system

Abstract

The system according to this embodiment aims to analyze health data related to vision and provide early detection and long-term countermeasures for vision loss and visual impairment. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a communication unit, a prediction unit, and a provision unit. The collection unit collects visual acuity test data, eye photographs, and lifestyle data. The analysis unit analyzes the data collected by the collection unit. The communication unit communicates individual visual states based on the results analyzed by the analysis unit. The prediction unit predicts changes over time based on the visual states communicated by the communication unit. The provision unit provides long-term countermeasures based on the results predicted by the prediction unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, early detection of vision loss and visual impairment has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze health data related to vision and provide early detection and long-term countermeasures for vision loss and visual impairment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a communication unit, a prediction unit, and a provision unit. The collection unit collects visual acuity test data, eye photographs, and lifestyle data. The analysis unit analyzes the data collected by the collection unit. The communication unit communicates individual visual states based on the results analyzed by the analysis unit. The prediction unit predicts changes over time based on the visual states communicated by the communication unit. The provision unit provides long-term countermeasures based on the results predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze health data related to vision and provide early detection and long-term countermeasures for vision loss and visual impairment. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​Vision Monitoring System according to an embodiment of the present invention is a platform that analyzes health data related to vision to aim for the early detection of vision loss and visual impairment. The AI ​​Vision Monitoring System uses an AI agent to analyze vision test data, eye photographs, and lifestyle data, communicates the individual's visual condition, and provides long-term countermeasures along with predictions of age-related changes. In addition, to prevent vision loss caused by the widespread use of digital devices, it detects signs of eye strain and dryness in real time and provides immediate and appropriate advice. For example, the user inputs vision test data, eye photographs, and lifestyle data. This data is analyzed by the AI ​​agent. The AI ​​agent grasps the state of vision from the vision test data, checks the health of the eyes from the eye photographs, and identifies factors that affect vision from the lifestyle data. For example, it analyzes the impact of prolonged use of digital devices and irregular lifestyle habits on vision. Next, the AI ​​agent communicates the individual's visual condition to the user based on the analysis results. For example, if vision is declining or there are problems with the eye health, the user is notified. It also predicts age-related changes and presents the risk of future vision loss. This allows the user to understand their own visual condition and take early countermeasures. Furthermore, the AI ​​agent detects signs of eye strain and dryness in real time to prevent vision deterioration caused by the widespread use of digital devices. For example, it can detect eye strain and dryness from prolonged use of digital devices and advise the user to take a break. It also provides specific measures to maintain eye health, such as eye stretches and setting up an appropriate lighting environment. As a result, the AI ​​Vision Protection System can detect the risk of vision deterioration and visual impairment early and take appropriate measures. This helps maintain vision health and prevent the occurrence of visual impairment. In addition, users can receive specific advice to prevent vision deterioration caused by the widespread use of digital devices, thus protecting their eye health. As a result, the AI ​​Vision Protection System can provide early detection and countermeasures for vision deterioration and visual impairment.

[0029] The AI ​​vision monitoring system according to this embodiment comprises a collection unit, an analysis unit, a communication unit, a prediction unit, and a provision unit. The collection unit collects vision test data, eye photographs, and lifestyle data. For example, the collection unit uses a vision measuring device to acquire vision test data. The vision measuring device can measure the user's vision and collect the results as digital data. The collection unit also uses a camera to take eye photographs. The camera can take photographs of the user's eyes and collect the image data. Furthermore, the collection unit provides an interface for inputting information about the user's lifestyle in order to collect lifestyle data. For example, the user can input information such as eating habits, exercise habits, and sleep patterns. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes vision test data to understand the user's vision status. Based on the vision test data, the analysis unit can evaluate the user's visual acuity values ​​and fluctuations. The analysis unit also analyzes eye photographs to check the user's eye health status. Based on the eye photographs, the analysis unit can identify the presence or absence of eye diseases and structural abnormalities of the eyes. Furthermore, the analysis unit analyzes lifestyle data to identify factors that affect vision. Based on lifestyle data, the analysis unit can analyze the impact of prolonged use of digital devices and irregular lifestyle habits on vision. The communication unit communicates individual visual status to the user based on the results analyzed by the analysis unit. For example, the communication unit notifies the user if their vision is deteriorating or if there are problems with their eye health. The communication unit can use text messages or email as a means of notification. The communication unit can also adjust the frequency of communication. For example, by regularly communicating their visual status, it makes it easier for the user to understand their own visual condition. The prediction unit makes predictions about changes over time based on the visual status communicated by the communication unit. For example, the prediction unit presents the risk of future vision deterioration based on visual status fluctuation data. The prediction unit can predict changes in visual status over time using a prediction algorithm. The provision unit provides long-term countermeasures based on the results predicted by the prediction unit.The service unit, for example, detects signs of eye strain or dryness in real time and advises the user to take a break. The service unit can detect signs of eye strain or dryness using sensors. The service unit also provides specific measures to maintain eye health. For example, the service unit provides advice such as eye stretches and setting an appropriate lighting environment. As a result, the AI ​​vision protection system according to this embodiment can provide early detection and countermeasures for vision loss and visual impairment.

