the app and creates an account. They then follow the app's instructions to begin the vision test. Specifically, the device displays a vision test chart and asks the user to select the smallest letter they can see. Based on this input, the device generates vision data and evaluates its accuracy.

[0359] Step 2: The device performs emotion recognition

[0360] During the vision test, the device's camera and microphone record the user's facial expressions and voice. The emotion engine analyzes this data to identify the user's emotional state. The emotional data obtained through this analysis reflects the user's stress level and concentration in real time.

[0361] Step 3: The device sends the visual acuity data and emotion data to the cloud server.

[0362] After completing the vision test and emotion recognition, the device sends the generated vision and emotion data to a cloud server, where the data is encrypted and securely transmitted to the cloud server.

[0363] Step 4: The server saves the data to the database

[0364] The cloud server stores the received visual acuity and emotion data in a database, maintaining data integrity and efficiently managing it for future analysis.

[0365] Step 5: The server provides the data to the AI ​​model

[0366] The AI ​​model uses the stored visual acuity and emotional data to learn from past data and analyze the user's data. Specifically, it calculates optimal screen display settings, taking into account trends in visual acuity and changes in emotional state.

[0367] Step 6: The server calculates the optimal display settings

[0368] The AI ​​model analyzes the data and calculates the optimal screen display settings for the user, including text size, color, and contrast. For example, a user with poor eyesight might be suggested to increase text size and enhance color contrast.

[0369] Step 7: The server sends the optimization settings to the device

[0370] The server calculates the optimal screen display settings and sends them to the user's device. The settings information is encrypted and sent securely.

[0371] Step 8: Your device adjusts the display

[0372] The device automatically adjusts the size, color, and contrast of on-screen text based on the settings information it receives, and users can review these settings and fine-tune them as needed through the user interface.

[0373] Step 9: Server generates scheduled reports

[0374] The server periodically re-analyzes the user's visual acuity and emotional data and generates a report based on the results, including changes in visual acuity, fluctuations in emotional state, and recommendations for further screen settings.

[0375] Step 10: The server notifies the user of the report

[0376] The server generates reports and sends them to the user via email or in-app notifications, allowing the user to keep track of fluctuations in their visual acuity and emotional state and take appropriate action.

[0377] (Application example 2)

[0378] 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."

[0379] Conventional vision testing systems optimize screen displays by taking into account only the user's visual acuity data, making it difficult to optimize the display while also reflecting the user's emotional state. Furthermore, for users to comfortably navigate environments with a lot of visual information, such as virtual stores, it is necessary to optimize the visual environment by taking into account not only visual acuity but also emotions. However, no such technology has existed to date. Therefore, there has been a demand for the development of a system that provides an optimal visual environment that simultaneously considers the user's visual acuity and emotional state.

[0380] The specific processing by the specific 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 transmitting input visual acuity data and emotion data to a cloud server, means for the server to analyze the received data and calculate optimal screen display settings using a generative AI model, means for transmitting the optimal screen display settings to the user's terminal, and means for the terminal to automatically adjust the screen display based on the setting information received. This makes it possible to provide a comfortable visual environment that simultaneously takes into account the user's visual acuity state and emotional state.

[0381] 1. "Eye Test" means a series of tests administered to assess a User's eyesight.

[0382] 2. "Input means" refers to the device or interface through which the user conducts the vision test.

[0383] 3. "Cloud Server" means a remote server for storing, processing, and analyzing data via the Internet.

[0384] 4. "Visual acuity data" means information about a user's visual acuity, such as the numerical values ​​or results obtained from a visual acuity test.

[0385] 5. "Emotion data" refers to information indicating the user's emotional state obtained from facial expressions, voice, etc.

[0386] 6. A "generative AI model" is an artificial intelligence algorithm that is trained based on large amounts of data and used to perform data analysis and predictions.

[0387] 7. "Screen display settings" are settings for adjusting visual elements of the screen, such as font size, color, and contrast.

[0388] 8. "Terminal" refers to a computer device used by a User, such as a smartphone or PC.

[0389] 9. "Automatic adjustment means" means a function that allows the system to automatically optimize the screen display without user intervention.

[0390] 10. "User interface" refers to the screens and operating means through which a user interacts with and operates a system.

[0391] The present invention is a system that provides an optimal visual environment by simultaneously considering the user's visual acuity and emotional state. This system automatically optimizes the screen display based on the results of a visual acuity test and emotional recognition.

[0392] System configuration

[0393] 1. Input means (terminal) for users to take the vision test

[0394] The terminal uses a device such as a smartphone or a head-mounted display (HMD) that allows the user to take the vision test. The user takes the vision test through the terminal, and the results of the vision test are acquired by the terminal as numerical data.

[0395] 2. Means for sending and receiving visual acuity data and emotion data (terminal and cloud server)

[0396] After the eye test is completed, the device sends the eye data to the cloud server. At the same time, the device is equipped with an emotion engine that analyzes the user's facial expressions and voice to obtain emotion data. This data is then sent to the cloud server in parallel.

[0397] 3. Data analysis and calculation of optimal screen display settings (cloud server)

[0398] The visual acuity and emotion data stored on the cloud server is analyzed using a generative AI model. The generative AI model is trained based on a large amount of previously collected visual acuity and emotion data, and calculates the optimal screen display settings for each user's visual acuity and emotional state. These settings include text size, color, contrast, etc.

[0399] 4. Transmission of optimal screen display settings and screen customization methods (cloud server and device)

[0400] The optimal screen display settings analyzed and calculated by the AI ​​model are sent from the cloud server to the user's device. The device then automatically adjusts the screen display based on the received setting information, ensuring that the user always experiences the optimal visual environment.

[0401] 5. Providing a User Interface

[0402] The user is also provided with a user interface on the device to check and further adjust the screen settings based on the results of the vision test and emotion recognition. The user can make fine adjustments through this interface.

[0403] Example

[0404] Example 1: First-time user

[0405] 1. The user puts on the head-mounted display and starts the application.

[0406] 2. The application will automatically start the vision test and emotion recognition and send the data to the cloud server.

[0407] 3. The cloud server analyzes the received visual acuity data and emotion data and calculates the optimal screen display settings.

[0408] 4. Optimal setting information is sent to the device, and the user's visual environment is automatically adjusted.

[0409] 5. Users can enjoy a comfortable shopping experience in a virtual store.

[0410] Example 2: For a regular user

[0411] 1. Periodic reminder notifications for vision tests and emotion recognition will be sent to your device.

[0412] 2. The user checks the notification and performs a visual acuity test and emotion recognition.

[0413] 3. The device sends the visual acuity data and emotion data to the cloud server.

[0414] 4. The cloud server analyzes the latest data and recalculates the optimal screen display settings.

[0415] 5. The new configuration information is sent to the device, and the user's visual environment is updated.

[0416] Prompt Sentence Examples

[0417] "An application that optimizes the visual environment for users wearing head-mounted displays. It collects the user's emotional and visual acuity data in real time, analyzes it on a cloud server, and provides optimal screen settings."

[0418] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0419] Step 1:

[0420] The user uses an input means for taking a visual acuity test to obtain visual acuity data.

[0421] Specific operation: The user launches an application on their smartphone or head-mounted display and takes a vision test. The vision test collects responses to the displayed test patterns and letters, and stores them as vision data on the device. In this step, the user's vision response is input, and vision data is obtained as output.

[0422] Step 2:

[0423] The terminal uses an emotion engine to acquire emotion data of the user.

[0424] Specific operation: The device's camera and microphone are used to analyze the user's facial expressions and voice in real time. The emotion engine processes this data and outputs the user's emotional state as numerical or categorical data. In this step, the user's facial expressions and voice are taken as input, and emotion data is generated as output.

[0425] Step 3:

[0426] The visual acuity data and emotion data are sent to a cloud server.

[0427] Specific operation: The terminal transmits the acquired visual acuity data and emotion data as a data packet to the cloud server. In this step, the visual acuity data and emotion data are input, and the transmitted data packet is output.

[0428] Step 4:

[0429] The cloud server analyzes the received data using a generative AI model and calculates the optimal screen display settings.

[0430] Specific operation: The cloud server stores the received visual acuity data and emotion data in a database and runs a generative AI model based on these data. The AI ​​model compares these data with past data and calculates the optimal screen display settings (e.g., font size, color, contrast, etc.) for the user's condition. In this step, the data sent to the cloud server is the input, and the optimal screen display settings are obtained as the output.

[0431] Step 5:

[0432] Send optimal screen display settings to the user's device.

[0433] Specific operation: The cloud server sends the calculated screen display settings to the terminal as a data packet. In this step, the optimal screen display settings are input, and the transmitted setting information is output.

[0434] Step 6:

[0435] The device automatically adjusts the screen display based on the received setting information.

[0436] Specific operation: The device applies the received screen display setting information and automatically adjusts the font size, color, contrast, etc. This setting allows the user to experience an optimized visual environment. In this step, the received setting information is the input, and the adjusted screen display is the output.

[0437] Step 7:

[0438] A user interface allows the user to view and fine-tune the screen settings.

[0439] Specific operation: The device provides a user interface, and the user checks the initial screen display based on the results of the vision test and emotion recognition, and fine-tunes it as necessary. In this step, the input is screen setting information, and the output is a screen display optimized for the user.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] [Second embodiment]

[0444] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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).

[0450] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] In the smart glasses 214, the 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.

[0455] 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."

[0456] The present invention relates to a system that eliminates the inconveniences associated with poor eyesight and provides a user with a comfortable visual environment. The components of this system and their specific operations are described below.

[0457] System configuration

[0458] 1. Input means (terminal) for users to take the vision test

[0459] The terminal is a device that can connect to the Internet, such as a PC or smartphone, and has an application installed that allows users to take the vision test. Users use this application to take the vision test.

[0460] 2. Means for sending and receiving visual acuity data (terminal and server)

[0461] Once the vision test is complete, the device sends the vision data to a cloud server, which stores it in a database and uses it for analysis.

[0462] 3. Data analysis and calculation of optimal screen display settings (server)

[0463] The visual acuity data stored on the cloud server is analyzed by an AI model. The AI ​​model learns from a large amount of visual acuity data collected in the past and calculates the optimal screen display settings for each individual user. The calculated settings include font size, color, contrast, etc.

[0464] 4. Means for sending optimal screen display settings and customizing the screen (server and terminal)

[0465] The optimal screen display settings calculated by the AI ​​model are sent from the server to the user's device. The device automatically adjusts the screen display based on the received setting information, allowing the user to view a screen optimized for their eyesight.

[0466] 5. Generation and notification of regular reports (server and terminal)

[0467] The server periodically analyzes the user's vision data and generates a report based on the analysis results, which is then sent to the user via email or in-app notification, containing information about changes in vision, recommendations for screen settings, and other important information.

[0468] Specific examples

[0469] Example 1: First-time user case

[0470] 1. The user takes the eye test

[0471] Users using the system for the first time install a dedicated app on their smartphone and create an account.

[0472] The user starts the vision test and follows the instructions on the screen to perform the vision assessment.

[0473] The test results are sent from the terminal to the server.

[0474] 2. The server analyzes the data

[0475] The server stores the received vision data in a database and analyzes it using an AI model.

[0476] The AI ​​model calculates the optimal screen display settings and sends that information to the device.

[0477] 3. The device optimizes the screen

[0478] The system automatically adjusts the font size, color, and contrast on the screen based on the settings information received by the user's smartphone.

[0479] The user can check the adjusted screen and make fine adjustments as necessary.

[0480] Example 2: Regular User Case

[0481] 1. Get reminders for your eye exams

[0482] Regular reminders for vision tests are sent to the user's device.

[0483] The user confirms the notification and takes the vision test.

[0484] 2. The server analyzes the new data

[0485] The device sends the new visual acuity data to the server, which stores it in a database.

[0486] The AI ​​model recalculates the optimal screen display settings based on the latest visual acuity data and sends them to the device.

[0487] 3. Update screen settings and generate reports

[0488] The device will reflect the new settings and adjust the screen display.

[0489] The server generates a report containing the results of the periodic analysis and sends it to the user.

[0490] As a result, the present invention allows users to easily take visual acuity tests and provides a screen display that is always optimized for their visual acuity. This system also monitors changes in visual acuity and adjusts the visual environment as needed, which is expected to help prevent the user's visual acuity from declining.

[0491] The processing flow will be explained below.

[0492] Step 1:

[0493] The user installs a dedicated application on their PC or smartphone. They then launch the app and create an account by entering basic information such as their name, age, email address, and vision correction method. The information they entered is sent from the device to a server, which then stores it in a database.

[0494] Step 2:

[0495] The user follows the app's instructions to begin the vision test. The test involves reading letters and symbols displayed on the screen, and the device obtains the user's vision data (e.g., 0.8 for the right eye, 0.7 for the left eye).

[0496] Step 3:

[0497] Once the vision test is complete, the device sends the acquired vision data to a cloud server, which stores the received data in a database.

[0498] Step 4:

[0499] The server inputs the visual acuity data stored in the database into an AI model for analysis, which then calculates optimal screen display settings (text size, color, contrast) based on the user's visual acuity data.

[0500] Step 5:

[0501] The server calculates the optimal screen display settings and sends them to the user's device. The settings information sent includes details about font size, color, and contrast.

[0502] Step 6:

[0503] The display screen is automatically adjusted based on the screen setting information received by the device. The user can then check the adjusted screen and make fine adjustments as necessary.

[0504] Step 7:

[0505] The server periodically analyzes the vision data and generates a report based on the results (including changes in vision and recommendations for optimal screen settings), which is then sent to the user via email or in-app notification.

[0506] Step 8:

[0507] The user receives a reminder to periodically take a vision test. When the user takes the test according to the reminder, the device sends new vision data to the server. The server stores the latest data and updates the screen display settings based on it.

[0508] Example 1

[0509] 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."

[0510] Many users experience the inconvenience of declining eyesight, but do not take appropriate measures to avoid the hassle and cost of taking appropriate measures. Furthermore, conventional vision assistance systems are unable to adaptively respond to changes in a user's eyesight, making it difficult to provide a screen display optimized for each individual's eyesight.

[0511] 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.

[0512] In this invention, the server includes a means for storing visual acuity data in a database, a means for analyzing the stored visual acuity data using an AI model and calculating optimal screen display settings, and a means for transmitting the optimal screen display settings to the user's device. This allows the user to easily take a visual acuity test and always provide a screen display optimized for the user's visual acuity. In addition, by continuously monitoring changes in the user's visual acuity and adjusting the visual environment as needed, it is expected that the effect of preventing the user's visual acuity from declining can also be expected.

[0513] "User" refers to the person or entity administering the vision test.

[0514] "Input means" refers to an interface for inputting data for a vision test into an endpoint device.

[0515] "Cloud server" refers to remote computing resources and data storage accessible over the Internet.

[0516] "Visual acuity data" refers to information about a user's visual acuity obtained through a visual acuity test.

[0517] "Database" refers to a system for efficiently storing and retrieving structured or unstructured data.

[0518] An "AI model" refers to a program that uses machine learning algorithms to analyze vision data and make predictions and classifications.

[0519] "Screen display settings" refers to parameters for adjusting the device screen, such as character size, background color, and contrast, to make it easier for the user to visually recognize.

[0520] "Transmission means" refers to a mechanism for transferring data or information from one system or device to another.

[0521] "Regular report" refers to written or electronic data that analyzes visual acuity data and periodically notifies the user of the results.

[0522] "User interface" refers to the interactive screens and controls that allow a user to interact with a system and input or receive information.

[0523] The present invention relates to a vision assistance system that eliminates the inconveniences associated with poor eyesight and provides users with a comfortable visual environment. This system is designed to optimize screen display settings based on the user's visual acuity data. Each component of this system and its specific operation are described below.

[0524] System configuration

[0525] 1. Input means (terminal) for users to take the vision test

[0526] The terminal is a device that can connect to the Internet, such as a PC or smartphone, and has an application installed that allows users to take the vision test. The user uses this application to take the vision test. Input means include a touch screen, keyboard, and mouse.

[0527] 2. Means of transmitting visual acuity data (terminal and server)

[0528] Once the vision test is complete, the device sends the vision data to a cloud server. The device encrypts the data using SSL / TLS protocol to ensure security. The server then stores the received vision data in a database.

[0529] 3. Data analysis method (server)

[0530] The visual acuity data stored on the cloud server is analyzed using an AI model. The AI ​​model is built using machine learning libraries such as TensorFlow and PyTorch. This AI model learns from a large amount of visual acuity data collected in the past and calculates the optimal screen display settings for each individual user. The visual acuity data analyzed includes text size, color, contrast, etc.

[0531] 4. Means of sending optimal screen display settings (server and device)

[0532] The optimal screen display settings calculated by the AI ​​model are sent from the server to the user's device. The device then automatically adjusts the screen display based on the received setting information. During this process, for example, the smartphone's settings menu is automatically changed to adjust the font size and background color.

[0533] 5. Method for generating scheduled reports (server)

[0534] The server periodically analyzes the user's vision data and generates a report based on the analysis, which is then sent to the user via email or in-app notification, containing information about changes in vision, recommendations for screen settings, and other important information.