[0030] The data collection unit collects visual acuity test data, eye photographs, and lifestyle data. For example, to acquire visual acuity test data, the unit uses a visual acuity measuring device. This device measures the user's visual acuity and collects the results as digital data. Specifically, the device measures the size of the letters and symbols the user reads on a visual acuity chart and quantifies the results. This allows for an accurate understanding of the user's visual acuity. The data collection unit also uses a camera to take eye photographs. The camera captures high-resolution images, recording detailed information about the user's eyes. For example, the camera can capture detailed images of the eye's surface and blood vessels, aiding in the early detection of eye diseases. Furthermore, the data collection unit provides an interface for inputting information about the user's lifestyle to collect lifestyle data. For example, the user can input information such as their eating habits, exercise habits, and sleep patterns. This allows for the comprehensive collection of factors that may affect visual acuity. The data collection unit centrally manages this data, making it accessible to the analysis and prediction units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes vision test data to understand the user's visual acuity. Based on the vision test data, the analysis unit can evaluate the user's visual acuity values ​​and fluctuations. Specifically, it analyzes vision test data in a time series to identify patterns of visual acuity fluctuations. The analysis unit also analyzes eye photographs to check the user's eye health. Based on eye photographs, the analysis unit can identify the presence or absence of eye diseases and abnormalities in the eye structure. For example, it can use image recognition technology to detect abnormalities on the surface of the eye and changes in blood vessels, enabling early detection of abnormalities. Furthermore, the analysis unit analyzes lifestyle data to identify factors that affect visual acuity. Based on lifestyle data, the analysis unit can analyze the impact of prolonged use of digital devices and irregular lifestyle habits on visual acuity. For example, by correlating the duration of digital device use with fluctuations in visual acuity, it can identify the causes of vision deterioration. The analysis unit comprehensively analyzes this data to comprehensively evaluate the user's visual acuity and health status. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analyses. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The communication unit communicates individual visual status to users based on the results analyzed by the analysis unit. For example, if a user's vision deteriorates or they have problems with their eye health, the communication unit will notify them. The communication unit can use text messages or email as notification methods. Specifically, it will send notifications to the user's smartphone or computer, providing information on changes in vision and health status. The communication unit can also adjust the frequency of communication. For example, by regularly communicating the user's visual status, it can help users understand their own visual condition. Furthermore, the communication unit can collect user feedback and continuously improve the accuracy and effectiveness of its communications. For example, based on user feedback, it can review the content and timing of notifications and provide more effective communication methods. This allows the communication unit to provide users with information quickly and accurately, supporting early intervention for vision deterioration and changes in eye health.

[0033] The prediction unit predicts age-related changes based on the visual state communicated by the communication unit. For example, the prediction unit presents the risk of future vision deterioration based on visual acuity fluctuation data. The prediction unit can predict age-related changes in visual acuity using a prediction algorithm. Specifically, it analyzes visual acuity fluctuation patterns based on past visual acuity data and lifestyle data to evaluate the risk of future vision deterioration. For example, it can predict the impact of long-term use of digital devices and irregular lifestyle habits on visual acuity and present the risk to the user. Furthermore, the prediction unit can use AI to simulate multiple scenarios and identify the most likely risk. This allows the prediction unit to predict the risk of future vision deterioration with high accuracy and provide the user with information to take appropriate measures. In addition, the prediction unit can continuously revise prediction results based on data updated in real time to respond to the latest situation. This allows the prediction unit to always provide highly accurate risk predictions based on the latest information and support quick and appropriate responses.

[0034] The service provider provides long-term solutions based on the results predicted by the prediction unit. For example, the service provider can detect signs of eye strain and dryness in real time and advise the user to take a break. The service provider can detect signs of eye strain and dryness using sensors. Specifically, it monitors the temperature and humidity around the eyes, blinking frequency, etc., to detect signs of eye strain and dryness. The service provider also provides specific measures to maintain eye health. For example, it provides advice on eye stretches and setting up an appropriate lighting environment. Furthermore, the service provider can propose individualized measures tailored to the user's lifestyle. For example, it can provide advice on when to take breaks to reduce the impact of prolonged digital device use on vision, and advice on eye-friendly foods. In this way, the service provider can provide users with specific and practical measures to help prevent vision loss and visual impairment. Furthermore, the service provider can continuously improve the accuracy and effectiveness of the measures based on user feedback. In this way, the service provider can provide users with optimal measures, enabling early detection and prevention of vision loss and visual impairment.

[0035] The data collection unit collects vision test data, eye photographs, and lifestyle data. For example, the data collection unit uses a vision measuring device to acquire vision test data. The vision measuring device can measure the user's vision and collect the results as digital data. The data collection unit also uses a camera to take photographs of the user's eyes. The camera can take photographs of the user's eyes and collect the image data. Furthermore, the data collection unit provides an interface for inputting information about the user's lifestyle in order to collect lifestyle data. For example, the user can input information such as eating habits, exercise habits, and sleep patterns. By collecting vision test data, eye photographs, and lifestyle data, health data related to vision can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from the vision measuring device into a generating AI to acquire vision test data, and have the generating AI perform the collection of vision test data.

[0036] The analysis unit grasps the state of visual acuity from visual acuity test data, checks the health of the eyes from eye photographs, and identifies factors that affect visual acuity from lifestyle data. For example, the analysis unit analyzes visual acuity test data to understand the state of the user's visual acuity. Based on the visual acuity test data, the analysis unit can evaluate the numerical value and fluctuations of the user's visual acuity. The analysis unit also analyzes eye photographs to check the health of the user's eyes. Based on the eye photographs, the analysis unit can identify the presence or absence of eye diseases and structural abnormalities of the eyes. Furthermore, the analysis unit analyzes lifestyle data to identify factors that affect visual acuity. Based on lifestyle data, the analysis unit can analyze the impact of prolonged use of digital devices and irregular lifestyle habits on visual acuity. In this way, by analyzing visual acuity test data, eye photographs, and lifestyle data, the state of visual acuity, the health of the eyes, and factors that affect visual acuity can be identified. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input visual acuity test data into a generating AI and have the generating AI perform the task of understanding the state of visual acuity.

[0037] The communication unit contacts the user with their individual visual condition based on the analysis results. For example, if the user's vision is deteriorating or there is a problem with their eye health, the communication unit will notify the user accordingly. The communication unit can use text messages or email as a means of notification. The communication unit can also adjust the frequency of contact. For example, by regularly contacting the user with their visual condition, it makes it easier for the user to understand their own visual condition. This allows the user to understand their own visual condition by contacting them with their individual visual condition based on the analysis results. Some or all of the above processing in the communication unit may be performed using AI, for example, or not using AI. For example, the communication unit can input the analysis results into a generating AI and have the generating AI execute the notification of the individual visual condition.

[0038] The prediction unit predicts age-related changes and presents the risk of future vision deterioration. For example, the prediction unit presents the risk of future vision deterioration based on vision fluctuation data. The prediction unit can predict age-related changes in vision using a prediction algorithm. This allows users to take early action by predicting age-related changes and presenting the risk of future vision deterioration. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input vision fluctuation data into a generating AI and have the generating AI perform age-related change predictions.

[0039] The service unit detects signs of eye strain and dryness in real time and advises the user to take a break. The service unit can, for example, use sensors to detect signs of eye strain and dryness. The service unit can detect eye redness and dryness and advise the user to take a break. In addition, the service unit provides appropriate advice to the user based on the results detected in real time. For example, the service unit can advise on stretches to reduce eye strain or setting an appropriate lighting environment. In this way, by detecting signs of eye strain and dryness in real time and advising the user to take a break, vision deterioration can be prevented. Some or all of the above processing in the service unit may be performed using AI, for example, or without AI. For example, the service unit can input data from sensors into a generating AI and have the generating AI perform the detection of signs of eye strain and dryness.

[0040] The service provider offers specific measures to maintain eye health. For example, the service provider provides advice on eye stretches and setting up an appropriate lighting environment. The service provider can provide users with specific measures to maintain eye health. For example, the service provider can explain how to stretch eyes and have the user practice it. The service provider can also advise on how to set up an appropriate lighting environment and have the user practice it. By providing specific measures to maintain eye health, it is possible to prevent vision loss and visual impairment. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input measures to maintain eye health into a generating AI and have the generating AI execute the provision of specific measures.

[0041] The data collection unit analyzes the user's past vision test history and selects the optimal data collection method. For example, the data collection unit proposes the optimal data collection method based on the user's past vision test data. The data collection unit can select the most effective data collection timing from the user's past vision test history. The data collection unit can also analyze the user's past vision test history and select the optimal data collection method. This allows the optimal data collection method to be selected by analyzing the user's past vision test history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past vision test history into a generating AI and have the generating AI select the optimal data collection method.

[0042] The data collection unit filters the vision test data based on the user's current lifestyle and areas of interest. For example, the data collection unit filters the vision test data considering the user's current lifestyle. The data collection unit can filter the vision test data based on the user's areas of interest. The data collection unit can also filter the vision test data based on the user's current lifestyle and areas of interest. This allows for the collection of highly relevant data by filtering the vision test data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering of the vision test data.

[0043] The data collection unit prioritizes collecting highly relevant data when collecting vision test data, taking into account the user's geographical location. For example, the data collection unit prioritizes collecting highly relevant vision test data based on the user's current location. The data collection unit can collect optimal vision test data by taking into account the user's geographical location. Furthermore, the data collection unit can prioritize collecting highly relevant vision test data based on the user's geographical location. This allows for the acquisition of more accurate data by collecting highly relevant data while considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0044] The data collection unit analyzes the user's social media activity and collects relevant data when collecting vision test data. For example, the data collection unit analyzes the user's social media activity and collects data related to vision. The data collection unit can collect vision test data based on the user's social media activity. The data collection unit can also analyze the user's social media activity and collect relevant vision test data. This allows for the collection of vision-related data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0045] The analysis unit adjusts the level of detail of the analysis based on the importance of the visual acuity test data during the analysis. For example, the analysis unit performs a detailed analysis on important visual acuity test data. The analysis unit can perform a simplified analysis on less important visual acuity test data. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the visual acuity test data. This allows for a detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the visual acuity test data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the visual acuity test data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0046] The analysis unit applies different analysis algorithms depending on the category of the visual acuity test data during analysis. For example, the analysis unit applies the optimal analysis algorithm depending on the category of the visual acuity test data. The analysis unit can apply different analysis algorithms based on the category of the visual acuity test data. Furthermore, the analysis unit can select an appropriate analysis algorithm depending on the category of the visual acuity test data. This allows for the provision of optimal analysis results by applying different analysis algorithms depending on the category of the visual acuity test data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the visual acuity test data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0047] The analysis unit determines the priority of analysis based on the submission date of the vision test data during the analysis. For example, the analysis unit can prioritize the analysis based on the submission date of the vision test data. The analysis unit can prioritize the analysis of vision test data that was submitted earlier. The analysis unit can also postpone the analysis of vision test data that was submitted later. In this way, by determining the priority of analysis based on the submission date of the vision test data, data that was submitted earlier can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the submission date of the vision test data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0048] The analysis unit adjusts the order of analysis based on the relevance of the vision test data during the analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the vision test data. The analysis unit can prioritize the analysis of vision test data with high relevance. The analysis unit can also postpone the analysis of vision test data with low relevance. In this way, by adjusting the order of analysis based on the relevance of the vision test data, highly relevant data can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the relevance of the vision test data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0049] The communication unit adjusts the level of detail in its communication based on the importance of the vision test data. For example, the communication unit provides detailed communication for important vision test data. For less important vision test data, it can provide simplified communication. The communication unit can also adjust the level of detail in its communication based on the importance of the vision test data. This allows for detailed communication for important data by adjusting the level of detail in communication based on the importance of the vision test data. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the importance of the vision test data into a generating AI and have the generating AI perform the adjustment of the level of detail in communication.

[0050] The communication unit applies different communication algorithms depending on the category of the vision test data when communication is made. For example, the communication unit applies the optimal communication algorithm depending on the category of the vision test data. The communication unit can apply different communication algorithms based on the category of the vision test data. The communication unit can also select an appropriate communication algorithm depending on the category of the vision test data. This allows for the provision of the optimal communication method by applying different communication algorithms depending on the category of the vision test data. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the category of the vision test data into a generating AI and have the generating AI execute the application of different communication algorithms.