[0535] Specific examples

[0536] Example 1: First-time user case

[0537] 1. The user takes the eye test

[0538] A user using the system for the first time installs the dedicated app on their smartphone and creates an account. The user starts the vision test and follows the instructions displayed on the screen to evaluate their vision. The test results are sent from the device to the server.

[0539] 2. The server analyzes the data

[0540] The server stores the received visual acuity data in a database and analyzes it using an AI model, which then calculates the optimal screen display settings and sends that information to the device.

[0541] 3. The device optimizes the screen

[0542] The system automatically adjusts the font size, color, and contrast on the screen based on the settings information received by the user's smartphone. The user can then check the adjusted screen and make fine adjustments as necessary.

[0543] Example 2: Regular User Case

[0544] 1. Get reminders for your eye exams

[0545] The user will be notified of periodic reminders for vision tests on their device. The user will check the notifications and take the vision test.

[0546] 2. The server analyzes the new data

[0547] The device sends the new visual acuity data to the server, which stores it in a database. The AI ​​model then recalculates the optimal screen display settings based on the latest visual acuity data and sends them to the device.

[0548] 3. Update screen settings and generate reports

[0549] The device reflects the new setting information and adjusts the screen display. The server generates a report containing the results of the periodic analysis and sends it to the user.

[0550] Prompt Sentence Examples

[0551] The first-time vision test prompt is as follows:

[0552] "Install the app for the first time and take the eye test. Select the direction in which you see the Landolt ring and submit your results. Our server will analyze the results and let you know the optimal screen settings."

[0553] The prompt for the regular vision test is as follows:

[0554] "Please take a vision test once every three months. We will send your latest vision data to our server and you will receive new screen settings. Once you receive the notification, please start the vision test."

[0555] As a result, the present invention allows users to easily conduct visual acuity tests and provides a screen display that is always optimized for their visual acuity. Furthermore, by continuously monitoring changes in visual acuity, it is expected to have the effect of preventing the user's visual acuity from declining.

[0556] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0557] Step 1:

[0558] The user installs a dedicated application on their device and creates an account. When the user starts the vision test, the app displays the vision test instructions. The user then performs a vision test such as a Landolt ring test and enters the results into the app. The input for this step is the vision test data, and the output is to temporarily store the vision test results on the device.

[0559] Step 2:

[0560] Once the vision test is complete, the device sends the vision data to a cloud server. The data is encrypted using SSL / TLS protocol for security. The input of this step is the vision test result, and the output is data transmission to the cloud server.

[0561] Step 3:

[0562] The server receives the visual acuity data and stores it in a database. The database can be MySQL or PostgreSQL. The input of this step is the visual acuity data sent to the cloud server, and the output is storage in the database.

[0563] Step 4:

[0564] The server analyzes the visual acuity data using an AI model. The AI ​​model is built using TensorFlow and PyTorch. Based on past data, visual acuity data is used as input, and optimal screen display settings (text size, color, contrast) are calculated as output. This is the step where data analysis takes place.

[0565] Step 5:

[0566] The server sends the optimal screen display settings to the user's device. This can be done using REST API or WebSocket. The input to this step is the screen display settings calculated by the server, and the output is the settings sent to the device.

[0567] Step 6:

[0568] The device automatically adjusts the screen display based on the setting information received. For example, on an Android device, you can adjust the font size or background color by directly changing the settings menu. The input for this step is the screen display setting received from the server, and the output is the adjusted screen display.

[0569] Step 7:

[0570] The server periodically re-analyzes the visual acuity data and generates a report based on the results, including any changes in visual acuity and recommendations for new screen settings. The input for this step is the periodically collected visual acuity data, and the output is the generated report.

[0571] Step 8:

[0572] The generated report is sent to the user via email or in-app notification, and the user can check their own vision status based on the report contents. The input of this step is the generated report, and the output is a notification to the user.

[0573] Through the above processing steps, the system provides a screen display optimized for the user's eyesight, and by continuously monitoring changes in eyesight, it is expected to be effective in preventing the user's eyesight from deteriorating.

[0574] (Application example 1)

[0575] 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."

[0576] Conventional vision assistance systems have limited themselves to adjusting the screen's text size and contrast, making it difficult for users with poor eyesight to comfortably use online services such as online shopping sites. Furthermore, because appropriate adjustments are not made in response to changes in vision, there is a risk of vision deterioration progressing. Furthermore, the process of users undergoing a vision test and then applying the optimal settings based on the results is complicated. The present invention aims to resolve these issues and provide optimal product display according to visual acuity.

[0577] 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.

[0578] In this invention, the server includes an input means for a user to take a visual acuity test, a means for transmitting the input visual acuity data to the cloud server, a means for analyzing the data received by the server and calculating optimal screen display settings, a means for transmitting the optimal screen display settings to the user's terminal, a means for optimizing the display of product pages based on the analysis results, and a means for displaying product information according to the user's visual acuity. This allows users with poor eyesight to comfortably use online shopping sites, and since appropriate adjustments are automatically made in response to changes in visual acuity, the risk of vision deterioration can be reduced.

[0579] "Input means for a user to take a vision test" refers to the device or software interface that a user uses to take a vision test.

[0580] A "cloud server" is a remote server used to store and process data over the Internet.

[0581] "Visual acuity data" refers to the visual acuity measurement results obtained by a user through a visual acuity test.

[0582] "Optimal screen display settings" refers to settings that adjust the screen's character size, color, contrast, etc. based on the user's eyesight.

[0583] A "user interface" is a screen or operation panel that a user uses to input information and check results.

[0584] A "product page" is a web page on an online shopping site that displays product information, images, prices, etc.

[0585] "Product information" refers to detailed information such as the name, description, and price of a product sold on an online shopping site.

[0586] "Analysis results" refer to evaluations and judgments based on vision test data.

[0587] "Optimization" means to set or adjust something so that it is most effective under certain conditions.

[0588] "Visual acuity-appropriate display" refers to a display method that is customized based on the user's visual acuity.

[0589] The present invention aims to realize a system that provides optimal product display according to visual acuity. Each component of this system and its specific operation will be described below.

[0590] System configuration and operation

[0591] Input means (terminal) for users to take the vision test

[0592] This system uses internet-connected devices such as smartphones and tablets as an input means for users to take the vision test. Users take the vision test using a smartphone or other device with a dedicated application installed. During the vision test, their eyesight is evaluated by following the instructions displayed on the screen.

[0593] Means for sending and receiving visual acuity data (terminal and server)

[0594] Once the vision test is complete, the device sends the acquired vision data to a cloud server, where it is received and stored in a database.

[0595] Data analysis and calculation of optimal screen display settings (server)

[0596] The visual acuity data stored on the cloud server is analyzed using a generative AI model. The AI ​​model is trained based on a large amount of previously collected visual acuity data and calculates the optimal screen display settings for each individual user. These calculated settings include text size, color, and contrast.

[0597] Sending optimal screen display settings and screen customization methods (server and terminal)

[0598] The optimal screen display settings calculated by the AI ​​model are sent from the server to the user's device. The device automatically adjusts the display of the product page based on the received settings, allowing the user to view product information optimized for their eyesight.

[0599] Scheduled report generation and notification method (server and terminal)

[0600] The user's device will periodically receive reminders for vision tests. The user will check the notifications and take the test again. The new vision data will be sent to the server, which will store it in a database. The AI ​​model will then recalculate the optimal screen display settings based on the latest vision data and send it to the device, providing product displays that match the user's latest vision.

[0601] Specific examples

[0602] For first-time users

[0603] 1. Conducting a vision test

[0604] Users install the dedicated app on their smartphone and create an account.

[0605] Begin the vision test and perform the vision assessment as directed.

[0606] The visual acuity data is transmitted to a server.

[0607] 2. Data Analysis and Set Calculations

[0608] The server stores the received visual acuity data in a database and analyzes it using a generative AI model.

[0609] The AI ​​model calculates the optimal screen display settings and sends that information to the device.

[0610] 3. Screen display optimization

[0611] The display of the product page is automatically adjusted based on the setting information received by the user's smartphone.

[0612] The user can check the screen after adjustment and make fine adjustments as necessary.

[0613] For regular users

[0614] 1. Vision test reminder

[0615] Regular reminders for vision tests are sent to the user's device.

[0616] The user confirms the notification and takes the vision test.

[0617] 2. Analysis of new data and reconfiguration

[0618] The device sends the new visual acuity data to the server, which stores it in a database.

[0619] The AI ​​model recalculates the optimal screen display settings based on the latest visual acuity data and sends them to the device.

[0620] 3. Update the display and generate a report

[0621] The device will reflect the new settings and adjust the display of the product page.

[0622] Based on the results of the periodic analysis, the server generates a report and sends it to the user.

[0623] Adding specific examples

[0624] For example, if a user receives the results of a visual acuity test that show "right eye 0.7, left eye 0.8" and wants to send this along with past data to a cloud server, the following could be an example of a prompt sentence:

[0625] Prompt Sentence Examples

[0626] User ID: user_123

[0627] Right eye visual acuity: 0.7

[0628] Left eye visual acuity: 0.8

[0629] Based on this, the present invention can provide optimal product display according to the visual acuity, allowing users to enjoy online shopping in an optimal visual environment that adapts to changes in visual acuity.

[0630] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0631] Step 1:

[0632] Conducting a vision test

[0633] Users install a dedicated app on their smartphone and create an account. After creating an account, they start the vision test via the application. During the vision test, they follow the instructions displayed on the screen to tap and swipe to evaluate their vision. The results of this vision test are obtained as input data. The input data includes the visual acuity value for each eye (e.g., 0.7 for the right eye, 0.8 for the left eye).

[0634] Step 2:

[0635] Submitting visual acuity data

[0636] The device sends the acquired visual acuity data to the cloud server. At this stage, the visual acuity data is sent to the server via the network, and processing on the server side begins. The input data is the results of the visual acuity test (visual acuity value), and the output data is saved in a visual acuity database on the cloud.

[0637] Step 3:

[0638] Data analysis and calculation of optimal display settings

[0639] The server stores the received visual acuity data in a database and analyzes the data using a generative AI model. As a result of the analysis, the optimal screen display settings for each user's visual acuity are calculated. These settings include text size, color, contrast, etc. The input data is visual acuity data and past data, and the optimal screen display settings are obtained as output data.

[0640] Step 4:

[0641] Sending optimal screen display settings and customizing the screen

[0642] The server sends the calculated optimal screen display settings to the user's device. The device reflects the received setting information and automatically adjusts the display of the product page. This allows the user to see product information optimized for their eyesight. The input data are the optimal screen display settings, and the output data is the adjusted product page.

[0643] Step 5:

[0644] Scheduled report generation and notification

[0645] The server periodically analyzes the visual acuity data and generates a report based on the results. The generated report is sent to the user via email or in-app notification. The report includes changes in visual acuity and recommendations for screen settings. The input data is the visual acuity data history, and the generated analysis report is the output data.

[0646] Step 6:

[0647] Eye test reminder notifications

[0648] A visual acuity test reminder is sent to the user's device periodically. The user checks the notification and takes the visual acuity test again, and the latest visual acuity data is sent to the server. The input data is the reminder notification and the user's visual acuity test results, and the output data is updated visual acuity data.

[0649] Through the above process, the system can provide optimal product display tailored to the user's eyesight and automatically adjust according to changes in eyesight.

[0650] 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.

[0651] This invention is a system that combines visual acuity testing and user emotion recognition to provide the user with an optimal visual environment. This system uses an emotion engine to acquire user emotion data and analyzes it together with visual acuity data to achieve more advanced optimization of screen display settings. Below, we will explain each component of this system and its specific operation.

[0652] System configuration

[0653] 1. Input means (terminal) for users to take the vision test

[0654] The terminal is a device that can connect to the internet, such as a PC or smartphone, and has a dedicated application installed. The user uses this application to take the vision test.

[0655] 2. Means for sending and receiving visual acuity data (terminal and server)

[0656] Once the vision test is complete, the device sends the vision data to a cloud server, which stores it in a database and uses it for analysis.

[0657] 3. Data analysis and calculation of optimal screen display settings (server)

[0658] The vision data stored on the cloud server is analyzed by an AI model. The AI ​​model learns from a large amount of previously collected vision and emotional data and calculates the optimal screen display settings for each user's vision and emotional state. The calculated settings include font size, color, contrast, etc.

[0659] 4. Emotion Engine (Terminal)

[0660] The device is equipped with an emotion engine that recognizes the user's emotions. The emotion engine uses sensors such as a camera and microphone to analyze the user's facial expressions and voice to obtain emotional data.

[0661] 5. Means for sending and receiving emotion data (terminal and server)

[0662] The emotion data acquired by the emotion engine is sent to a cloud server along with the visual acuity data. The server stores the received emotion data in a database and uses it for analysis along with the visual acuity data.

[0663] 6. Transmission of optimal screen display settings and screen customization means (server and terminal)

[0664] The optimal screen display settings calculated by the AI ​​model are sent from the server to the user's device. The device automatically adjusts the screen display based on the received setting information, allowing the user to view a screen optimized for their visual acuity and emotional state.

[0665] 7. Generation and notification of regular reports (server and terminal)

[0666] The server periodically analyzes the user's visual acuity and emotional data and generates a report based on the analysis results. The report is sent to the user via email or in-app notification. The report includes changes in visual acuity, emotional state, and recommendations for screen settings.

[0667] Specific examples

[0668] Example 1: First-time user case

[0669] 1. The user performs the visual acuity test and emotion recognition

[0670] Users using the system for the first time install a dedicated app on their smartphone and create an account.

[0671] The user follows the app's instructions to take a vision test, and the device collects vision data.

[0672] The emotion engine analyzes the user's facial expressions and voice to obtain emotional data.

[0673] The terminal transmits the acquired visual acuity data and emotion data to a cloud server.

[0674] 2. The server analyzes the data

[0675] The server stores the received data in a database and analyzes it using an AI model.

[0676] The AI ​​model calculates the optimal screen display settings and sends that information to the device.

[0677] 3. The device optimizes the screen

[0678] The system automatically adjusts the font size, color, and contrast on the screen based on the settings information received by the user's smartphone.

[0679] The user can check the adjusted screen and make fine adjustments as necessary.

[0680] Example 2: Regular User Case

[0681] 1. Receive emotion recognition and eye test reminders

[0682] Periodic reminders for emotion recognition and vision tests are sent to the user's device.

[0683] The user checks the notification and performs a vision test and emotion recognition.

[0684] 2. The server analyzes the new data

[0685] The device sends new data to the server, which stores it in a database.

[0686] The AI ​​model recalculates the optimal screen display settings based on the latest visual acuity and emotional data and sends them to the device.

[0687] 3. Update screen settings and generate reports

[0688] The device will reflect the new settings and adjust the screen display.

[0689] The server generates a report containing the periodic analysis results and sends it to the user.

[0690] As a result, the present invention can provide an optimal visual environment that takes into account the user's visual acuity and emotional state. Furthermore, the system continuously monitors emotional changes, contributing to reducing user stress and providing a comfortable environment.

[0691] The processing flow will be explained below.

[0692] Step 1:

[0693] The user installs a dedicated application on their PC or smartphone. They then launch the app and create an account by entering basic information such as their name, age, email address, and vision correction method. The entered information is sent from the device to the server, which then stores it in a database.

[0694] Step 2:

[0695] The user follows the instructions in the app to begin the vision test. The test involves reading the letters and symbols displayed on the screen, and the device obtains the user's visual acuity data (e.g., 0.8 for the right eye, 0.7 for the left eye).

[0696] Step 3:

[0697] The device's built-in emotion engine analyzes the user's facial expressions and voice to obtain current emotional data, thereby collecting emotional information such as stress and fatigue felt by the user during the test.

[0698] Step 4:

[0699] After the vision test is completed, the device sends the acquired vision data and emotion data to a cloud server, which stores the received data in a database.

[0700] Step 5:

[0701] The server inputs the visual acuity and emotional data stored in the database into an AI model for analysis. The AI ​​model calculates optimal screen display settings (text size, color, contrast) based on the user's visual acuity and emotional data.

[0702] Step 6:

[0703] The server calculates the optimal screen display settings and sends them to the user's device. The settings information sent includes details about font size, color, and contrast.

[0704] Step 7:

[0705] The display screen is automatically adjusted based on the screen setting information received by the device. The user can then check the adjusted screen and make fine adjustments as necessary.

[0706] Step 8:

[0707] The server periodically analyzes the user's visual acuity and emotional data and generates a report based on the results. The report is sent to the user via email or in-app notification. The report includes changes in visual acuity, emotional state, and recommended screen settings.

[0708] Step 9:

[0709] The user receives a reminder to periodically take a vision test. The user then takes the test according to the reminder, and new vision and emotion data are sent from the device to the server. The server stores the latest data and updates the screen display settings based on it.

[0710] Through this series of steps, the user can obtain a visual experience optimized for their visual acuity and emotional state.

[0711] Example 2

[0712] 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."

[0713] While conventional vision testing systems can provide screen display settings based on a user's visual acuity, they are unable to consider the user's emotional state, making it difficult to alleviate visual fatigue and stress. It is also difficult to respond appropriately to periodic changes in visual acuity or fluctuations in emotional state. Furthermore, they lack an intuitive user interface for users to check and fine-tune their own screen settings.

[0714] The identification process 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 an input means for the user to perform a visual acuity test and emotion recognition, a means for transmitting the input visual acuity data and emotion data to the cloud server, a means for the server to analyze the received visual acuity data and emotion data and calculate optimal screen display settings, a means for transmitting the optimal screen display settings to the user's terminal and the terminal automatically adjusting the screen display, a means for providing a user interface for the user to fine-tune the settings, and a means for the server to generate periodic reports based on the analysis results of the visual acuity data and emotion data and notify the user. This allows the user to obtain screen displays optimized for their visual acuity and emotion status, thereby alleviating visual fatigue and stress. Furthermore, the periodic reports allow the user to understand fluctuations in visual acuity and emotion and take appropriate measures.

[0715]

[0716] A "vision test" is a series of procedures for measuring a user's vision condition.

[0717] "Emotion recognition" is a technology that analyzes data such as a user's facial expressions and voice to identify their emotional state.

[0718] "Input means" refers to a device or part thereof that allows a user to perform vision testing and emotion recognition.

[0719] "Visual acuity data" is information about the user's visual acuity obtained through a visual acuity test.

[0720] "Emotion data" is information about the user's emotional state obtained through emotion recognition.

[0721] A "cloud server" refers to a server located in a remote location via the Internet that has the ability to store and analyze large amounts of data.

[0722] A "database" is a collection of information that stores received data in an organized manner and can be used for later analysis and retrieval.

[0723] The "AI model" is an algorithm that learns from large amounts of data, analyzes visual acuity data and emotional data, and calculates optimal screen display settings.

[0724] "Screen display settings" are settings that adjust the character size, color, contrast, etc. on the terminal screen based on the user's eyesight and emotional state.

[0725] A "user interface" refers to an operation screen and means for a user to directly operate and check and adjust screen settings.

[0726] A "regular report" is a report created based on the results of analyzing visual acuity data and emotional data, and includes information about changes in visual acuity and emotional state.

[0727] "Notifications" are messages or alerts that notify the user when new information or settings are created.

[0728]

[0729] This invention is a system that combines visual acuity testing and user emotion recognition to provide the optimal visual environment for the user. This system uses an emotion engine to acquire user emotion data and analyzes it together with visual acuity data to achieve more advanced optimization of screen display settings.

[0730] System configuration

[0731] 1. Input means (terminal) for users to take the vision test

[0732] The terminal is a device that can connect to the Internet, such as a PC or smartphone, and has a dedicated application installed. The user uses this application to take the vision test. The vision test is carried out by following the charts and instructions displayed during the test, and vision data is collected.

[0733] 2. Emotion engine (terminal) for emotion recognition

[0734] The device is equipped with an emotion engine that recognizes the user's emotions. The emotion engine uses sensors such as a camera and microphone to analyze the user's facial expressions and voice to obtain emotion data. For example, while the user is taking an eye test, a camera captures the user's face, and the emotion engine identifies smiling, serious expressions, etc. The microphone is also used to analyze the tone and volume of the user's voice to evaluate the user's emotional state.

[0735] 3. Data transmission and storage (devices and servers)

[0736] After completing the vision test and emotion recognition, the device sends the data to a cloud server. The data is encrypted and securely transmitted. The vision and emotion data received by the cloud server is stored in a database, making it available for later analysis.

[0737] 4. Data analysis and generation of optimization settings (server)

[0738] The cloud server provides the AI ​​model with the visual acuity and emotional data stored in the database. The AI ​​model learns from a large amount of previously collected data and calculates the optimal screen display settings for each user's visual acuity and emotional state. The calculated settings include text size, color, and contrast. For example, if a user shows signs of poor visual acuity and is in a state of high stress, the AI ​​model suggests increasing the text size and changing the color tone to a more subdued one.

[0739] 5. Sending and applying optimization settings (server and terminal)

[0740] The optimal screen display settings calculated by the server are sent to the user's device. The device automatically adjusts the screen display based on the received setting information. The user can then view the optimized screen. Furthermore, the user can make fine adjustments to suit their preferences through the user interface.

[0741] 6. Scheduled report generation and notification (server and terminal)

[0742] The server periodically re-analyzes the user's visual acuity and emotional data and generates a report based on the results. The report includes changes in visual acuity, fluctuations in emotional state, and recommendations for further screen settings. The generated report is sent to the user via email or in-app notification. This allows the user to constantly monitor fluctuations in their visual acuity and emotional state and take appropriate action.

[0743] Specific examples

[0744] Example 1: First-time user case

[0745] 1. The user performs the visual acuity test and emotion recognition

[0746] Users install a dedicated app on their smartphone and create an account.

[0747] Launch the app and follow the instructions to take the vision test. For example, you might be prompted to "Choose the smallest letter you can see."

[0748] The device's camera and microphone are used to record the user's facial expressions and voice, and obtain emotional data.

[0749] The terminal transmits the visual acuity data and the emotion data to the cloud server.

[0750] 2. The server analyzes the data

[0751] The server stores the data in a database and analyzes it using an AI model.

[0752] The AI ​​model calculates the optimal screen display settings and sends that information to the device.

[0753] 3. The device optimizes the screen

[0754] The device automatically adjusts the text size, color, and contrast on the screen based on the settings information it receives.

[0755] The user can check the screen after adjustment and make fine adjustments as necessary.

[0756] Example prompt:

[0757] The user installs a dedicated app and takes a vision test. The device acquires vision and emotion data and sends it to a cloud server. The server analyzes the data and sends optimized screen display settings to the device. The device applies the settings, and the user checks the screen.

[0758] Example 2: Regular User Case

[0759] 1. Receive emotion recognition and eye test reminders

[0760] Periodic reminders for emotion recognition and vision tests are sent to the user's device.

[0761] The user checks the notification and performs a vision test and emotion recognition.

[0762] 2. The server analyzes the new data

[0763] The device sends new data to the server, which stores it in a database.

[0764] The AI ​​model recalculates the optimal screen display settings based on the latest visual acuity and emotional data and sends them to the device.

[0765] 3. Update screen settings and generate reports

[0766] The device will reflect the new settings and adjust the screen display.

[0767] The server generates a report containing the periodic analysis results and sends it to the user.

[0768] Example prompt:

[0769] The user receives periodic reminders to perform vision tests and emotion recognition. The device sends the data to the server. The server analyzes the data and sends the optimal settings to the device. The device refreshes the screen. The server generates a report and sends it to the user.

[0770] As a result, the present invention provides an optimal visual environment that takes into account the user's visual acuity and emotional state. Continuous monitoring of emotional changes contributes to reducing stress and providing a comfortable user environment. This system is a comprehensive solution that allows users to better manage their visual acuity and emotional state and enjoy an optimal visual environment.

[0771] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0772]

[0773] Step 1: The device performs a vision test

[0774] The user launches the app and creates an account. They then follow the app's instructions to begin the vision test. Specifically, the device displays a vision test chart and asks the user to select the smallest letter they can see. Based on this input, the device generates vision data and evaluates its accuracy.

[0775] Step 2: The device performs emotion recognition

[0776] During the vision test, the device's camera and microphone record the user's facial expressions and voice. The emotion engine analyzes this data to identify the user's emotional state. The emotional data obtained through this analysis reflects the user's stress level and concentration in real time.

[0777] Step 3: The device sends the visual acuity data and emotion data to the cloud server.

[0778] After completing the vision test and emotion recognition, the device sends the generated vision and emotion data to a cloud server, where the data is encrypted and securely transmitted to the cloud server.

[0779] Step 4: The server saves the data to the database

[0780] The cloud server stores the received visual acuity and emotion data in a database, maintaining data integrity and efficiently managing it for future analysis.

[0781] Step 5: The server provides the data to the AI ​​model

[0782] The AI ​​model uses the stored visual acuity and emotional data to learn from past data and analyze the user's data. Specifically, it calculates optimal screen display settings, taking into account trends in visual acuity and changes in emotional state.

[0783] Step 6: The server calculates the optimal display settings

[0784] The AI ​​model analyzes the data and calculates the optimal screen display settings for the user, including text size, color, and contrast. For example, a user with poor eyesight might be suggested to increase text size and enhance color contrast.

[0785] Step 7: The server sends the optimization settings to the device

[0786] The server calculates the optimal screen display settings and sends them to the user's device. The settings information is encrypted and sent securely.

[0787] Step 8: Your device adjusts the display

[0788] The device automatically adjusts the size, color, and contrast of on-screen text based on the settings information it receives, and users can review these settings and fine-tune them as needed through the user interface.

[0789] Step 9: Server generates scheduled reports

[0790] The server periodically re-analyzes the user's visual acuity and emotional data and generates a report based on the results, including changes in visual acuity, fluctuations in emotional state, and recommendations for further screen settings.

[0791] Step 10: The server notifies the user of the report

[0792] The server generates reports and sends them to the user via email or in-app notifications, allowing the user to keep track of fluctuations in their visual acuity and emotional state and take appropriate action.

[0793] (Application example 2)

[0794] 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."

[0795] Conventional vision testing systems optimize screen displays by taking into account only the user's visual acuity data, making it difficult to optimize the display while also reflecting the user's emotional state. Furthermore, for users to comfortably navigate environments with a lot of visual information, such as virtual stores, it is necessary to optimize the visual environment by taking into account not only visual acuity but also emotions. However, no such technology has existed to date. Therefore, there has been a demand for the development of a system that provides an optimal visual environment that simultaneously considers the user's visual acuity and emotional state.

[0796] The specific processing by the specific 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 transmitting input visual acuity data and emotion data to a cloud server, means for the server to analyze the received data and calculate optimal screen display settings using a generative AI model, means for transmitting the optimal screen display settings to the user's terminal, and means for the terminal to automatically adjust the screen display based on the setting information received. This makes it possible to provide a comfortable visual environment that simultaneously takes into account the user's visual acuity state and emotional state.

[0797] 1. "Eye Test" means a series of tests administered to assess a User's eyesight.

[0798] 2. "Input means" refers to the device or interface through which the user conducts the vision test.

[0799] 3. "Cloud Server" means a remote server for storing, processing, and analyzing data via the Internet.

[0800] 4. "Visual acuity data" means information about a user's visual acuity, such as the numerical values ​​or results obtained from a visual acuity test.

[0801] 5. "Emotion data" refers to information indicating the user's emotional state obtained from facial expressions, voice, etc.

[0802] 6. A "generative AI model" is an artificial intelligence algorithm that is trained based on large amounts of data and used to perform data analysis and predictions.

[0803] 7. "Screen display settings" are settings for adjusting visual elements of the screen, such as font size, color, and contrast.

[0804] 8. "Terminal" refers to a computer device used by a User, such as a smartphone or PC.

[0805] 9. "Automatic adjustment means" means a function that allows the system to automatically optimize the screen display without user intervention.

[0806] 10. "User interface" refers to the screens and operating means through which a user interacts with and operates a system.

[0807] The present invention is a system that provides an optimal visual environment by simultaneously considering the user's visual acuity and emotional state. This system automatically optimizes the screen display based on the results of a visual acuity test and emotional recognition.

[0808] System configuration

[0809] 1. Input means (terminal) for users to take the vision test

[0810] The terminal uses a device such as a smartphone or a head-mounted display (HMD) that allows the user to take the vision test. The user takes the vision test through the terminal, and the results of the vision test are acquired by the terminal as numerical data.

[0811] 2. Means for sending and receiving visual acuity data and emotion data (terminal and cloud server)

[0812] After the eye test is completed, the device sends the eye data to the cloud server. At the same time, the device is equipped with an emotion engine that analyzes the user's facial expressions and voice to obtain emotion data. This data is then sent to the cloud server in parallel.

[0813] 3. Data analysis and calculation of optimal screen display settings (cloud server)

[0814] The visual acuity and emotion data stored on the cloud server is analyzed using a generative AI model. The generative AI model is trained based on a large amount of previously collected visual acuity and emotion data, and calculates the optimal screen display settings for each user's visual acuity and emotional state. These settings include text size, color, contrast, etc.

[0815] 4. Transmission of optimal screen display settings and screen customization methods (cloud server and device)

[0816] The optimal screen display settings analyzed and calculated by the AI ​​model are sent from the cloud server to the user's device. The device then automatically adjusts the screen display based on the received setting information, ensuring that the user always experiences the optimal visual environment.

[0817] 5. Providing a User Interface

[0818] The user is also provided with a user interface on the device to check and further adjust the screen settings based on the results of the vision test and emotion recognition. The user can make fine adjustments through this interface.

[0819] Example

[0820] Example 1: First-time user

[0821] 1. The user puts on the head-mounted display and starts the application.

[0822] 2. The application will automatically start the vision test and emotion recognition and send the data to the cloud server.

[0823] 3. The cloud server analyzes the received visual acuity data and emotion data and calculates the optimal screen display settings.

[0824] 4. Optimal setting information is sent to the device, and the user's visual environment is automatically adjusted.

[0825] 5. Users can enjoy a comfortable shopping experience in a virtual store.

[0826] Example 2: For a regular user

[0827] 1. Periodic reminder notifications for vision tests and emotion recognition will be sent to your device.

[0828] 2. The user checks the notification and performs a visual acuity test and emotion recognition.

[0829] 3. The device sends the visual acuity data and emotion data to the cloud server.

[0830] 4. The cloud server analyzes the latest data and recalculates the optimal screen display settings.

[0831] 5. The new configuration information is sent to the device, and the user's visual environment is updated.

[0832] Prompt Sentence Examples

[0833] "An application that optimizes the visual environment for users wearing head-mounted displays. It collects the user's emotional and visual acuity data in real time, analyzes it on a cloud server, and provides optimal screen settings."

[0834] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0835] Step 1:

[0836] The user uses an input means for taking a visual acuity test to obtain visual acuity data.

[0837] Specific operation: The user launches an application on their smartphone or head-mounted display and takes a vision test. The vision test collects responses to the displayed test patterns and letters, and stores them as vision data on the device. In this step, the user's vision response is input, and vision data is obtained as output.

[0838] Step 2:

[0839] The terminal uses an emotion engine to acquire emotion data of the user.

[0840] Specific operation: The device's camera and microphone are used to analyze the user's facial expressions and voice in real time. The emotion engine processes this data and outputs the user's emotional state as numerical or categorical data. In this step, the user's facial expressions and voice are taken as input, and emotion data is generated as output.

[0841] Step 3:

[0842] The visual acuity data and emotion data are sent to a cloud server.

[0843] Specific operation: The terminal transmits the acquired visual acuity data and emotion data as a data packet to the cloud server. In this step, the visual acuity data and emotion data are input, and the transmitted data packet is output.

[0844] Step 4:

[0845] The cloud server analyzes the received data using a generative AI model and calculates the optimal screen display settings.

[0846] Specific operation: The cloud server stores the received visual acuity data and emotion data in a database and runs a generative AI model based on these data. The AI ​​model compares these data with past data and calculates the optimal screen display settings (e.g., font size, color, contrast, etc.) for the user's condition. In this step, the data sent to the cloud server is the input, and the optimal screen display settings are obtained as the output.

[0847] Step 5:

[0848] Send optimal screen display settings to the user's device.

[0849] Specific operation: The cloud server sends the calculated screen display settings to the terminal as a data packet. In this step, the optimal screen display settings are input, and the transmitted setting information is output.

[0850] Step 6:

[0851] The device automatically adjusts the screen display based on the received setting information.

[0852] Specific operation: The device applies the received screen display setting information and automatically adjusts the font size, color, contrast, etc. This setting allows the user to experience an optimized visual environment. In this step, the received setting information is the input, and the adjusted screen display is the output.

[0853] Step 7:

[0854] A user interface allows the user to view and fine-tune the screen settings.

[0855] Specific operation: The device provides a user interface, and the user checks the initial screen display based on the results of the vision test and emotion recognition, and fine-tunes it as necessary. In this step, the input is screen setting information, and the output is a screen display optimized for the user.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] [Third embodiment]

[0860] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0861] 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.

[0862] 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).

[0863] 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.

[0864] 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.

[0865] 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).

[0866] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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."

[0872] The present invention relates to a system that eliminates the inconveniences associated with poor eyesight and provides a user with a comfortable visual environment. The components of this system and their specific operations are described below.

[0873] System configuration

[0874] 1. Input means (terminal) for users to take the vision test

[0875] The terminal is a device that can connect to the Internet, such as a PC or smartphone, and has an application installed that allows users to take the vision test. Users use this application to take the vision test.

[0876] 2. Means for sending and receiving visual acuity data (terminal and server)

[0877] Once the vision test is complete, the device sends the vision data to a cloud server, which stores it in a database and uses it for analysis.

[0878] 3. Data analysis and calculation of optimal screen display settings (server)

[0879] The visual acuity data stored on the cloud server is analyzed by an AI model. The AI ​​model learns from a large amount of visual acuity data collected in the past and calculates the optimal screen display settings for each individual user. The calculated settings include font size, color, contrast, etc.

[0880] 4. Means for sending optimal screen display settings and customizing the screen (server and terminal)

[0881] The optimal screen display settings calculated by the AI ​​model are sent from the server to the user's device. The device automatically adjusts the screen display based on the received setting information, allowing the user to view a screen optimized for their eyesight.

[0882] 5. Generation and notification of regular reports (server and terminal)

[0883] The server periodically analyzes the user's vision data and generates a report based on the analysis results, which is sent to the user via email or in-app notification, containing information about changes in vision, recommendations for screen settings, and other important information.