[0051] The liaison department determines the priority of contact based on the submission date of the vision test data. For example, the liaison department can prioritize contacting individuals with vision test data submitted earlier. Conversely, the liaison department can postpone contacting individuals with vision test data submitted later. This allows the liaison department to prioritize contacting individuals with data submitted earlier by determining the priority of contact based on the submission date of the vision test data. Some or all of the above processing in the liaison department may be performed using AI, for example, or without AI. For example, the liaison department can input the submission dates of the vision test data into a generating AI and have the generating AI determine the priority of contact.

[0052] The communication unit adjusts the order of communication based on the relevance of the vision test data. For example, the communication unit adjusts the order of communication based on the relevance of the vision test data. The communication unit can prioritize communicating highly relevant vision test data. The communication unit can also postpone communicating less relevant vision test data. In this way, by adjusting the order of communication based on the relevance of the vision test data, highly relevant data can be communicated preferentially. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the relevance of the vision test data into a generating AI and have the generating AI perform the adjustment of the order of communication.

[0053] The prediction unit adjusts the level of detail of the prediction based on the importance of the vision test data during the prediction process. For example, the prediction unit can make detailed predictions for important vision test data. For less important vision test data, the prediction unit can make simplified predictions. The prediction unit can also adjust the level of detail of the prediction based on the importance of the vision test data. This allows for detailed predictions for important data by adjusting the level of detail of the prediction based on the importance of the vision test data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the importance of the vision test data into a generating AI and have the generating AI perform the adjustment of the level of detail of the prediction.

[0054] The prediction unit applies different prediction algorithms depending on the category of the vision test data during prediction. For example, the prediction unit applies the optimal prediction algorithm depending on the category of the vision test data. The prediction unit can apply different prediction algorithms based on the category of the vision test data. The prediction unit can also select an appropriate prediction algorithm depending on the category of the vision test data. This allows for the provision of optimal prediction results by applying different prediction algorithms depending on the category of the vision test data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the category of the vision test data into a generating AI and cause the generating AI to apply different prediction algorithms.

[0055] The prediction unit determines the prediction priority based on the submission timing of the vision test data. For example, the prediction unit can prioritize predicting vision test data that has been submitted earlier. It can also postpone predicting vision test data that has been submitted later. In this way, by determining the prediction priority based on the submission timing of the vision test data, it is possible to prioritize predicting data that has been submitted earlier. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the submission timing of the vision test data into a generating AI and have the generating AI perform the determination of the prediction priority.

[0056] The prediction unit adjusts the order of predictions based on the relevance of the vision test data during prediction. For example, the prediction unit adjusts the order of predictions based on the relevance of the vision test data. The prediction unit can prioritize predicting vision test data that is highly relevant. The prediction unit can also postpone predicting vision test data that is less relevant. In this way, by adjusting the order of predictions based on the relevance of the vision test data, highly relevant data can be predicted preferentially. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the relevance of the vision test data into a generating AI and have the generating AI perform the adjustment of the prediction order.

[0057] The service provider adjusts the level of detail of the countermeasures provided based on the importance of the vision test data at the time of provision. For example, the service provider can provide detailed countermeasures for important vision test data. For less important vision test data, the service provider can provide simplified countermeasures. The service provider can also adjust the level of detail of the countermeasures provided based on the importance of the vision test data. This allows for the provision of detailed countermeasures for important data by adjusting the level of detail of the countermeasures provided based on the importance of the vision test data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of the vision test data into a generating AI and have the generating AI perform the adjustment of the level of detail of the countermeasures provided.

[0058] The service provider applies different countermeasure algorithms depending on the category of the vision test data at the time of provision. For example, the service provider applies the optimal countermeasure algorithm depending on the category of the vision test data. The service provider can apply different countermeasure algorithms based on the category of the vision test data. Furthermore, the service provider can select an appropriate countermeasure algorithm depending on the category of the vision test data. This allows for the provision of optimal countermeasures by applying different countermeasure algorithms depending on the category of the vision test data. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the category of the vision test data into a generating AI and have the generating AI execute the application of different countermeasure algorithms.

[0059] The service provider determines the priority of the countermeasures to be provided based on the submission date of the vision test data. For example, the service provider can prioritize countermeasures for vision test data submitted earlier. The service provider can also postpone countermeasures for vision test data submitted later. This allows for prioritizing countermeasures for data submitted earlier by determining the priority of countermeasures based on the submission date of the vision test data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the submission date of the vision test data into a generating AI and have the generating AI determine the priority of the countermeasures to be provided.

[0060] The service provider adjusts the order of the countermeasures provided based on the relevance of the vision test data at the time of provision. For example, the service provider adjusts the order of the countermeasures provided based on the relevance of the vision test data. The service provider can prioritize providing countermeasures for highly relevant vision test data. The service provider can also postpone providing countermeasures for less relevant vision test data. In this way, by adjusting the order of countermeasures provided based on the relevance of the vision test data, highly relevant data can be prioritized. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the relevance of the vision test data into a generating AI and have the generating AI perform the adjustment of the order of the countermeasures provided.

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

[0062] The data collection unit can suggest the optimal collection method by referring to the user's past vision test data when collecting user vision test data. For example, it can select the optimal collection timing and method based on the user's past vision test data. Furthermore, when collecting user lifestyle data, the data collection unit can filter the data based on the user's current lifestyle and areas of interest. This allows for the priority collection of highly relevant data. In addition, the data collection unit can prioritize the collection of highly relevant vision test data by considering the user's geographical location. This allows for the acquisition of more accurate data based on the user's current location.