[0884] Specific examples

[0885] Example 1: First-time user case

[0886] 1. The user takes the eye test

[0887] Users using the system for the first time install a dedicated app on their smartphone and create an account.

[0888] The user starts the vision test and follows the instructions on the screen to perform the vision assessment.

[0889] The test results are sent from the terminal to the server.

[0890] 2. The server analyzes the data

[0891] The server stores the received vision data in a database and analyzes it using an AI model.

[0892] The AI ​​model calculates the optimal screen display settings and sends that information to the device.

[0893] 3. The device optimizes the screen

[0894] The system automatically adjusts the font size, color, and contrast on the screen based on the settings information received by the user's smartphone.

[0895] The user can check the adjusted screen and make fine adjustments as necessary.

[0896] Example 2: Regular User Case

[0897] 1. Get reminders for your eye exams

[0898] Regular reminders for vision tests are sent to the user's device.

[0899] The user confirms the notification and takes the vision test.

[0900] 2. The server analyzes the new data

[0901] The device sends the new visual acuity data to the server, which stores it in a database.

[0902] The AI ​​model recalculates the optimal screen display settings based on the latest visual acuity data and sends them to the device.

[0903] 3. Update screen settings and generate reports

[0904] The device will reflect the new settings and adjust the screen display.

[0905] The server generates a report containing the results of the periodic analysis and sends it to the user.

[0906] As a result, the present invention allows users to easily take visual acuity tests and provides a screen display that is always optimized for their visual acuity. This system also monitors changes in visual acuity and adjusts the visual environment as needed, which is expected to help prevent the user's visual acuity from declining.

[0907] The processing flow will be explained below.

[0908] Step 1:

[0909] The user installs a dedicated application on their PC or smartphone. They then launch the app and create an account by entering basic information such as their name, age, email address, and vision correction method. The information they entered is sent from the device to a server, which then stores it in a database.

[0910] Step 2:

[0911] The user follows the app's instructions to begin the vision test. The test involves reading letters and symbols displayed on the screen, and the device obtains the user's vision data (e.g., 0.8 for the right eye, 0.7 for the left eye).

[0912] Step 3:

[0913] Once the vision test is complete, the device sends the acquired vision data to a cloud server, which stores the received data in a database.

[0914] Step 4:

[0915] The server inputs the visual acuity data stored in the database into an AI model for analysis, which then calculates optimal screen display settings (text size, color, contrast) based on the user's visual acuity data.

[0916] Step 5:

[0917] The server calculates the optimal screen display settings and sends them to the user's device. The settings information sent includes details about font size, color, and contrast.

[0918] Step 6:

[0919] The display screen is automatically adjusted based on the screen setting information received by the device. The user can then check the adjusted screen and make fine adjustments as necessary.

[0920] Step 7:

[0921] The server periodically analyzes the vision data and generates a report based on the results (including changes in vision and recommendations for optimal screen settings), which is then sent to the user via email or in-app notification.

[0922] Step 8:

[0923] The user receives a reminder to periodically take a vision test. When the user takes the test according to the reminder, the device sends new vision data to the server. The server stores the latest data and updates the screen display settings based on it.

[0924] Example 1

[0925] 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."

[0926] Many users experience the inconvenience of declining eyesight, but do not take appropriate measures to avoid the hassle and cost of taking appropriate measures. Furthermore, conventional vision assistance systems are unable to adaptively respond to changes in a user's eyesight, making it difficult to provide a screen display optimized for each individual's eyesight.

[0927] 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.

[0928] In this invention, the server includes a means for storing visual acuity data in a database, a means for analyzing the stored visual acuity data using an AI model and calculating optimal screen display settings, and a means for transmitting the optimal screen display settings to the user's device. This allows the user to easily take a visual acuity test and always provide a screen display optimized for the user's visual acuity. In addition, by continuously monitoring changes in the user's visual acuity and adjusting the visual environment as needed, it is expected that the effect of preventing the user's visual acuity from declining can also be expected.

[0929] "User" refers to the person or entity administering the vision test.

[0930] "Input means" refers to an interface for inputting data for a vision test into an endpoint device.

[0931] "Cloud server" refers to remote computing resources and data storage accessible over the Internet.

[0932] "Visual acuity data" refers to information about a user's visual acuity obtained through a visual acuity test.

[0933] "Database" refers to a system for efficiently storing and retrieving structured or unstructured data.

[0934] An "AI model" refers to a program that uses machine learning algorithms to analyze vision data and make predictions and classifications.

[0935] "Screen display settings" refers to parameters for adjusting the device screen, such as character size, background color, and contrast, to make it easier for the user to visually recognize.

[0936] "Transmission means" refers to a mechanism for transferring data or information from one system or device to another.

[0937] "Regular report" refers to written or electronic data that analyzes visual acuity data and periodically notifies the user of the results.

[0938] "User interface" refers to the interactive screens and controls that allow a user to interact with a system and input or receive information.

[0939] The present invention relates to a vision assistance system that eliminates the inconveniences associated with poor eyesight and provides users with a comfortable visual environment. This system is designed to optimize screen display settings based on the user's visual acuity data. Each component of this system and its specific operation are described below.

[0940] System configuration

[0941] 1. Input means (terminal) for users to take the vision test

[0942] The terminal is a device that can connect to the Internet, such as a PC or smartphone, and has an application installed that allows users to take the vision test. The user uses this application to take the vision test. Input means include a touch screen, keyboard, and mouse.

[0943] 2. Means of transmitting visual acuity data (terminal and server)

[0944] Once the vision test is complete, the device sends the vision data to a cloud server. The device encrypts the data using SSL / TLS protocol to ensure security. The server then stores the received vision data in a database.

[0945] 3. Data analysis method (server)

[0946] The visual acuity data stored on the cloud server is analyzed using an AI model. The AI ​​model is built using machine learning libraries such as TensorFlow and PyTorch. This AI model learns from a large amount of visual acuity data collected in the past and calculates the optimal screen display settings for each individual user. The visual acuity data analyzed includes text size, color, contrast, etc.

[0947] 4. Means of sending optimal screen display settings (server and device)

[0948] The optimal screen display settings calculated by the AI ​​model are sent from the server to the user's device. The device then automatically adjusts the screen display based on the received setting information. During this process, for example, the smartphone's settings menu is automatically changed to adjust the font size and background color.

[0949] 5. Method for generating scheduled reports (server)

[0950] The server periodically analyzes the user's vision data and generates a report based on the analysis, which is then sent to the user via email or in-app notification, containing information about changes in vision, recommendations for screen settings, and other important information.

[0951] Specific examples

[0952] Example 1: First-time user case

[0953] 1. The user takes the eye test

[0954] A user using the system for the first time installs the dedicated app on their smartphone and creates an account. The user starts the vision test and performs a visual acuity evaluation by following the instructions displayed on the screen. The test results are sent from the device to the server.

[0955] 2. The server analyzes the data

[0956] The server stores the received visual acuity data in a database and analyzes it using an AI model, which calculates the optimal screen display settings and sends that information to the device.

[0957] 3. The device optimizes the screen

[0958] The system automatically adjusts the font size, color, and contrast on the screen based on the settings information received by the user's smartphone. The user can then check the adjusted screen and make fine adjustments as necessary.

[0959] Example 2: Regular User Case

[0960] 1. Get reminders for your eye exams

[0961] The user will be notified of periodic reminders for vision tests on their device. The user will check the notifications and take the vision test.

[0962] 2. The server analyzes the new data

[0963] The device sends the new visual acuity data to the server, which stores it in a database. The AI ​​model then recalculates the optimal screen display settings based on the latest visual acuity data and sends them to the device.

[0964] 3. Update screen settings and generate reports

[0965] The device reflects the new setting information and adjusts the screen display. The server generates a report containing the results of the periodic analysis and sends it to the user.

[0966] Prompt Sentence Examples

[0967] The first-time vision test prompt is as follows:

[0968] "Install the app for the first time and take the eye test. Select the direction in which you see the Landolt ring and submit your results. Our server will analyze the results and let you know the optimal screen settings."

[0969] The prompt for the regular vision test is as follows:

[0970] "Please take a vision test once every three months. We will send your latest vision data to our server and you will receive new screen settings. When you receive the notification, please start the vision test."

[0971] As a result, the present invention allows users to easily conduct visual acuity tests and provides a screen display that is always optimized for their visual acuity. Furthermore, by continuously monitoring changes in visual acuity, it is expected to have the effect of preventing the user's visual acuity from declining.

[0972] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0973] Step 1:

[0974] The user installs a dedicated application on their device and creates an account. When the user starts the vision test, the app displays the vision test instructions. The user then performs a vision test such as a Landolt ring test and enters the results into the app. The input for this step is the vision test data, and the output is to temporarily store the vision test results on the device.

[0975] Step 2:

[0976] Once the vision test is complete, the device sends the vision data to a cloud server. The data is encrypted using SSL / TLS protocol for security. The input of this step is the vision test result, and the output is data transmission to the cloud server.

[0977] Step 3:

[0978] The server receives the visual acuity data and stores it in a database. The database can be MySQL or PostgreSQL. The input of this step is the visual acuity data sent to the cloud server, and the output is storage in the database.

[0979] Step 4:

[0980] The server analyzes the visual acuity data using an AI model. The AI ​​model is built using TensorFlow and PyTorch. Based on past data, visual acuity data is used as input, and optimal screen display settings (text size, color, contrast) are calculated as output. This is the step where data analysis takes place.

[0981] Step 5:

[0982] The server sends the optimal screen display settings to the user's device. This can be done using REST API or WebSocket. The input to this step is the screen display settings calculated by the server, and the output is the settings sent to the device.

[0983] Step 6:

[0984] The device automatically adjusts the screen display based on the setting information received. For example, on an Android device, you can adjust the font size or background color by directly changing the settings menu. The input for this step is the screen display setting received from the server, and the output is the adjusted screen display.

[0985] Step 7:

[0986] The server periodically re-analyzes the visual acuity data and generates a report based on the results, including any changes in visual acuity and recommendations for new screen settings. The input for this step is the periodically collected visual acuity data, and the output is the generated report.

[0987] Step 8:

[0988] The generated report is sent to the user via email or in-app notification, and the user can check their own vision status based on the report contents. The input of this step is the generated report, and the output is a notification to the user.

[0989] Through the above processing steps, the system provides a screen display optimized for the user's eyesight, and by continuously monitoring changes in eyesight, it is expected to help prevent the user's eyesight from deteriorating.

[0990] (Application example 1)

[0991] 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."

[0992] Conventional vision assistance systems have limited themselves to adjusting the screen's text size and contrast, making it difficult for users with poor eyesight to comfortably use online services such as online shopping sites. Furthermore, because appropriate adjustments are not made in response to changes in vision, there is a risk of vision deterioration progressing. Furthermore, the process of users undergoing a vision test and then applying the optimal settings based on the results is complicated. The present invention aims to resolve these issues and provide optimal product display according to visual acuity.

[0993] 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.

[0994] In this invention, the server includes an input means for a user to take a visual acuity test, a means for transmitting the input visual acuity data to the cloud server, a means for analyzing the data received by the server and calculating optimal screen display settings, a means for transmitting the optimal screen display settings to the user's terminal, a means for optimizing the display of product pages based on the analysis results, and a means for displaying product information according to the user's visual acuity. This allows users with poor eyesight to comfortably use online shopping sites, and since appropriate adjustments are automatically made in response to changes in visual acuity, the risk of vision deterioration can be reduced.

[0995] "Input means for a user to take a vision test" refers to the device or software interface that a user uses to take a vision test.

[0996] A "cloud server" is a remote server used to store and process data over the Internet.

[0997] "Visual acuity data" refers to the visual acuity measurement results obtained by a user through a visual acuity test.

[0998] "Optimal screen display settings" refers to settings that adjust the screen's character size, color, contrast, etc. based on the user's eyesight.

[0999] A "user interface" is a screen or operation panel that a user uses to input information and check results.

[1000] A "product page" is a web page on an online shopping site that displays product information, images, prices, etc.

[1001] "Product information" refers to detailed information such as the name, description, and price of a product sold on an online shopping site.

[1002] "Analysis results" refer to evaluations and judgments based on vision test data.

[1003] "Optimization" means to set or adjust something so that it is most effective under certain conditions.

[1004] "Visual acuity-appropriate display" refers to a display method that is customized based on the user's visual acuity.

[1005] The present invention aims to realize a system that provides optimal product display according to visual acuity. Each component of this system and its specific operation will be described below.

[1006] System configuration and operation

[1007] Input means (terminal) for users to take the vision test

[1008] This system uses internet-connected devices such as smartphones and tablets as an input means for users to take the vision test. Users take the vision test using a smartphone or other device with a dedicated application installed. During the vision test, their eyesight is evaluated by following the instructions displayed on the screen.

[1009] Means for sending and receiving visual acuity data (terminal and server)

[1010] Once the vision test is complete, the device sends the acquired vision data to a cloud server, where it is received and stored in a database.

[1011] Data analysis and calculation of optimal screen display settings (server)

[1012] The visual acuity data stored on the cloud server is analyzed using a generative AI model. The AI ​​model is trained based on a large amount of visual acuity data collected in the past, and calculates the optimal screen display settings for each user's visual acuity. These calculated settings include text size, color, and contrast.

[1013] Sending optimal screen display settings and screen customization methods (server and terminal)

[1014] The optimal screen display settings calculated by the AI ​​model are sent from the server to the user's device. The device automatically adjusts the display of the product page based on the received settings, allowing the user to view product information optimized for their eyesight.

[1015] Scheduled report generation and notification method (server and terminal)

[1016] The user's device will periodically receive reminders for vision tests. The user will check the notifications and take the test again. The new vision data will be sent to the server, which will store it in a database. The AI ​​model will then recalculate the optimal screen display settings based on the latest vision data and send it to the device, providing product displays that match the user's latest vision.

[1017] Specific examples

[1018] For first-time users

[1019] 1. Conducting a vision test

[1020] Users install the dedicated app on their smartphone and create an account.

[1021] Begin the vision test and perform the vision assessment as directed.

[1022] The visual acuity data is transmitted to a server.

[1023] 2. Data Analysis and Set Calculations

[1024] The server stores the received visual acuity data in a database and analyzes it using a generative AI model.

[1025] The AI ​​model calculates the optimal screen display settings and sends that information to the device.

[1026] 3. Screen display optimization

[1027] The display of the product page is automatically adjusted based on the setting information received by the user's smartphone.

[1028] The user can check the screen after adjustment and make fine adjustments as necessary.

[1029] For regular users

[1030] 1. Vision test reminder notifications

[1031] Regular reminders for vision tests are sent to the user's device.

[1032] The user confirms the notification and takes the vision test.

[1033] 2. Analysis of new data and reconfiguration

[1034] The device sends the new visual acuity data to the server, which stores it in a database.

[1035] The AI ​​model recalculates the optimal screen display settings based on the latest visual acuity data and sends them to the device.

[1036] 3. Update the display and generate a report

[1037] The device will reflect the new settings and adjust the display of the product page.

[1038] Based on the results of the periodic analysis, the server generates a report and sends it to the user.

[1039] Adding specific examples

[1040] For example, if a user receives the results of a visual acuity test that show "right eye 0.7, left eye 0.8" and wants to send this along with past data to a cloud server, the following could be an example of a prompt sentence:

[1041] Prompt Sentence Examples

[1042] User ID: user_123

[1043] Right eye visual acuity: 0.7

[1044] Left eye visual acuity: 0.8

[1045] Based on this, the present invention can provide optimal product display according to the visual acuity, allowing users to enjoy online shopping in an optimal visual environment that adapts to changes in visual acuity.

[1046] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1047] Step 1:

[1048] Conducting a vision test

[1049] Users install a dedicated app on their smartphone and create an account. After creating an account, they start the vision test via the application. During the vision test, they follow the instructions displayed on the screen to tap and swipe to evaluate their vision. The results of this vision test are obtained as input data. The input data includes the visual acuity value for each eye (e.g., 0.7 for the right eye, 0.8 for the left eye).

[1050] Step 2:

[1051] Submitting visual acuity data

[1052] The device sends the acquired visual acuity data to the cloud server. At this stage, the visual acuity data is sent to the server via the network, and processing on the server side begins. The input data is the results of the visual acuity test (visual acuity value), and the output data is saved in a visual acuity database on the cloud.

[1053] Step 3:

[1054] Data analysis and calculation of optimal display settings

[1055] The server stores the received visual acuity data in a database and analyzes the data using a generative AI model. As a result of the analysis, the optimal screen display settings for each user's visual acuity are calculated. These settings include text size, color, contrast, etc. The input data is visual acuity data and past data, and the optimal screen display settings are obtained as output data.

[1056] Step 4:

[1057] Sending optimal screen display settings and customizing the screen

[1058] The server sends the calculated optimal screen display settings to the user's device. The device reflects the received setting information and automatically adjusts the display of the product page. This allows the user to see product information optimized for their eyesight. The input data are the optimal screen display settings, and the output data is the adjusted product page.

[1059] Step 5:

[1060] Scheduled report generation and notification

[1061] The server periodically analyzes the visual acuity data and generates a report based on the results. The generated report is sent to the user via email or in-app notification. The report includes changes in visual acuity and recommendations for screen settings. The input data is the visual acuity data history, and the generated analysis report is the output data.

[1062] Step 6:

[1063] Eye test reminder notifications

[1064] A visual acuity test reminder is sent to the user's device periodically. The user checks the notification and takes the visual acuity test again, and the latest visual acuity data is sent to the server. The input data is the reminder notification and the user's visual acuity test results, and the output data is updated visual acuity data.

[1065] Through the above process, the system can provide optimal product display tailored to the user's visual acuity and automatically adjust according to changes in visual acuity.

[1066] 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.

[1067] This invention is a system that combines visual acuity testing and user emotion recognition to provide the user with an optimal visual environment. This system uses an emotion engine to acquire user emotion data and analyzes it together with visual acuity data to achieve more advanced optimization of screen display settings. Below, we will explain each component of this system and its specific operation.

[1068] System configuration

[1069] 1. Input means (terminal) for users to take the vision test

[1070] The terminal is a device that can connect to the internet, such as a PC or smartphone, and has a dedicated application installed. The user uses this application to take the vision test.

[1071] 2. Means for sending and receiving visual acuity data (terminal and server)

[1072] Once the vision test is complete, the device sends the vision data to a cloud server, which stores it in a database and uses it for analysis.

[1073] 3. Data analysis and calculation of optimal screen display settings (server)

[1074] The vision data stored on the cloud server is analyzed by an AI model. The AI ​​model learns from a large amount of previously collected vision and emotional data and calculates the optimal screen display settings for each user's vision and emotional state. The calculated settings include font size, color, contrast, etc.

[1075] 4. Emotion Engine (Terminal)

[1076] The device is equipped with an emotion engine that recognizes the user's emotions. The emotion engine uses sensors such as a camera and microphone to analyze the user's facial expressions and voice to obtain emotional data.

[1077] 5. Means for sending and receiving emotion data (terminal and server)

[1078] The emotion data acquired by the emotion engine is sent to a cloud server along with the visual acuity data. The server stores the received emotion data in a database and uses it for analysis along with the visual acuity data.

[1079] 6. Transmission of optimal screen display settings and screen customization means (server and terminal)

[1080] The optimal screen display settings calculated by the AI ​​model are sent from the server to the user's device. The device automatically adjusts the screen display based on the received setting information, allowing the user to view a screen optimized for their visual acuity and emotional state.

[1081] 7. Generation and notification of regular reports (server and terminal)

[1082] The server periodically analyzes the user's visual acuity and emotional data and generates a report based on the analysis results. The report is sent to the user via email or in-app notification. The report includes changes in visual acuity, emotional state, and recommendations for screen settings.

[1083] Specific examples

[1084] Example 1: First-time user case

[1085] 1. The user performs the visual acuity test and emotion recognition

[1086] Users using the system for the first time install a dedicated app on their smartphone and create an account.

[1087] The user follows the app's instructions to take a vision test, and the device collects vision data.

[1088] The emotion engine analyzes the user's facial expressions and voice to obtain emotional data.

[1089] The terminal transmits the acquired visual acuity data and emotion data to a cloud server.

[1090] 2. The server analyzes the data

[1091] The server stores the received data in a database and analyzes it using an AI model.

[1092] The AI ​​model calculates the optimal screen display settings and sends that information to the device.

[1093] 3. The device optimizes the screen

[1094] The system automatically adjusts the font size, color, and contrast on the screen based on the settings information received by the user's smartphone.

[1095] The user can check the adjusted screen and make fine adjustments as necessary.

[1096] Example 2: Regular User Case

[1097] 1. Receive emotion recognition and eye test reminders

[1098] Periodic reminders for emotion recognition and vision tests are sent to the user's device.

[1099] The user checks the notification and performs a vision test and emotion recognition.

[1100] 2. The server analyzes the new data

[1101] The device sends new data to the server, which stores it in a database.

[1102] The AI ​​model recalculates the optimal screen display settings based on the latest visual acuity and emotional data and sends them to the device.

[1103] 3. Update screen settings and generate reports

[1104] The device will reflect the new settings and adjust the screen display.

[1105] The server generates a report containing the periodic analysis results and sends it to the user.

[1106] As a result, the present invention can provide an optimal visual environment that takes into account the user's visual acuity and emotional state. Furthermore, the system continuously monitors emotional changes, contributing to reducing user stress and providing a comfortable environment.

[1107] The processing flow will be explained below.

[1108] Step 1:

[1109] The user installs a dedicated application on their PC or smartphone. They then launch the app and create an account by entering basic information such as their name, age, email address, and vision correction method. The entered information is sent from the device to the server, which then stores it in a database.

[1110] Step 2:

[1111] The user follows the instructions in the app to begin the vision test. The test involves reading the letters and symbols displayed on the screen, and the device obtains the user's visual acuity data (e.g., 0.8 for the right eye, 0.7 for the left eye).

[1112] Step 3:

[1113] The device's built-in emotion engine analyzes the user's facial expressions and voice to obtain current emotional data, thereby collecting emotional information such as stress and fatigue felt by the user during the test.

[1114] Step 4:

[1115] After the vision test is completed, the device sends the acquired vision data and emotion data to a cloud server, which stores the received data in a database.

[1116] Step 5:

[1117] The server inputs the visual acuity and emotional data stored in the database into an AI model for analysis. The AI ​​model calculates optimal screen display settings (text size, color, contrast) based on the user's visual acuity and emotional data.

[1118] Step 6:

[1119] The server calculates the optimal screen display settings and sends them to the user's device. The settings information sent includes details about font size, color, and contrast.

[1120] Step 7:

[1121] The display screen is automatically adjusted based on the screen setting information received by the device. The user can then check the adjusted screen and make fine adjustments as necessary.

[1122] Step 8:

[1123] The server periodically analyzes the user's visual acuity and emotional data and generates a report based on the results. The report is sent to the user via email or in-app notification. The report includes changes in visual acuity, emotional state, and recommended screen settings.

[1124] Step 9:

[1125] The user receives a reminder to periodically take a vision test. The user then takes the test according to the reminder, and new vision and emotion data are sent from the device to the server. The server stores the latest data and updates the screen display settings based on it.

[1126] Through this series of steps, the user can obtain a visual experience optimized for their visual acuity and emotional state.

[1127] Example 2

[1128] 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."

[1129] While conventional vision testing systems can provide screen display settings based on a user's visual acuity, they are unable to consider the user's emotional state, making it difficult to alleviate visual fatigue and stress. It is also difficult to respond appropriately to periodic changes in visual acuity or fluctuations in emotional state. Furthermore, they lack an intuitive user interface for users to check and fine-tune their own screen settings.

[1130] The identification process 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 an input means for the user to perform a visual acuity test and emotion recognition, a means for transmitting the input visual acuity data and emotion data to the cloud server, a means for the server to analyze the received visual acuity data and emotion data and calculate optimal screen display settings, a means for transmitting the optimal screen display settings to the user's terminal and the terminal automatically adjusting the screen display, a means for providing a user interface for the user to fine-tune the settings, and a means for the server to generate periodic reports based on the analysis results of the visual acuity data and emotion data and notify the user. This allows the user to obtain screen displays optimized for their visual acuity and emotion status, thereby alleviating visual fatigue and stress. Furthermore, the periodic reports allow the user to understand fluctuations in visual acuity and emotion and take appropriate measures.

[1131]

[1132] A "vision test" is a series of procedures for measuring a user's vision condition.

[1133] "Emotion recognition" is a technology that analyzes data such as a user's facial expressions and voice to identify their emotional state.

[1134] "Input means" refers to a device or part thereof that allows a user to perform vision testing and emotion recognition.

[1135] "Visual acuity data" is information about the user's visual acuity obtained through a visual acuity test.

[1136] "Emotion data" is information about the user's emotional state obtained through emotion recognition.

[1137] A "cloud server" refers to a server located in a remote location via the Internet that has the ability to store and analyze large amounts of data.

[1138] A "database" is a collection of information that stores received data in an organized manner and can be used for later analysis and retrieval.

[1139] The "AI model" is an algorithm that learns from large amounts of data, analyzes visual acuity data and emotional data, and calculates optimal screen display settings.

[1140] "Screen display settings" are settings that adjust the character size, color, contrast, etc. on the terminal screen based on the user's eyesight and emotional state.

[1141] A "user interface" refers to an operation screen and means for a user to directly operate and check and adjust screen settings.

[1142] A "regular report" is a report created based on the results of analyzing visual acuity data and emotional data, and includes information about changes in visual acuity and emotional state.

[1143] "Notifications" are messages or alerts that notify the user when new information or settings are created.

[1144]

[1145] This invention is a system that combines visual acuity testing and user emotion recognition to provide the optimal visual environment for the user. This system uses an emotion engine to acquire user emotion data and analyzes it together with visual acuity data to achieve more advanced optimization of screen display settings.

[1146] System configuration

[1147] 1. Input means (terminal) for users to take the vision test

[1148] The terminal is a device that can connect to the Internet, such as a PC or smartphone, and has a dedicated application installed. The user uses this application to take the vision test. The vision test is carried out by following the charts and instructions displayed during the test, and vision data is collected.

[1149] 2. Emotion engine (terminal) for emotion recognition

[1150] The device is equipped with an emotion engine that recognizes the user's emotions. The emotion engine uses sensors such as a camera and microphone to analyze the user's facial expressions and voice to obtain emotion data. For example, while the user is taking an eye test, a camera captures the user's face, and the emotion engine identifies smiling, serious expressions, etc. The microphone is also used to analyze the tone and volume of the user's voice to evaluate the user's emotional state.

[1151] 3. Data transmission and storage (devices and servers)

[1152] After completing the vision test and emotion recognition, the device sends the data to a cloud server. The data is encrypted and securely transmitted. The vision and emotion data received by the cloud server is stored in a database, making it available for later analysis.

[1153] 4. Data analysis and generation of optimization settings (server)

[1154] The cloud server provides the AI ​​model with the visual acuity and emotional data stored in the database. The AI ​​model learns from a large amount of previously collected data and calculates the optimal screen display settings for each user's visual acuity and emotional state. The calculated settings include text size, color, and contrast. For example, if a user shows signs of poor visual acuity and is in a state of high stress, the AI ​​model suggests increasing the text size and changing the color tone to a more subdued one.

[1155] 5. Sending and applying optimization settings (server and terminal)

[1156] The optimal screen display settings calculated by the server are sent to the user's device. The device automatically adjusts the screen display based on the received setting information. The user can then view the optimized screen. Furthermore, the user can make fine adjustments to suit their preferences through the user interface.

[1157] 6. Scheduled report generation and notification (server and terminal)

[1158] The server periodically re-analyzes the user's visual acuity and emotional data and generates a report based on the results. The report includes changes in visual acuity, fluctuations in emotional state, and recommendations for further screen settings. The generated report is sent to the user via email or in-app notification. This allows the user to constantly monitor fluctuations in their visual acuity and emotional state and take appropriate action.

[1159] Specific examples

[1160] Example 1: First-time user case

[1161] 1. The user performs the visual acuity test and emotion recognition

[1162] Users install a dedicated app on their smartphone and create an account.

[1163] Launch the app and follow the instructions to take the vision test. For example, you might be prompted to "Choose the smallest letter you can see."

[1164] The device's camera and microphone are used to record the user's facial expressions and voice, and obtain emotional data.

[1165] The terminal transmits the visual acuity data and the emotion data to the cloud server.

[1166] 2. The server analyzes the data

[1167] The server stores the data in a database and analyzes it using an AI model.

[1168] The AI ​​model calculates the optimal screen display settings and sends that information to the device.

[1169] 3. The device optimizes the screen

[1170] The device automatically adjusts the text size, color, and contrast on the screen based on the settings information it receives.

[1171] The user can check the screen after adjustment and make fine adjustments as necessary.

[1172] Example prompt:

[1173] The user installs a dedicated app and takes a vision test. The device acquires vision and emotion data and sends it to a cloud server. The server analyzes the data and sends optimized screen display settings to the device. The device applies the settings, and the user checks the screen.

[1174] Example 2: Regular User Case

[1175] 1. Receive emotion recognition and eye test reminders

[1176] Periodic reminders for emotion recognition and vision tests are sent to the user's device.

[1177] The user checks the notification and performs a vision test and emotion recognition.

[1178] 2. The server analyzes the new data

[1179] The device sends new data to the server, which stores it in a database.

[1180] The AI ​​model recalculates the optimal screen display settings based on the latest visual acuity and emotional data and sends them to the device.

[1181] 3. Update screen settings and generate reports

[1182] The device will reflect the new settings and adjust the screen display.

[1183] The server generates a report containing the periodic analysis results and sends it to the user.

[1184] Example prompt:

[1185] The user receives periodic reminders to perform vision tests and emotion recognition. The device sends the data to the server. The server analyzes the data and sends the optimal settings to the device. The device refreshes the screen. The server generates a report and sends it to the user.

[1186] As a result, the present invention provides an optimal visual environment that takes into account the user's visual acuity and emotional state. Continuous monitoring of emotional changes contributes to reducing stress and providing a comfortable user environment. This system is a comprehensive solution that allows users to better manage their visual acuity and emotional state and enjoy an optimal visual environment.

[1187] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1188]

[1189] Step 1: The device performs a vision test

[1190] The user launches the app and creates an account. They then follow the app's instructions to begin the vision test. Specifically, the device displays a vision test chart and asks the user to select the smallest letter they can see. Based on this input, the device generates vision data and evaluates its accuracy.

[1191] Step 2: The device performs emotion recognition

[1192] During the vision test, the device's camera and microphone record the user's facial expressions and voice. The emotion engine analyzes this data to identify the user's emotional state. The emotional data obtained through this analysis reflects the user's stress level and concentration in real time.

[1193] Step 3: The device sends the visual acuity data and emotion data to the cloud server.

[1194] After completing the vision test and emotion recognition, the device sends the generated vision and emotion data to a cloud server, where the data is encrypted and securely transmitted to the cloud server.

[1195] Step 4: The server saves the data to the database

[1196] The cloud server stores the received visual acuity and emotion data in a database, maintaining data integrity and efficiently managing it for future analysis.

[1197] Step 5: The server provides the data to the AI ​​model

[1198] The AI ​​model uses the stored visual acuity and emotional data to learn from past data and analyze the user's data. Specifically, it calculates optimal screen display settings, taking into account trends in visual acuity and changes in emotional state.

[1199] Step 6: The server calculates the optimal display settings

[1200] The AI ​​model analyzes the data and calculates the optimal screen display settings for the user, including text size, color, and contrast. For example, a user with poor eyesight might be suggested to increase text size and enhance color contrast.

[1201] Step 7: The server sends the optimization settings to the device

[1202] The server calculates the optimal screen display settings and sends them to the user's device. The settings information is encrypted and sent securely.

[1203] Step 8: Your device adjusts the display

[1204] The device automatically adjusts the size, color, and contrast of on-screen text based on the settings information it receives, and users can review these settings and fine-tune them as needed through the user interface.

[1205] Step 9: Server generates scheduled reports

[1206] The server periodically re-analyzes the user's visual acuity and emotional data and generates a report based on the results, including changes in visual acuity, fluctuations in emotional state, and recommendations for further screen settings.

[1207] Step 10: The server notifies the user of the report

[1208] The server generates reports and sends them to the user via email or in-app notifications, allowing the user to keep track of fluctuations in their visual acuity and emotional state and take appropriate action.

[1209] (Application example 2)

[1210] 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."

[1211] Conventional vision testing systems optimize screen displays by taking into account only the user's visual acuity data, making it difficult to optimize the display while also reflecting the user's emotional state. Furthermore, for users to comfortably navigate environments with a lot of visual information, such as virtual stores, it is necessary to optimize the visual environment by taking into account not only visual acuity but also emotions. However, no such technology has existed to date. Therefore, there has been a demand for the development of a system that provides an optimal visual environment that simultaneously considers the user's visual acuity and emotional state.

[1212] The specific processing by the specific 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 transmitting input visual acuity data and emotion data to a cloud server, means for the server to analyze the received data and calculate optimal screen display settings using a generative AI model, means for transmitting the optimal screen display settings to the user's terminal, and means for the terminal to automatically adjust the screen display based on the setting information received. This makes it possible to provide a comfortable visual environment that simultaneously takes into account the user's visual acuity state and emotional state.

[1213] 1. "Eye Test" means a series of tests administered to assess a User's eyesight.

[1214] 2. "Input means" refers to the device or interface through which the user conducts the vision test.

[1215] 3. "Cloud Server" means a remote server for storing, processing, and analyzing data via the Internet.

[1216] 4. "Visual acuity data" means information about a user's visual acuity, such as the numerical values ​​or results obtained from a visual acuity test.

[1217] 5. "Emotion data" refers to information indicating the user's emotional state obtained from facial expressions, voice, etc.

[1218] 6. A "generative AI model" is an artificial intelligence algorithm that is trained based on large amounts of data and used to perform data analysis and predictions.

[1219] 7. "Screen display settings" are settings for adjusting visual elements of the screen, such as font size, color, and contrast.

[1220] 8. "Terminal" refers to a computer device used by a User, such as a smartphone or PC.

[1221] 9. "Automatic adjustment means" means a function that allows the system to automatically optimize the screen display without user intervention.