[0063] The analysis unit can adjust the level of detail in the analysis of visual acuity test data based on its importance. For example, it can perform a detailed analysis on important visual acuity test data and a simplified analysis on less important data. Furthermore, the analysis unit can apply different analysis algorithms depending on the category of the visual acuity test data, thereby providing optimal analysis results. In addition, the analysis unit can determine the priority of analysis based on when the visual acuity test data was submitted. Prioritizing the analysis of data submitted earlier enables a faster response.

[0064] The prediction unit can adjust the level of detail in its predictions based on the importance of the vision test data. For example, it can perform detailed predictions for important vision test data and simplified predictions for less important data. Furthermore, the prediction unit can apply different prediction algorithms depending on the category of the vision test data, thereby providing optimal prediction results. In addition, the prediction unit can prioritize predictions based on when the vision test data was submitted. Prioritizing earlier submissions enables a quicker response.

[0065] The analysis unit can adjust the level of detail in the analysis of visual acuity test data based on its importance. For example, it can perform a detailed analysis on important visual acuity test data and a simplified analysis on less important data. Furthermore, the analysis unit can apply different analysis algorithms depending on the category of the visual acuity test data, thereby providing optimal analysis results. In addition, the analysis unit can determine the priority of analysis based on when the visual acuity test data was submitted. Prioritizing the analysis of data submitted earlier enables a faster response.

[0066] The prediction unit can adjust the level of detail in its predictions based on the importance of the vision test data. For example, it can perform detailed predictions for important vision test data and simplified predictions for less important data. Furthermore, the prediction unit can apply different prediction algorithms depending on the category of the vision test data, thereby providing optimal prediction results. In addition, the prediction unit can prioritize predictions based on when the vision test data was submitted. Prioritizing earlier submissions enables a quicker response.

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

[0068] Step 1: The data collection unit collects vision test data, eye photographs, and lifestyle data. The data collection unit uses a vision measuring device to acquire vision test data, takes eye photographs using a camera, and provides an interface for inputting information about the user's lifestyle. For example, the user can input information such as eating habits, exercise habits, and sleep patterns. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the visual acuity test data to understand the user's visual acuity, analyzes eye photographs to check the health of the eyes, and analyzes lifestyle data to identify factors that affect visual acuity. Step 3: The communication unit communicates the individual visual condition to the user based on the results analyzed by the analysis unit. The communication unit notifies the user if their vision is deteriorating or if there are problems with their eye health. Text messages or email can be used as notification methods, and the frequency of communication can be adjusted. Step 4: The prediction unit makes predictions about changes over time based on the visual state communicated by the communication unit. Based on the visual acuity fluctuation data, the prediction unit presents the risk of future visual acuity decline and uses a prediction algorithm to predict changes in visual acuity over time. Step 5: The service provider provides long-term solutions based on the results predicted by the forecasting unit. The service provider detects signs of eye strain and dryness in real time, advises the user to take breaks, and provides specific measures to maintain eye health. For example, it may provide advice on eye stretches and setting up an appropriate lighting environment.

[0069] (Example of form 2) The AI ​​Vision Monitoring System according to an embodiment of the present invention is a platform that analyzes health data related to vision to aim for the early detection of vision loss and visual impairment. The AI ​​Vision Monitoring System uses an AI agent to analyze vision test data, eye photographs, and lifestyle data, communicates the individual's visual condition, and provides long-term countermeasures along with predictions of age-related changes. In addition, to prevent vision loss caused by the widespread use of digital devices, it detects signs of eye strain and dryness in real time and provides immediate and appropriate advice. For example, the user inputs vision test data, eye photographs, and lifestyle data. This data is analyzed by the AI ​​agent. The AI ​​agent grasps the state of vision from the vision test data, checks the health of the eyes from the eye photographs, and identifies factors that affect vision from the lifestyle data. For example, it analyzes the impact of prolonged use of digital devices and irregular lifestyle habits on vision. Next, the AI ​​agent communicates the individual's visual condition to the user based on the analysis results. For example, if vision is declining or there are problems with the eye health, the user is notified. It also predicts age-related changes and presents the risk of future vision loss. This allows the user to understand their own visual condition and take early countermeasures. Furthermore, the AI ​​agent detects signs of eye strain and dryness in real time to prevent vision deterioration caused by the widespread use of digital devices. For example, it can detect eye strain and dryness from prolonged use of digital devices and advise the user to take a break. It also provides specific measures to maintain eye health, such as eye stretches and setting up an appropriate lighting environment. As a result, the AI ​​Vision Protection System can detect the risk of vision deterioration and visual impairment early and take appropriate measures. This helps maintain vision health and prevent the occurrence of visual impairment. In addition, users can receive specific advice to prevent vision deterioration caused by the widespread use of digital devices, thus protecting their eye health. As a result, the AI ​​Vision Protection System can provide early detection and countermeasures for vision deterioration and visual impairment.