[1222] 10. "User interface" refers to the screens and operating means through which a user interacts with and operates a system.

[1223] The present invention is a system that provides an optimal visual environment by simultaneously considering the user's visual acuity and emotional state. This system automatically optimizes the screen display based on the results of a visual acuity test and emotional recognition.

[1224] System configuration

[1225] 1. Input means (terminal) for users to take the vision test

[1226] The terminal uses a device such as a smartphone or a head-mounted display (HMD) that allows the user to take the vision test. The user takes the vision test through the terminal, and the results of the vision test are acquired by the terminal as numerical data.

[1227] 2. Means for sending and receiving visual acuity data and emotion data (terminal and cloud server)

[1228] After the eye test is completed, the device sends the eye data to the cloud server. At the same time, the device is equipped with an emotion engine that analyzes the user's facial expressions and voice to obtain emotion data. This data is then sent to the cloud server in parallel.

[1229] 3. Data analysis and calculation of optimal screen display settings (cloud server)

[1230] The visual acuity and emotion data stored on the cloud server is analyzed using a generative AI model. The generative AI model is trained based on a large amount of previously collected visual acuity and emotion data, and calculates the optimal screen display settings for each user's visual acuity and emotional state. These settings include text size, color, contrast, etc.

[1231] 4. Transmission of optimal screen display settings and screen customization methods (cloud server and device)

[1232] The optimal screen display settings analyzed and calculated by the AI ​​model are sent from the cloud server to the user's device. The device then automatically adjusts the screen display based on the received setting information, ensuring that the user always experiences the optimal visual environment.

[1233] 5. Providing a User Interface

[1234] The user is also provided with a user interface on the device to check and further adjust the screen settings based on the results of the vision test and emotion recognition. The user can make fine adjustments through this interface.

[1235] Example

[1236] Example 1: First-time user

[1237] 1. The user puts on the head-mounted display and starts the application.

[1238] 2. The application will automatically start the vision test and emotion recognition and send the data to the cloud server.

[1239] 3. The cloud server analyzes the received visual acuity data and emotion data and calculates the optimal screen display settings.

[1240] 4. Optimal setting information is sent to the device, and the user's visual environment is automatically adjusted.

[1241] 5. Users can enjoy a comfortable shopping experience in a virtual store.

[1242] Example 2: For a regular user

[1243] 1. Periodic reminder notifications for vision tests and emotion recognition will be sent to your device.

[1244] 2. The user checks the notification and performs a visual acuity test and emotion recognition.

[1245] 3. The device sends the visual acuity data and emotion data to the cloud server.

[1246] 4. The cloud server analyzes the latest data and recalculates the optimal screen display settings.

[1247] 5. The new configuration information is sent to the device, and the user's visual environment is updated.

[1248] Prompt Sentence Examples

[1249] "An application that optimizes the visual environment for users wearing head-mounted displays. It collects the user's emotional and visual acuity data in real time, analyzes it on a cloud server, and provides optimal screen settings."

[1250] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1251] Step 1:

[1252] The user uses an input means for taking a visual acuity test to obtain visual acuity data.

[1253] Specific operation: The user launches an application on their smartphone or head-mounted display and takes a vision test. The vision test collects responses to the displayed test patterns and letters, and stores them as vision data on the device. In this step, the user's vision response is input, and vision data is obtained as output.

[1254] Step 2:

[1255] The terminal uses an emotion engine to acquire emotion data of the user.

[1256] Specific operation: The device's camera and microphone are used to analyze the user's facial expressions and voice in real time. The emotion engine processes this data and outputs the user's emotional state as numerical or categorical data. In this step, the user's facial expressions and voice are taken as input, and emotion data is generated as output.

[1257] Step 3:

[1258] The visual acuity data and emotion data are sent to a cloud server.

[1259] Specific operation: The terminal transmits the acquired visual acuity data and emotion data as a data packet to the cloud server. In this step, the visual acuity data and emotion data are input, and the transmitted data packet is output.

[1260] Step 4:

[1261] The cloud server analyzes the received data using a generative AI model and calculates the optimal screen display settings.

[1262] Specific operation: The cloud server stores the received visual acuity data and emotion data in a database and runs a generative AI model based on these data. The AI ​​model compares these data with past data and calculates the optimal screen display settings (e.g., font size, color, contrast, etc.) for the user's condition. In this step, the data sent to the cloud server is the input, and the optimal screen display settings are obtained as the output.

[1263] Step 5:

[1264] Send optimal screen display settings to the user's device.

[1265] Specific operation: The cloud server sends the calculated screen display settings to the terminal as a data packet. In this step, the optimal screen display settings are input, and the transmitted setting information is output.

[1266] Step 6:

[1267] The device automatically adjusts the screen display based on the received setting information.

[1268] Specific operation: The device applies the received screen display setting information and automatically adjusts the font size, color, contrast, etc. This setting allows the user to experience an optimized visual environment. In this step, the received setting information is the input, and the adjusted screen display is the output.

[1269] Step 7:

[1270] A user interface allows the user to view and fine-tune the screen settings.

[1271] Specific operation: The device provides a user interface, and the user checks the initial screen display based on the results of the vision test and emotion recognition, and fine-tunes it as necessary. In this step, the input is screen setting information, and the output is a screen display optimized for the user.

[1272] 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.

[1273] 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.

[1274] 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.

[1275] [Fourth embodiment]

[1276] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1277] 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.

[1278] 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).

[1279] 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.

[1280] 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.

[1281] 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).

[1282] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1283] 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.

[1284] 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.

[1285] 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.

[1286] 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.

[1287] 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.

[1288] 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."

[1289] The present invention relates to a system that eliminates the inconveniences associated with poor eyesight and provides a user with a comfortable visual environment. The components of this system and their specific operations are described below.

[1290] System configuration

[1291] 1. Input means (terminal) for users to take the vision test

[1292] The terminal is a device that can connect to the Internet, such as a PC or smartphone, and has an application installed that allows users to take the vision test. Users use this application to take the vision test.

[1293] 2. Means for sending and receiving visual acuity data (terminal and server)

[1294] Once the vision test is complete, the device sends the vision data to a cloud server, which stores it in a database and uses it for analysis.

[1295] 3. Data analysis and calculation of optimal screen display settings (server)

[1296] The visual acuity data stored on the cloud server is analyzed by an AI model. The AI ​​model learns from a large amount of visual acuity data collected in the past and calculates the optimal screen display settings for each individual user. The calculated settings include font size, color, contrast, etc.

[1297] 4. Means for sending optimal screen display settings and customizing the screen (server and terminal)

[1298] The optimal screen display settings calculated by the AI ​​model are sent from the server to the user's device. The device automatically adjusts the screen display based on the received setting information, allowing the user to view a screen optimized for their eyesight.

[1299] 5. Generation and notification of regular reports (server and terminal)

[1300] The server periodically analyzes the user's vision data and generates a report based on the analysis results, which is sent to the user via email or in-app notification, containing information about changes in vision, recommendations for screen settings, and other important information.

[1301] Specific examples

[1302] Example 1: First-time user case

[1303] 1. The user takes the eye test

[1304] Users using the system for the first time install a dedicated app on their smartphone and create an account.

[1305] The user starts the vision test and follows the instructions on the screen to perform the vision assessment.

[1306] The test results are sent from the terminal to the server.

[1307] 2. The server analyzes the data

[1308] The server stores the received vision data in a database and analyzes it using an AI model.

[1309] The AI ​​model calculates the optimal screen display settings and sends that information to the device.

[1310] 3. The device optimizes the screen

[1311] The system automatically adjusts the font size, color, and contrast on the screen based on the settings information received by the user's smartphone.

[1312] The user can check the adjusted screen and make fine adjustments as necessary.

[1313] Example 2: Regular User Case

[1314] 1. Get reminders for your eye exams

[1315] Regular reminders for vision tests are sent to the user's device.

[1316] The user confirms the notification and takes the vision test.

[1317] 2. The server analyzes the new data

[1318] The device sends the new visual acuity data to the server, which stores it in a database.

[1319] The AI ​​model recalculates the optimal screen display settings based on the latest visual acuity data and sends them to the device.

[1320] 3. Update screen settings and generate reports

[1321] The device will reflect the new settings and adjust the screen display.

[1322] The server generates a report containing the results of the periodic analysis and sends it to the user.

[1323] As a result, the present invention allows users to easily take visual acuity tests and provides a screen display that is always optimized for their visual acuity. This system also monitors changes in visual acuity and adjusts the visual environment as needed, which is expected to help prevent the user's visual acuity from declining.

[1324] The processing flow will be explained below.

[1325] Step 1:

[1326] The user installs a dedicated application on their PC or smartphone. They then launch the app and create an account by entering basic information such as their name, age, email address, and vision correction method. The information they entered is sent from the device to a server, which then stores it in a database.

[1327] Step 2:

[1328] The user follows the app's instructions to begin the vision test. The test involves reading letters and symbols displayed on the screen, and the device obtains the user's vision data (e.g., 0.8 for the right eye, 0.7 for the left eye).

[1329] Step 3:

[1330] Once the vision test is complete, the device sends the acquired vision data to a cloud server, which stores the received data in a database.

[1331] Step 4:

[1332] The server inputs the visual acuity data stored in the database into an AI model for analysis, which then calculates optimal screen display settings (text size, color, contrast) based on the user's visual acuity data.

[1333] Step 5:

[1334] The server calculates the optimal screen display settings and sends them to the user's device. The settings information sent includes details about font size, color, and contrast.

[1335] Step 6:

[1336] The display screen is automatically adjusted based on the screen setting information received by the device. The user can then check the adjusted screen and make fine adjustments as necessary.

[1337] Step 7:

[1338] The server periodically analyzes the vision data and generates a report based on the results (including changes in vision and recommendations for optimal screen settings), which is then sent to the user via email or in-app notification.

[1339] Step 8:

[1340] The user receives a reminder to periodically take a vision test. When the user takes the test according to the reminder, the device sends new vision data to the server. The server stores the latest data and updates the screen display settings based on it.

[1341] Example 1

[1342] 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."

[1343] Many users experience the inconvenience of declining eyesight, but do not take appropriate measures to avoid the hassle and cost of taking appropriate measures. Furthermore, conventional vision assistance systems are unable to adaptively respond to changes in a user's eyesight, making it difficult to provide a screen display optimized for each individual's eyesight.

[1344] 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.

[1345] In this invention, the server includes a means for storing visual acuity data in a database, a means for analyzing the stored visual acuity data using an AI model and calculating optimal screen display settings, and a means for transmitting the optimal screen display settings to the user's device. This allows the user to easily take a visual acuity test and always provide a screen display optimized for the user's visual acuity. In addition, by continuously monitoring changes in the user's visual acuity and adjusting the visual environment as needed, it is expected that the effect of preventing the user's visual acuity from declining can also be expected.

[1346] "User" refers to the person or entity administering the vision test.

[1347] "Input means" refers to an interface for inputting data for a vision test into an endpoint device.

[1348] "Cloud server" refers to remote computing resources and data storage accessible over the Internet.

[1349] "Visual acuity data" refers to information about a user's visual acuity obtained through a visual acuity test.

[1350] "Database" refers to a system for efficiently storing and retrieving structured or unstructured data.

[1351] An "AI model" refers to a program that uses machine learning algorithms to analyze vision data and make predictions and classifications.

[1352] "Screen display settings" refers to parameters for adjusting the device screen, such as character size, background color, and contrast, to make it easier for the user to visually recognize.

[1353] "Transmission means" refers to a mechanism for transferring data or information from one system or device to another.

[1354] "Regular report" refers to written or electronic data that analyzes visual acuity data and periodically notifies the user of the results.

[1355] "User interface" refers to the interactive screens and controls that allow a user to interact with a system and input or receive information.

[1356] The present invention relates to a vision assistance system that eliminates the inconveniences associated with poor eyesight and provides users with a comfortable visual environment. This system is designed to optimize screen display settings based on the user's visual acuity data. Each component of this system and its specific operation are described below.

[1357] System configuration

[1358] 1. Input means (terminal) for users to take the vision test

[1359] The terminal is a device that can connect to the Internet, such as a PC or smartphone, and has an application installed that allows users to take the vision test. The user uses this application to take the vision test. Input means include a touch screen, keyboard, and mouse.

[1360] 2. Means of transmitting visual acuity data (terminal and server)

[1361] Once the vision test is complete, the device sends the vision data to a cloud server. The device encrypts the data using SSL / TLS protocol to ensure security. The server then stores the received vision data in a database.

[1362] 3. Data analysis method (server)

[1363] The visual acuity data stored on the cloud server is analyzed using an AI model. The AI ​​model is built using machine learning libraries such as TensorFlow and PyTorch. This AI model learns from a large amount of visual acuity data collected in the past and calculates the optimal screen display settings for each individual user. The visual acuity data analyzed includes text size, color, contrast, etc.

[1364] 4. Means of sending optimal screen display settings (server and device)

[1365] The optimal screen display settings calculated by the AI ​​model are sent from the server to the user's device. The device then automatically adjusts the screen display based on the received setting information. During this process, for example, the smartphone's settings menu is automatically changed to adjust the font size and background color.

[1366] 5. Method for generating scheduled reports (server)

[1367] The server periodically analyzes the user's vision data and generates a report based on the analysis, which is then sent to the user via email or in-app notification, containing information about changes in vision, recommendations for screen settings, and other important information.

[1368] Specific examples

[1369] Example 1: First-time user case

[1370] 1. The user takes the eye test

[1371] A user using the system for the first time installs the dedicated app on their smartphone and creates an account. The user starts the vision test and performs a visual acuity evaluation by following the instructions displayed on the screen. The test results are sent from the device to the server.

[1372] 2. The server analyzes the data

[1373] The server stores the received visual acuity data in a database and analyzes it using an AI model, which calculates the optimal screen display settings and sends that information to the device.

[1374] 3. The device optimizes the screen

[1375] The system automatically adjusts the font size, color, and contrast on the screen based on the settings information received by the user's smartphone. The user can then check the adjusted screen and make fine adjustments as necessary.

[1376] Example 2: Regular User Case

[1377] 1. Get reminders for your eye exams

[1378] The user will be notified of periodic reminders for vision tests on their device. The user will check the notifications and take the vision test.

[1379] 2. The server analyzes the new data

[1380] The device sends the new visual acuity data to the server, which stores it in a database. The AI ​​model then recalculates the optimal screen display settings based on the latest visual acuity data and sends them to the device.

[1381] 3. Update screen settings and generate reports

[1382] The device reflects the new setting information and adjusts the screen display. The server generates a report containing the results of the periodic analysis and sends it to the user.

[1383] Prompt Sentence Examples

[1384] The first-time vision test prompt is as follows:

[1385] "Install the app for the first time and take the eye test. Select the direction in which you see the Landolt ring and submit your results. Our server will analyze the results and let you know the optimal screen settings."

[1386] The prompt for the regular vision test is as follows:

[1387] "Please take a vision test once every three months. We will send your latest vision data to our server and you will receive new screen settings. When you receive the notification, please start the vision test."

[1388] As a result, the present invention allows users to easily conduct visual acuity tests and provides a screen display that is always optimized for their visual acuity. Furthermore, by continuously monitoring changes in visual acuity, it is expected to have the effect of preventing the user's visual acuity from declining.

[1389] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1390] Step 1:

[1391] The user installs a dedicated application on their device and creates an account. When the user starts the vision test, the app displays the vision test instructions. The user then performs a vision test such as a Landolt ring test and enters the results into the app. The input for this step is the vision test data, and the output is to temporarily store the vision test results on the device.

[1392] Step 2:

[1393] Once the vision test is complete, the device sends the vision data to a cloud server. The data is encrypted using SSL / TLS protocol for security. The input of this step is the vision test result, and the output is data transmission to the cloud server.

[1394] Step 3:

[1395] The server receives the visual acuity data and stores it in a database. The database can be MySQL or PostgreSQL. The input of this step is the visual acuity data sent to the cloud server, and the output is storage in the database.

[1396] Step 4:

[1397] The server analyzes the visual acuity data using an AI model. The AI ​​model is built using TensorFlow and PyTorch. Based on past data, visual acuity data is used as input, and optimal screen display settings (text size, color, contrast) are calculated as output. This is the step where data analysis takes place.

[1398] Step 5:

[1399] The server sends the optimal screen display settings to the user's device. This can be done using REST API or WebSocket. The input to this step is the screen display settings calculated by the server, and the output is the settings sent to the device.

[1400] Step 6:

[1401] The device automatically adjusts the screen display based on the setting information received. For example, on an Android device, you can adjust the font size or background color by directly changing the settings menu. The input for this step is the screen display setting received from the server, and the output is the adjusted screen display.

[1402] Step 7:

[1403] The server periodically re-analyzes the visual acuity data and generates a report based on the results, including any changes in visual acuity and recommendations for new screen settings. The input for this step is the periodically collected visual acuity data, and the output is the generated report.

[1404] Step 8:

[1405] The generated report is sent to the user via email or in-app notification, and the user can check their own vision status based on the report contents. The input of this step is the generated report, and the output is a notification to the user.

[1406] Through the above processing steps, the system provides a screen display optimized for the user's eyesight, and by continuously monitoring changes in eyesight, it is expected to help prevent the user's eyesight from deteriorating.

[1407] (Application example 1)

[1408] 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."