[0070] The AI ​​vision monitoring system according to this embodiment comprises a collection unit, an analysis unit, a communication unit, a prediction unit, and a provision unit. The collection unit collects vision test data, eye photographs, and lifestyle data. For example, the collection unit uses a vision measuring device to acquire vision test data. The vision measuring device can measure the user's vision and collect the results as digital data. The collection unit also uses a camera to take eye photographs. The camera can take photographs of the user's eyes and collect the image data. Furthermore, the collection unit provides an interface for inputting information about the user's lifestyle in order to collect lifestyle data. For example, the user can input information such as eating habits, exercise habits, and sleep patterns. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes vision test data to understand the user's vision status. Based on the vision test data, the analysis unit can evaluate the user's visual acuity values ​​and fluctuations. The analysis unit also analyzes eye photographs to check the user's eye health status. Based on the eye photographs, the analysis unit can identify the presence or absence of eye diseases and structural abnormalities of the eyes. Furthermore, the analysis unit analyzes lifestyle data to identify factors that affect vision. Based on lifestyle data, the analysis unit can analyze the impact of prolonged use of digital devices and irregular lifestyle habits on vision. The communication unit communicates individual visual status to the user based on the results analyzed by the analysis unit. For example, the communication unit notifies the user if their vision is deteriorating or if there are problems with their eye health. The communication unit can use text messages or email as a means of notification. The communication unit can also adjust the frequency of communication. For example, by regularly communicating their visual status, it makes it easier for the user to understand their own visual condition. The prediction unit makes predictions about changes over time based on the visual status communicated by the communication unit. For example, the prediction unit presents the risk of future vision deterioration based on visual status fluctuation data. The prediction unit can predict changes in visual status over time using a prediction algorithm. The provision unit provides long-term countermeasures based on the results predicted by the prediction unit.The service unit, for example, detects signs of eye strain or dryness in real time and advises the user to take a break. The service unit can detect signs of eye strain or dryness using sensors. The service unit also provides specific measures to maintain eye health. For example, the service unit provides advice such as eye stretches and setting an appropriate lighting environment. As a result, the AI ​​vision protection system according to this embodiment can provide early detection and countermeasures for vision loss and visual impairment.

[0071] The data collection unit collects visual acuity test data, eye photographs, and lifestyle data. For example, to acquire visual acuity test data, the unit uses a visual acuity measuring device. This device measures the user's visual acuity and collects the results as digital data. Specifically, the device measures the size of the letters and symbols the user reads on a visual acuity chart and quantifies the results. This allows for an accurate understanding of the user's visual acuity. The data collection unit also uses a camera to take eye photographs. The camera captures high-resolution images, recording detailed information about the user's eyes. For example, the camera can capture detailed images of the eye's surface and blood vessels, aiding in the early detection of eye diseases. Furthermore, the data collection unit provides an interface for inputting information about the user's lifestyle to collect lifestyle data. For example, the user can input information such as their eating habits, exercise habits, and sleep patterns. This allows for the comprehensive collection of factors that may affect visual acuity. The data collection unit centrally manages this data, making it accessible to the analysis and prediction units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0072] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes vision test data to understand the user's visual acuity. Based on the vision test data, the analysis unit can evaluate the user's visual acuity values ​​and fluctuations. Specifically, it analyzes vision test data in a time series to identify patterns of visual acuity fluctuations. The analysis unit also analyzes eye photographs to check the user's eye health. Based on eye photographs, the analysis unit can identify the presence or absence of eye diseases and abnormalities in the eye structure. For example, it can use image recognition technology to detect abnormalities on the surface of the eye and changes in blood vessels, enabling early detection of abnormalities. Furthermore, the analysis unit analyzes lifestyle data to identify factors that affect visual acuity. Based on lifestyle data, the analysis unit can analyze the impact of prolonged use of digital devices and irregular lifestyle habits on visual acuity. For example, by correlating the duration of digital device use with fluctuations in visual acuity, it can identify the causes of vision deterioration. The analysis unit comprehensively analyzes this data to comprehensively evaluate the user's visual acuity and health status. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analyses. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0073] The communication unit communicates individual visual status to users based on the results analyzed by the analysis unit. For example, if a user's vision deteriorates or they have problems with their eye health, the communication unit will notify them. The communication unit can use text messages or email as notification methods. Specifically, it will send notifications to the user's smartphone or computer, providing information on changes in vision and health status. The communication unit can also adjust the frequency of communication. For example, by regularly communicating the user's visual status, it can help users understand their own visual condition. Furthermore, the communication unit can collect user feedback and continuously improve the accuracy and effectiveness of its communications. For example, based on user feedback, it can review the content and timing of notifications and provide more effective communication methods. This allows the communication unit to provide users with information quickly and accurately, supporting early intervention for vision deterioration and changes in eye health.

[0074] The prediction unit predicts age-related changes based on the visual state communicated by the communication unit. For example, the prediction unit presents the risk of future vision deterioration based on visual acuity fluctuation data. The prediction unit can predict age-related changes in visual acuity using a prediction algorithm. Specifically, it analyzes visual acuity fluctuation patterns based on past visual acuity data and lifestyle data to evaluate the risk of future vision deterioration. For example, it can predict the impact of long-term use of digital devices and irregular lifestyle habits on visual acuity and present the risk to the user. Furthermore, the prediction unit can use AI to simulate multiple scenarios and identify the most likely risk. This allows the prediction unit to predict the risk of future vision deterioration with high accuracy and provide the user with information to take appropriate measures. In addition, the prediction unit can continuously revise prediction results based on data updated in real time to respond to the latest situation. This allows the prediction unit to always provide highly accurate risk predictions based on the latest information and support quick and appropriate responses.

[0075] The service provider provides long-term solutions based on the results predicted by the prediction unit. For example, the service provider can detect signs of eye strain and dryness in real time and advise the user to take a break. The service provider can detect signs of eye strain and dryness using sensors. Specifically, it monitors the temperature and humidity around the eyes, blinking frequency, etc., to detect signs of eye strain and dryness. The service provider also provides specific measures to maintain eye health. For example, it provides advice on eye stretches and setting up an appropriate lighting environment. Furthermore, the service provider can propose individualized measures tailored to the user's lifestyle. For example, it can provide advice on when to take breaks to reduce the impact of prolonged digital device use on vision, and advice on eye-friendly foods. In this way, the service provider can provide users with specific and practical measures to help prevent vision loss and visual impairment. Furthermore, the service provider can continuously improve the accuracy and effectiveness of the measures based on user feedback. In this way, the service provider can provide users with optimal measures, enabling early detection and prevention of vision loss and visual impairment.