[1409] Conventional vision assistance systems have limited themselves to adjusting the screen's text size and contrast, making it difficult for users with poor eyesight to comfortably use online services such as online shopping sites. Furthermore, because appropriate adjustments are not made in response to changes in vision, there is a risk of vision deterioration progressing. Furthermore, the process of users undergoing a vision test and then applying the optimal settings based on the results is complicated. The present invention aims to resolve these issues and provide optimal product display according to visual acuity.

[1410] 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.

[1411] In this invention, the server includes an input means for a user to take a visual acuity test, a means for transmitting the input visual acuity data to the cloud server, a means for analyzing the data received by the server and calculating optimal screen display settings, a means for transmitting the optimal screen display settings to the user's terminal, a means for optimizing the display of product pages based on the analysis results, and a means for displaying product information according to the user's visual acuity. This allows users with poor eyesight to comfortably use online shopping sites, and since appropriate adjustments are automatically made in response to changes in visual acuity, the risk of vision deterioration can be reduced.

[1412] "Input means for a user to take a vision test" refers to the device or software interface that a user uses to take a vision test.

[1413] A "cloud server" is a remote server used to store and process data over the Internet.

[1414] "Visual acuity data" refers to the visual acuity measurement results obtained by a user through a visual acuity test.

[1415] "Optimal screen display settings" refers to settings that adjust the screen's character size, color, contrast, etc. based on the user's eyesight.

[1416] A "user interface" is a screen or operation panel that a user uses to input information and check results.

[1417] A "product page" is a web page on an online shopping site that displays product information, images, prices, etc.

[1418] "Product information" refers to detailed information such as the name, description, and price of a product sold on an online shopping site.

[1419] "Analysis results" refer to evaluations and judgments based on vision test data.

[1420] "Optimization" means to set or adjust something so that it is most effective under certain conditions.

[1421] "Visual acuity-appropriate display" refers to a display method that is customized based on the user's visual acuity.

[1422] The present invention aims to realize a system that provides optimal product display according to visual acuity. Each component of this system and its specific operation will be described below.

[1423] System configuration and operation

[1424] Input means (terminal) for users to take the vision test

[1425] This system uses internet-connected devices such as smartphones and tablets as an input means for users to take the vision test. Users take the vision test using a smartphone or other device with a dedicated application installed. During the vision test, their eyesight is evaluated by following the instructions displayed on the screen.

[1426] Means for sending and receiving visual acuity data (terminal and server)

[1427] Once the vision test is complete, the device sends the acquired vision data to a cloud server, where it is received and stored in a database.

[1428] Data analysis and calculation of optimal screen display settings (server)

[1429] The visual acuity data stored on the cloud server is analyzed using a generative AI model. The AI ​​model is trained based on a large amount of visual acuity data collected in the past, and calculates the optimal screen display settings for each user's visual acuity. These calculated settings include text size, color, and contrast.

[1430] Sending optimal screen display settings and screen customization methods (server and terminal)

[1431] The optimal screen display settings calculated by the AI ​​model are sent from the server to the user's device. The device automatically adjusts the display of the product page based on the received settings, allowing the user to view product information optimized for their eyesight.

[1432] Scheduled report generation and notification method (server and terminal)

[1433] The user's device will periodically receive reminders for vision tests. The user will check the notifications and take the test again. The new vision data will be sent to the server, which will store it in a database. The AI ​​model will then recalculate the optimal screen display settings based on the latest vision data and send it to the device, providing product displays that match the user's latest vision.

[1434] Specific examples

[1435] For first-time users

[1436] 1. Conducting a vision test

[1437] Users install the dedicated app on their smartphone and create an account.

[1438] Begin the vision test and perform the vision assessment as directed.

[1439] The visual acuity data is transmitted to a server.

[1440] 2. Data Analysis and Set Calculations

[1441] The server stores the received visual acuity data in a database and analyzes it using a generative AI model.

[1442] The AI ​​model calculates the optimal screen display settings and sends that information to the device.

[1443] 3. Screen display optimization

[1444] The display of the product page is automatically adjusted based on the setting information received by the user's smartphone.

[1445] The user can check the screen after adjustment and make fine adjustments as necessary.

[1446] For regular users

[1447] 1. Vision test reminder notifications

[1448] Regular reminders for vision tests are sent to the user's device.

[1449] The user confirms the notification and takes the vision test.

[1450] 2. Analysis of new data and reconfiguration

[1451] The device sends the new visual acuity data to the server, which stores it in a database.

[1452] The AI ​​model recalculates the optimal screen display settings based on the latest visual acuity data and sends them to the device.

[1453] 3. Update the display and generate a report

[1454] The device will reflect the new settings and adjust the display of the product page.

[1455] Based on the results of the periodic analysis, the server generates a report and sends it to the user.

[1456] Adding specific examples

[1457] For example, if a user receives the results of a visual acuity test that show "right eye 0.7, left eye 0.8" and wants to send this along with past data to a cloud server, the following could be an example of a prompt sentence:

[1458] Prompt Sentence Examples

[1459] User ID: user_123

[1460] Right eye visual acuity: 0.7

[1461] Left eye visual acuity: 0.8

[1462] Based on this, the present invention can provide optimal product display according to the visual acuity, allowing users to enjoy online shopping in an optimal visual environment that adapts to changes in visual acuity.

[1463] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1464] Step 1:

[1465] Conducting a vision test

[1466] Users install a dedicated app on their smartphone and create an account. After creating an account, they start the vision test via the application. During the vision test, they follow the instructions displayed on the screen to tap and swipe to evaluate their vision. The results of this vision test are obtained as input data. The input data includes the visual acuity value for each eye (e.g., 0.7 for the right eye, 0.8 for the left eye).

[1467] Step 2:

[1468] Submitting visual acuity data

[1469] The device sends the acquired visual acuity data to the cloud server. At this stage, the visual acuity data is sent to the server via the network, and processing on the server side begins. The input data is the results of the visual acuity test (visual acuity value), and the output data is saved in a visual acuity database on the cloud.

[1470] Step 3:

[1471] Data analysis and calculation of optimal display settings

[1472] The server stores the received visual acuity data in a database and analyzes the data using a generative AI model. As a result of the analysis, the optimal screen display settings for each user's visual acuity are calculated. These settings include text size, color, contrast, etc. The input data is visual acuity data and past data, and the optimal screen display settings are obtained as output data.

[1473] Step 4:

[1474] Sending optimal screen display settings and customizing the screen

[1475] The server sends the calculated optimal screen display settings to the user's device. The device reflects the received setting information and automatically adjusts the display of the product page. This allows the user to see product information optimized for their eyesight. The input data are the optimal screen display settings, and the output data is the adjusted product page.

[1476] Step 5:

[1477] Scheduled report generation and notification

[1478] The server periodically analyzes the visual acuity data and generates a report based on the results. The generated report is sent to the user via email or in-app notification. The report includes changes in visual acuity and recommendations for screen settings. The input data is the visual acuity data history, and the generated analysis report is the output data.

[1479] Step 6:

[1480] Eye test reminder notifications

[1481] A visual acuity test reminder is sent to the user's device periodically. The user checks the notification and takes the visual acuity test again, and the latest visual acuity data is sent to the server. The input data is the reminder notification and the user's visual acuity test results, and the output data is updated visual acuity data.

[1482] Through the above process, the system can provide optimal product display tailored to the user's visual acuity and automatically adjust according to changes in visual acuity.

[1483] 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.

[1484] This invention is a system that combines visual acuity testing and user emotion recognition to provide the user with an optimal visual environment. This system uses an emotion engine to acquire user emotion data and analyzes it together with visual acuity data to achieve more advanced optimization of screen display settings. Below, we will explain each component of this system and its specific operation.

[1485] System configuration

[1486] 1. Input means (terminal) for users to take the vision test

[1487] The terminal is a device that can connect to the internet, such as a PC or smartphone, and has a dedicated application installed. The user uses this application to take the vision test.

[1488] 2. Means for sending and receiving visual acuity data (terminal and server)

[1489] Once the vision test is complete, the device sends the vision data to a cloud server, which stores it in a database and uses it for analysis.

[1490] 3. Data analysis and calculation of optimal screen display settings (server)

[1491] The vision data stored on the cloud server is analyzed by an AI model. The AI ​​model learns from a large amount of previously collected vision and emotional data and calculates the optimal screen display settings for each user's vision and emotional state. The calculated settings include font size, color, contrast, etc.

[1492] 4. Emotion Engine (Terminal)

[1493] The device is equipped with an emotion engine that recognizes the user's emotions. The emotion engine uses sensors such as a camera and microphone to analyze the user's facial expressions and voice to obtain emotional data.

[1494] 5. Means for sending and receiving emotion data (terminal and server)

[1495] The emotion data acquired by the emotion engine is sent to a cloud server along with the visual acuity data. The server stores the received emotion data in a database and uses it for analysis along with the visual acuity data.

[1496] 6. Transmission of optimal screen display settings and screen customization means (server and terminal)

[1497] The optimal screen display settings calculated by the AI ​​model are sent from the server to the user's device, which then automatically adjusts the screen display based on the received settings, allowing the user to view a screen optimized for their visual acuity and emotional state.

[1498] 7. Generation and notification of regular reports (server and terminal)

[1499] The server periodically analyzes the user's visual acuity and emotional data and generates a report based on the analysis results. The report is sent to the user via email or in-app notification. The report includes changes in visual acuity, emotional state, and recommendations for screen settings.

[1500] Specific examples

[1501] Example 1: First-time user case

[1502] 1. The user performs the visual acuity test and emotion recognition

[1503] Users using the system for the first time install a dedicated app on their smartphone and create an account.

[1504] The user follows the app's instructions to take a vision test, and the device collects vision data.

[1505] The emotion engine analyzes the user's facial expressions and voice to obtain emotional data.

[1506] The terminal transmits the acquired visual acuity data and emotion data to a cloud server.

[1507] 2. The server analyzes the data

[1508] The server stores the received data in a database and analyzes it using an AI model.

[1509] The AI ​​model calculates the optimal screen display settings and sends that information to the device.

[1510] 3. The device optimizes the screen

[1511] The system automatically adjusts the font size, color, and contrast on the screen based on the settings information received by the user's smartphone.

[1512] The user can check the adjusted screen and make fine adjustments as necessary.

[1513] Example 2: Regular User Case

[1514] 1. Receive emotion recognition and eye test reminders

[1515] Periodic reminders for emotion recognition and vision tests are sent to the user's device.

[1516] The user checks the notification and performs a vision test and emotion recognition.

[1517] 2. The server analyzes the new data

[1518] The device sends new data to the server, which stores it in a database.

[1519] The AI ​​model recalculates the optimal screen display settings based on the latest visual acuity and emotional data and sends them to the device.

[1520] 3. Update screen settings and generate reports

[1521] The device will reflect the new settings and adjust the screen display.

[1522] The server generates a report containing the periodic analysis results and sends it to the user.

[1523] As a result, the present invention can provide an optimal visual environment that takes into account the user's visual acuity and emotional state. Furthermore, the system continuously monitors emotional changes, contributing to reducing user stress and providing a comfortable environment.

[1524] The processing flow will be explained below.

[1525] Step 1:

[1526] The user installs a dedicated application on their PC or smartphone. They then launch the app and create an account by entering basic information such as their name, age, email address, and vision correction method. The entered information is sent from the device to the server, which then stores it in a database.

[1527] Step 2:

[1528] The user follows the instructions in the app to begin the vision test. The test involves reading letters and symbols displayed on the screen, and the device obtains the user's visual acuity data (e.g., 0.8 for the right eye, 0.7 for the left eye).

[1529] Step 3:

[1530] The device's built-in emotion engine analyzes the user's facial expressions and voice to obtain current emotional data, thereby collecting emotional information such as stress and fatigue felt by the user during the test.

[1531] Step 4:

[1532] After the vision test is completed, the device sends the acquired vision data and emotion data to a cloud server, which stores the received data in a database.

[1533] Step 5:

[1534] The server inputs the visual acuity and emotional data stored in the database into an AI model for analysis. The AI ​​model calculates optimal screen display settings (text size, color, contrast) based on the user's visual acuity and emotional data.

[1535] Step 6:

[1536] The server calculates the optimal screen display settings and sends them to the user's device. The settings information sent includes details about font size, color, and contrast.

[1537] Step 7:

[1538] The display screen is automatically adjusted based on the screen setting information received by the device. The user can then check the adjusted screen and make fine adjustments as necessary.

[1539] Step 8:

[1540] The server periodically analyzes the user's visual acuity and emotional data and generates a report based on the results. The report is sent to the user via email or in-app notification. The report includes changes in visual acuity, emotional state, and recommended screen settings.

[1541] Step 9:

[1542] The user receives a reminder to periodically take a vision test. The user takes the test according to the reminder, and new vision data and emotion data are sent from the device to the server. The server stores the latest data and updates the screen display settings based on it.

[1543] Through this series of steps, the user can obtain a visual experience optimized for their visual acuity and emotional state.

[1544] Example 2

[1545] 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."

[1546] While conventional vision testing systems can provide screen display settings based on a user's visual acuity, they are unable to consider the user's emotional state, making it difficult to alleviate visual fatigue and stress. It is also difficult to respond appropriately to periodic changes in visual acuity or fluctuations in emotional state. Furthermore, they lack an intuitive user interface for users to check and fine-tune their own screen settings.

[1547] The identification process 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 an input means for the user to perform a visual acuity test and emotion recognition, a means for transmitting the input visual acuity data and emotion data to the cloud server, a means for the server to analyze the received visual acuity data and emotion data and calculate optimal screen display settings, a means for transmitting the optimal screen display settings to the user's terminal and the terminal automatically adjusting the screen display, a means for providing a user interface for the user to fine-tune the settings, and a means for the server to generate periodic reports based on the analysis results of the visual acuity data and emotion data and notify the user. This allows the user to obtain screen displays optimized for their visual acuity and emotion status, thereby alleviating visual fatigue and stress. Furthermore, the periodic reports allow the user to understand fluctuations in visual acuity and emotion and take appropriate measures.

[1548]

[1549] A "vision test" is a series of procedures for measuring a user's vision condition.

[1550] "Emotion recognition" is a technology that analyzes data such as a user's facial expressions and voice to identify their emotional state.

[1551] "Input means" refers to a device or part thereof that allows a user to perform vision testing and emotion recognition.

[1552] "Visual acuity data" is information about the user's visual acuity obtained through a visual acuity test.

[1553] "Emotion data" is information about the user's emotional state obtained through emotion recognition.

[1554] A "cloud server" refers to a server located in a remote location via the Internet that has the ability to store and analyze large amounts of data.

[1555] A "database" is a collection of information that stores received data in an organized manner and can be used for later analysis and retrieval.

[1556] The "AI model" is an algorithm that learns from large amounts of data, analyzes visual acuity data and emotional data, and calculates optimal screen display settings.

[1557] "Screen display settings" are settings that adjust the character size, color, contrast, etc. on the terminal screen based on the user's eyesight and emotional state.

[1558] A "user interface" refers to an operation screen and means for a user to directly operate and check and adjust screen settings.

[1559] A "regular report" is a report created based on the results of analyzing visual acuity data and emotional data, and includes information about changes in visual acuity and emotional state.

[1560] "Notifications" are messages or alerts that notify the user when new information or settings are created.

[1561]

[1562] This invention is a system that combines visual acuity testing and user emotion recognition to provide the optimal visual environment for the user. This system uses an emotion engine to acquire user emotion data and analyzes it together with visual acuity data to achieve more advanced optimization of screen display settings.

[1563] System configuration

[1564] 1. Input means (terminal) for users to take the vision test

[1565] The terminal is a device that can connect to the Internet, such as a PC or smartphone, and has a dedicated application installed. The user uses this application to take the vision test. The vision test is carried out by following the charts and instructions displayed during the test, and vision data is collected.

[1566] 2. Emotion engine (terminal) for emotion recognition

[1567] The device is equipped with an emotion engine that recognizes the user's emotions. The emotion engine uses sensors such as a camera and microphone to analyze the user's facial expressions and voice to obtain emotion data. For example, while the user is taking an eye test, a camera captures the user's face, and the emotion engine identifies smiling, serious expressions, etc. The microphone is also used to analyze the tone and volume of the user's voice to evaluate the user's emotional state.

[1568] 3. Data transmission and storage (devices and servers)

[1569] After completing the vision test and emotion recognition, the device sends the data to a cloud server. The data is encrypted and securely transmitted. The vision and emotion data received by the cloud server is stored in a database, making it available for later analysis.

[1570] 4. Data analysis and generation of optimization settings (server)

[1571] The cloud server provides the AI ​​model with the visual acuity and emotional data stored in the database. The AI ​​model learns from a large amount of previously collected data and calculates the optimal screen display settings for each user's visual acuity and emotional state. The calculated settings include text size, color, and contrast. For example, if a user shows signs of poor visual acuity and is in a state of high stress, the AI ​​model suggests increasing the text size and changing the color tone to a more subdued one.

[1572] 5. Sending and applying optimization settings (server and terminal)

[1573] The optimal screen display settings calculated by the server are sent to the user's device. The device automatically adjusts the screen display based on the received setting information. The user can then view the optimized screen. Furthermore, the user can make fine adjustments to suit their preferences through the user interface.

[1574] 6. Scheduled report generation and notification (server and terminal)

[1575] The server periodically re-analyzes the user's visual acuity and emotional data and generates a report based on the results. The report includes changes in visual acuity, fluctuations in emotional state, and recommendations for further screen settings. The generated report is sent to the user via email or in-app notification. This allows the user to constantly monitor fluctuations in their visual acuity and emotional state and take appropriate action.

[1576] Specific examples

[1577] Example 1: First-time user case

[1578] 1. The user performs the visual acuity test and emotion recognition

[1579] Users install a dedicated app on their smartphone and create an account.

[1580] Launch the app and follow the instructions to take the vision test. For example, you might be prompted to "Choose the smallest letter you can see."

[1581] The device's camera and microphone are used to record the user's facial expressions and voice, and obtain emotional data.

[1582] The terminal transmits the visual acuity data and the emotion data to the cloud server.

[1583] 2. The server analyzes the data

[1584] The server stores the data in a database and analyzes it using an AI model.

[1585] The AI ​​model calculates the optimal screen display settings and sends that information to the device.

[1586] 3. The device optimizes the screen

[1587] The device automatically adjusts the text size, color, and contrast on the screen based on the settings information it receives.

[1588] The user can check the screen after adjustment and make fine adjustments as necessary.

[1589] Example prompt:

[1590] The user installs a dedicated app and takes a vision test. The device acquires vision and emotion data and sends it to a cloud server. The server analyzes the data and sends optimized screen display settings to the device. The device applies the settings, and the user checks the screen.

[1591] Example 2: Regular User Case

[1592] 1. Receive emotion recognition and eye test reminders

[1593] Periodic reminders for emotion recognition and vision tests are sent to the user's device.

[1594] The user checks the notification and performs a vision test and emotion recognition.

[1595] 2. The server analyzes the new data

[1596] The device sends new data to the server, which stores it in a database.

[1597] The AI ​​model recalculates the optimal screen display settings based on the latest visual acuity and emotional data and sends them to the device.

[1598] 3. Update screen settings and generate reports

[1599] The device will reflect the new settings and adjust the screen display.

[1600] The server generates a report containing the periodic analysis results and sends it to the user.

[1601] Example prompt:

[1602] The user receives periodic reminders to perform vision tests and emotion recognition. The device sends the data to the server. The server analyzes the data and sends the optimal settings to the device. The device refreshes the screen. The server generates a report and sends it to the user.

[1603] As a result, the present invention provides an optimal visual environment that takes into account the user's visual acuity and emotional state. Continuous monitoring of emotional changes contributes to reducing stress and providing a comfortable user environment. This system is a comprehensive solution that allows users to better manage their visual acuity and emotional state and enjoy an optimal visual environment.

[1604] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1605]

[1606] Step 1: The device performs a vision test

[1607] The user launches the app and creates an account. They then follow the app's instructions to begin the vision test. Specifically, the device displays a vision test chart and asks the user to select the smallest letter they can see. Based on this input, the device generates vision data and evaluates its accuracy.

[1608] Step 2: The device performs emotion recognition

[1609] During the eye test, the device's camera and microphone record the user's facial expressions and voice. The emotion engine analyzes this data to identify the user's emotional state. The emotional data obtained through this analysis reflects the user's stress level and concentration in real time.

[1610] Step 3: The device sends the visual acuity data and emotion data to the cloud server.

[1611] After completing the vision test and emotion recognition, the device sends the generated vision and emotion data to a cloud server, where the data is encrypted and securely transmitted to the cloud server.

[1612] Step 4: The server saves the data to the database

[1613] The cloud server stores the received visual acuity and emotion data in a database, maintaining data integrity and efficiently managing it for future analysis.

[1614] Step 5: The server provides the data to the AI ​​model

[1615] The AI ​​model uses the stored visual acuity and emotional data to learn from past data and analyze the user's data. Specifically, it calculates optimal screen display settings, taking into account trends in visual acuity and changes in emotional state.

[1616] Step 6: The server calculates the optimal display settings

[1617] The AI ​​model analyzes the data and calculates the optimal screen display settings for the user, including text size, color, and contrast. For example, a user with poor eyesight might be suggested to increase text size and enhance color contrast.

[1618] Step 7: The server sends the optimization settings to the device

[1619] The server calculates the optimal screen display settings and sends them to the user's device. The settings information is encrypted and sent securely.

[1620] Step 8: Your device adjusts the display

[1621] The device automatically adjusts the size, color, and contrast of on-screen text based on the settings information it receives, and users can review these settings and fine-tune them as needed through the user interface.

[1622] Step 9: Server generates scheduled reports

[1623] The server periodically re-analyzes the user's visual acuity and emotional data and generates a report based on the results, including changes in visual acuity, fluctuations in emotional state, and recommendations for further screen settings.

[1624] Step 10: The server notifies the user of the report

[1625] The server generates reports and sends them to the user via email or in-app notifications, allowing the user to keep track of fluctuations in their visual acuity and emotional state and take appropriate action.

[1626] (Application example 2)

[1627] 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."

[1628] Conventional vision testing systems optimize screen displays by taking into account only the user's visual acuity data, making it difficult to optimize the display while also reflecting the user's emotional state. Furthermore, for users to comfortably navigate environments with a lot of visual information, such as virtual stores, it is necessary to optimize the visual environment by taking into account not only visual acuity but also emotions. However, no such technology has existed to date. Therefore, there has been a demand for the development of a system that provides an optimal visual environment that simultaneously considers the user's visual acuity and emotional state.

[1629] The specific processing by the specific 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 transmitting input visual acuity data and emotion data to a cloud server, means for the server to analyze the received data and calculate optimal screen display settings using a generative AI model, means for transmitting the optimal screen display settings to the user's terminal, and means for the terminal to automatically adjust the screen display based on the setting information received. This makes it possible to provide a comfortable visual environment that simultaneously takes into account the user's visual acuity state and emotional state.

[1630] 1. "Eye Test" means a series of tests administered to assess a User's eyesight.

[1631] 2. "Input means" refers to the device or interface through which the user conducts the vision test.

[1632] 3. "Cloud Server" means a remote server for storing, processing, and analyzing data via the Internet.

[1633] 4. "Visual acuity data" means information about a user's visual acuity, such as the numerical values ​​or results obtained from a visual acuity test.

[1634] 5. "Emotion data" refers to information indicating the user's emotional state obtained from facial expressions, voice, etc.

[1635] 6. A "generative AI model" is an artificial intelligence algorithm that is trained based on large amounts of data and used to perform data analysis and predictions.

[1636] 7. "Screen display settings" are settings for adjusting visual elements of the screen, such as font size, color, and contrast.

[1637] 8. "Terminal" means a computer device used by a User, such as a smartphone or PC.

[1638] 9. "Automatic adjustment means" means a function that allows the system to automatically optimize the screen display without user intervention.

[1639] 10. "User interface" refers to the screens and operating means through which a user interacts with and operates a system.

[1640] The present invention is a system that provides an optimal visual environment by simultaneously considering the user's visual acuity and emotional state. This system automatically optimizes the screen display based on the results of a visual acuity test and emotional recognition.

[1641] System configuration

[1642] 1. Input means (terminal) for users to take the vision test

[1643] The terminal uses a device such as a smartphone or a head-mounted display (HMD) that allows the user to take the vision test. The user takes the vision test through the terminal, and the results of the vision test are acquired by the terminal as numerical data.

[1644] 2. Means for sending and receiving visual acuity data and emotion data (terminal and cloud server)

[1645] After the eye test is completed, the device sends the eye data to the cloud server. At the same time, the device is equipped with an emotion engine that analyzes the user's facial expressions and voice to obtain emotion data. This data is then sent to the cloud server in parallel.

[1646] 3. Data analysis and calculation of optimal screen display settings (cloud server)

[1647] The visual acuity and emotion data stored on the cloud server is analyzed using a generative AI model. The generative AI model is trained based on a large amount of previously collected visual acuity and emotion data, and calculates the optimal screen display settings for each user's visual acuity and emotional state. These settings include text size, color, contrast, etc.

[1648] 4. Transmission of optimal screen display settings and screen customization methods (cloud server and device)

[1649] The optimal screen display settings analyzed and calculated by the AI ​​model are sent from the cloud server to the user's device. The device then automatically adjusts the screen display based on the received setting information, ensuring that the user always experiences the optimal visual environment.

[1650] 5. Providing a User Interface

[1651] The user is also provided with a user interface on the device to check and further adjust the screen settings based on the results of the vision test and emotion recognition. The user can make fine adjustments through this interface.

[1652] Example

[1653] Example 1: First-time user

[1654] 1. The user puts on the head-mounted display and starts the application.

[1655] 2. The application will automatically start the vision test and emotion recognition and send the data to the cloud server.

[1656] 3. The cloud server analyzes the received visual acuity data and emotion data and calculates the optimal screen display settings.

[1657] 4. Optimal setting information is sent to the device, and the user's visual environment is automatically adjusted.

[1658] 5. Users can enjoy a comfortable shopping experience in a virtual store.

[1659] Example 2: For a regular user

[1660] 1. Periodic reminder notifications for vision tests and emotion recognition will be sent to your device.

[1661] 2. The user checks the notification and performs a visual acuity test and emotion recognition.

[1662] 3. The device sends the visual acuity data and emotion data to the cloud server.

[1663] 4. The cloud server analyzes the latest data and recalculates the optimal screen display settings.

[1664] 5. The new configuration information is sent to the device, and the user's visual environment is updated.

[1665] Prompt Sentence Examples

[1666] "An application that optimizes the visual environment for users wearing head-mounted displays. It collects the user's emotional and visual acuity data in real time, analyzes it on a cloud server, and provides optimal screen settings."

[1667] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1668] Step 1:

[1669] The user uses an input means for taking a visual acuity test to obtain visual acuity data.

[1670] Specific operation: The user launches an application on their smartphone or head-mounted display and takes a vision test. The vision test collects responses to the displayed test patterns and letters, and stores them as vision data on the device. In this step, the user's vision response is input, and vision data is obtained as output.

[1671] Step 2:

[1672] The terminal uses an emotion engine to acquire emotion data of the user.

[1673] Specific operation: The device's camera and microphone are used to analyze the user's facial expressions and voice in real time. The emotion engine processes this data and outputs the user's emotional state as numerical or categorical data. In this step, the user's facial expressions and voice are taken as input, and emotion data is generated as output.

[1674] Step 3:

[1675] The visual acuity data and emotion data are sent to a cloud server.

[1676] Specific operation: The terminal transmits the acquired visual acuity data and emotion data as a data packet to the cloud server. In this step, the visual acuity data and emotion data are input, and the transmitted data packet is output.

[1677] Step 4:

[1678] The cloud server analyzes the received data using a generative AI model and calculates the optimal screen display settings.

[1679] Specific operation: The cloud server stores the received visual acuity data and emotion data in a database and runs a generative AI model based on these data. The AI ​​model compares these data with past data and calculates the optimal screen display settings (e.g., font size, color, contrast, etc.) for the user's condition. In this step, the data sent to the cloud server is the input, and the optimal screen display settings are obtained as the output.

[1680] Step 5:

[1681] Send optimal screen display settings to the user's device.

[1682] Specific operation: The cloud server sends the calculated screen display settings to the terminal as a data packet. In this step, the optimal screen display settings are input, and the transmitted setting information is output.

[1683] Step 6:

[1684] The device automatically adjusts the screen display based on the received setting information.

[1685] Specific operation: The device applies the received screen display setting information and automatically adjusts the font size, color, contrast, etc. This setting allows the user to experience an optimized visual environment. In this step, the received setting information is the input, and the adjusted screen display is the output.

[1686] Step 7:

[1687] A user interface allows the user to view and fine-tune the screen settings.

[1688] Specific operation: The device provides a user interface, and the user checks the initial screen display based on the results of the vision test and emotion recognition, and fine-tunes it as necessary. In this step, the input is screen setting information, and the output is a screen display optimized for the user.

[1689] 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.

[1690] 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.

[1691] 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.

[1692] 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.

[1693] 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.

[1694] 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.

[1695] 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).

[1696] 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.

[1697] 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."

[1698] 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.

[1699] 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).

[1700] 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.

[1701] 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.

[1702] 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.

[1703] 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.

[1704] 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.

[1705] 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.

[1706] 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.

[1707] 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.

[1708] 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.

[1709] 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.

[1710] The following is further disclosed regarding the above embodiment.

[1711] (Claim 1)

[1712] an input means for a user to perform a visual acuity test;

[1713] A means for transmitting the input visual acuity data to a cloud server;

[1714] A means for analyzing the received data and calculating optimal screen display settings by the server;

[1715] A system including means for transmitting optimal screen display settings to a user's terminal.

[1716] (Claim 2)

[1717] The system of claim 1, wherein the server stores the latest visual acuity data in a database and includes means for comparing it with past data.

[1718] (Claim 3)

[1719] 10. The system of claim 1, further comprising means for providing a user interface for a user to review and adjust screen settings based on the results of the vision test.

[1720] (Claim 4)

[1721] 2. The system of claim 1, wherein the server includes means for periodically generating analysis results and reports and notifying the user.

[1722] (Claim 5)

[1723] The system of claim 1, further comprising means for analyzing visual acuity data using AI.

[1724] "Example 1"

[1725] (Claim 1)

[1726] an input means for a user to perform a visual acuity test;

[1727] A means for transmitting the input visual acuity data to a cloud server;

[1728] A means for storing the received visual acuity data in a database by the server;

[1729] A means of analyzing the stored visual acuity data using an AI model and calculating the optimal screen display settings;

[1730] means for transmitting optimal screen display settings to a user's device;

[1731] means for automatically adjusting a screen display based on the setting information received by the terminal;

[1732] A system including a means for a server to periodically analyze visual acuity data and generate reports to inform the user.

[1733] (Claim 2)

[1734] The system of claim 1, wherein the server stores the latest visual acuity data in a database and includes means for comparing it with past data.

[1735] (Claim 3)

[1736] 10. The system of claim 1, further comprising means for providing a user interface for a user to review and adjust screen settings based on the results of the vision test.

[1737] "Application Example 1"

[1738] (Claim 1)

[1739] an input means for a user to perform a visual acuity test;

[1740] A means for transmitting the input visual acuity data to a cloud server;

[1741] A means for analyzing the received data and calculating optimal screen display settings by the server;

[1742] means for transmitting optimal screen display settings to a user's device;

[1743] A means of optimizing the display of product pages based on the analysis results,

[1744] A system including a means for displaying product information according to visual acuity.

[1745] (Claim 2)

[1746] The system of claim 1, wherein the server stores the latest visual acuity data in a database and includes means for comparing it with past data.

[1747] (Claim 3)

[1748] 10. The system of claim 1, further comprising means for providing a user interface for a user to review and adjust screen settings based on the results of the vision test.

[1749] "Example 2: Combining Emotion Engines"

[1750] (Claim 1)

[1751] input means for a user to perform vision testing and emotion recognition;

[1752] means for transmitting the input visual acuity data and emotion data to a cloud server;

[1753] A means for analyzing the received visual acuity data and emotion data by the server and calculating optimal screen display settings;

[1754] means for transmitting optimal screen display settings to a user's terminal, and for the terminal to automatically adjust the screen display;

[1755] means for providing a user interface for a user to fine-tune the settings;

[1756] a means for the server to generate a periodic report based on the analysis results of the visual acuity data and emotion data and notify the user;

[1757] A system including:

[1758] (Claim 2)

[1759] 2. The system according to claim 1, wherein the server includes means for storing the latest visual acuity data and emotion data in a database and for comparing the data with past data.

[1760] (Claim 3)

[1761] 10. The system of claim 1, further comprising means for providing a user interface for a user to review and adjust screen settings based on the results of the vision test and the emotion recognition results.

[1762] "Application example 2 when combining emotion engines"

[1763] (Claim 1)

[1764] an input means for a user to perform a visual acuity test;

[1765] means for transmitting the input visual acuity data and emotion data to a cloud server;

[1766] A means for the server to analyze the received data and calculate optimal screen display settings using a generative AI model;

[1767] means for transmitting optimal screen display settings to a user's device;

[1768] means for automatically adjusting a screen display based on the setting information received by the terminal;

[1769] A system including:

[1770] (Claim 2)

[1771] 2. The system according to claim 1, wherein the server includes means for storing the latest visual acuity data and emotion data in a database and for comparing the data with past data.

[1772] (Claim 3)

[1773] 10. The system of claim 1, further comprising means for providing a user interface for a user to review and adjust screen settings based on the results of the vision test and emotion recognition. [Explanation of symbols]

[1774] 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. an input means for a user to perform a visual acuity test; A means for transmitting the input visual acuity data to a cloud server; A means for analyzing the received data and calculating optimal screen display settings by the server; A system including means for transmitting optimal screen display settings to a user's terminal.

2. The system according to claim 1, wherein the server stores the latest visual acuity data in a database and includes means for comparing the data with past data.

3. 10. The system of claim 1, further comprising means for providing a user interface for a user to review and adjust screen settings based on the results of the vision test.

4. 2. The system according to claim 1, wherein the server includes means for periodically generating analysis results and reports and notifying the user of the results.

5. The system of claim 1, further comprising means for analyzing visual acuity data using AI.

Images