[0076] The data collection unit collects vision test data, eye photographs, and lifestyle data. For example, the data collection unit uses a vision measuring device to acquire vision test data. The vision measuring device can measure the user's vision and collect the results as digital data. The data collection unit also uses a camera to take photographs of the user's eyes. The camera can take photographs of the user's eyes and collect the image data. Furthermore, the data collection unit provides an interface for inputting information about the user's lifestyle in order to collect lifestyle data. For example, the user can input information such as eating habits, exercise habits, and sleep patterns. By collecting vision test data, eye photographs, and lifestyle data, health data related to vision can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from the vision measuring device into a generating AI to acquire vision test data, and have the generating AI perform the collection of vision test data.

[0077] The analysis unit grasps the state of visual acuity from visual acuity test data, checks the health of the eyes from eye photographs, and identifies factors that affect visual acuity from lifestyle data. For example, the analysis unit analyzes visual acuity test data to understand the state of the user's visual acuity. Based on the visual acuity test data, the analysis unit can evaluate the numerical value and fluctuations of the user's visual acuity. The analysis unit also analyzes eye photographs to check the health of the user's eyes. Based on the eye photographs, the analysis unit can identify the presence or absence of eye diseases and structural abnormalities of the eyes. Furthermore, the analysis unit analyzes lifestyle data to identify factors that affect visual acuity. Based on lifestyle data, the analysis unit can analyze the impact of prolonged use of digital devices and irregular lifestyle habits on visual acuity. In this way, by analyzing visual acuity test data, eye photographs, and lifestyle data, the state of visual acuity, the health of the eyes, and factors that affect visual acuity can be identified. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input visual acuity test data into a generating AI and have the generating AI perform the task of understanding the state of visual acuity.

[0078] The communication unit contacts the user with their individual visual condition based on the analysis results. For example, if the user's vision is deteriorating or there is a problem with their eye health, the communication unit will notify the user accordingly. The communication unit can use text messages or email as a means of notification. The communication unit can also adjust the frequency of contact. For example, by regularly contacting the user with their visual condition, it makes it easier for the user to understand their own visual condition. This allows the user to understand their own visual condition by contacting them with their individual visual condition based on the analysis results. Some or all of the above processing in the communication unit may be performed using AI, for example, or not using AI. For example, the communication unit can input the analysis results into a generating AI and have the generating AI execute the notification of the individual visual condition.

[0079] The prediction unit predicts age-related changes and presents the risk of future vision deterioration. For example, the prediction unit presents the risk of future vision deterioration based on vision fluctuation data. The prediction unit can predict age-related changes in vision using a prediction algorithm. This allows users to take early action by predicting age-related changes and presenting the risk of future vision deterioration. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input vision fluctuation data into a generating AI and have the generating AI perform age-related change predictions.

[0080] The service unit detects signs of eye strain and dryness in real time and advises the user to take a break. The service unit can, for example, use sensors to detect signs of eye strain and dryness. The service unit can detect eye redness and dryness and advise the user to take a break. In addition, the service unit provides appropriate advice to the user based on the results detected in real time. For example, the service unit can advise on stretches to reduce eye strain or setting an appropriate lighting environment. In this way, by detecting signs of eye strain and dryness in real time and advising the user to take a break, vision deterioration can be prevented. Some or all of the above processing in the service unit may be performed using AI, for example, or without AI. For example, the service unit can input data from sensors into a generating AI and have the generating AI perform the detection of signs of eye strain and dryness.

[0081] The service provider offers specific measures to maintain eye health. For example, the service provider provides advice on eye stretches and setting up an appropriate lighting environment. The service provider can provide users with specific measures to maintain eye health. For example, the service provider can explain how to stretch eyes and have the user practice it. The service provider can also advise on how to set up an appropriate lighting environment and have the user practice it. By providing specific measures to maintain eye health, it is possible to prevent vision loss and visual impairment. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input measures to maintain eye health into a generating AI and have the generating AI execute the provision of specific measures.

[0082] The data collection unit estimates the user's emotions and adjusts the timing of eye exam data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit adjusts the timing so that the user can take the eye exam in a relaxed state. If the user is relaxed, the data collection unit can collect the eye exam data immediately. Also, if the user is in a hurry, the data collection unit can collect the eye exam data in a short amount of time. By adjusting the timing of eye exam data collection based on the user's emotions, more accurate data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of eye exam data collection.

[0083] The data collection unit analyzes the user's past vision test history and selects the optimal data collection method. For example, the data collection unit proposes the optimal data collection method based on the user's past vision test data. The data collection unit can select the most effective data collection timing from the user's past vision test history. The data collection unit can also analyze the user's past vision test history and select the optimal data collection method. This allows the optimal data collection method to be selected by analyzing the user's past vision test history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past vision test history into a generating AI and have the generating AI select the optimal data collection method.

[0084] The data collection unit filters the vision test data based on the user's current lifestyle and areas of interest. For example, the data collection unit filters the vision test data considering the user's current lifestyle. The data collection unit can filter the vision test data based on the user's areas of interest. The data collection unit can also filter the vision test data based on the user's current lifestyle and areas of interest. This allows for the collection of highly relevant data by filtering the vision test data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering of the vision test data.

[0085] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit prioritizes collecting important data. If the user is relaxed, the data collection unit can collect detailed data. Also, if the user is in a hurry, the data collection unit can prioritize collecting only the essential data. In this way, by prioritizing the data to be collected based on the user's emotions, important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0086] The data collection unit prioritizes collecting highly relevant data when collecting vision test data, taking into account the user's geographical location. For example, the data collection unit prioritizes collecting highly relevant vision test data based on the user's current location. The data collection unit can collect optimal vision test data by taking into account the user's geographical location. Furthermore, the data collection unit can prioritize collecting highly relevant vision test data based on the user's geographical location. This allows for the acquisition of more accurate data by collecting highly relevant data while considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0087] The data collection unit analyzes the user's social media activity and collects relevant data when collecting vision test data. For example, the data collection unit analyzes the user's social media activity and collects data related to vision. The data collection unit can collect vision test data based on the user's social media activity. The data collection unit can also analyze the user's social media activity and collect relevant vision test data. This allows for the collection of vision-related data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0088] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides the analysis results in a simple format. If the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis based on the user's emotions, the analysis results can be made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0089] The analysis unit adjusts the level of detail of the analysis based on the importance of the visual acuity test data during the analysis. For example, the analysis unit performs a detailed analysis on important visual acuity test data. The analysis unit can perform a simplified analysis on less important visual acuity test data. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the visual acuity test data. This allows for a detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the visual acuity test data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the visual acuity test data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0090] The analysis unit applies different analysis algorithms depending on the category of the visual acuity test data during analysis. For example, the analysis unit applies the optimal analysis algorithm depending on the category of the visual acuity test data. The analysis unit can apply different analysis algorithms based on the category of the visual acuity test data. Furthermore, the analysis unit can select an appropriate analysis algorithm depending on the category of the visual acuity test data. This allows for the provision of optimal analysis results by applying different analysis algorithms depending on the category of the visual acuity test data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the visual acuity test data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0091] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a brief analysis result. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an analysis result of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0092] The analysis unit determines the priority of analysis based on the submission date of the vision test data during the analysis. For example, the analysis unit can prioritize the analysis based on the submission date of the vision test data. The analysis unit can prioritize the analysis of vision test data that was submitted earlier. The analysis unit can also postpone the analysis of vision test data that was submitted later. In this way, by determining the priority of analysis based on the submission date of the vision test data, data that was submitted earlier can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the submission date of the vision test data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0093] The analysis unit adjusts the order of analysis based on the relevance of the vision test data during the analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the vision test data. The analysis unit can prioritize the analysis of vision test data with high relevance. The analysis unit can also postpone the analysis of vision test data with low relevance. In this way, by adjusting the order of analysis based on the relevance of the vision test data, highly relevant data can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the relevance of the vision test data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0094] The communication unit estimates the user's emotions and adjusts the way it communicates based on those emotions. For example, if the user is stressed, the communication unit will communicate in a simple manner. If the user is relaxed, the communication unit can communicate in detail. If the user is in a hurry, the communication unit can communicate to the point. In this way, by adjusting the way it communicates based on the user's emotions, it is possible to communicate in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI or not using AI. For example, the communication unit can input user emotion data into a generative AI and have the generative AI adjust the way it communicates.

[0095] The communication unit adjusts the level of detail in its communication based on the importance of the vision test data. For example, the communication unit provides detailed communication for important vision test data. For less important vision test data, it can provide simplified communication. The communication unit can also adjust the level of detail in its communication based on the importance of the vision test data. This allows for detailed communication for important data by adjusting the level of detail in communication based on the importance of the vision test data. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the importance of the vision test data into a generating AI and have the generating AI perform the adjustment of the level of detail in communication.

[0096] The communication unit applies different communication algorithms depending on the category of the vision test data when communication is made. For example, the communication unit applies the optimal communication algorithm depending on the category of the vision test data. The communication unit can apply different communication algorithms based on the category of the vision test data. The communication unit can also select an appropriate communication algorithm depending on the category of the vision test data. This allows for the provision of the optimal communication method by applying different communication algorithms depending on the category of the vision test data. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the category of the vision test data into a generating AI and have the generating AI execute the application of different communication algorithms.

[0097] The communication unit estimates the user's emotions and adjusts the length of the message based on the estimated emotions. For example, if the user is stressed, the communication unit will send a short, to-the-point message. If the user is relaxed, the communication unit can send a detailed message. Also, if the user is in a hurry, the communication unit can send a concise message. In this way, by adjusting the length of the message based on the user's emotions, the communication unit can send a message of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI or not using AI. For example, the communication unit can input user emotion data into a generative AI and have the generative AI adjust the length of the message.

[0098] The liaison department determines the priority of contact based on the submission date of the vision test data. For example, the liaison department can prioritize contacting individuals with vision test data submitted earlier. Conversely, the liaison department can postpone contacting individuals with vision test data submitted later. This allows the liaison department to prioritize contacting individuals with data submitted earlier by determining the priority of contact based on the submission date of the vision test data. Some or all of the above processing in the liaison department may be performed using AI, for example, or without AI. For example, the liaison department can input the submission dates of the vision test data into a generating AI and have the generating AI determine the priority of contact.

[0099] The communication unit adjusts the order of communication based on the relevance of the vision test data. For example, the communication unit adjusts the order of communication based on the relevance of the vision test data. The communication unit can prioritize communicating highly relevant vision test data. The communication unit can also postpone communicating less relevant vision test data. In this way, by adjusting the order of communication based on the relevance of the vision test data, highly relevant data can be communicated preferentially. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the relevance of the vision test data into a generating AI and have the generating AI perform the adjustment of the order of communication.

[0100] The prediction unit estimates the user's emotions and adjusts the way the prediction is expressed based on the estimated emotions. For example, if the user is stressed, the prediction unit can provide a simple representation of the prediction result. If the user is relaxed, the prediction unit can provide a detailed prediction result. If the user is in a hurry, the prediction unit can provide a concise prediction result. In this way, by adjusting the way the prediction is expressed based on the user's emotions, the prediction result can be made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI adjust the way the prediction is expressed.

[0101] The prediction unit adjusts the level of detail of the prediction based on the importance of the vision test data during the prediction process. For example, the prediction unit can make detailed predictions for important vision test data. For less important vision test data, the prediction unit can make simplified predictions. The prediction unit can also adjust the level of detail of the prediction based on the importance of the vision test data. This allows for detailed predictions for important data by adjusting the level of detail of the prediction based on the importance of the vision test data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the importance of the vision test data into a generating AI and have the generating AI perform the adjustment of the level of detail of the prediction.

[0102] The prediction unit applies different prediction algorithms depending on the category of the vision test data during prediction. For example, the prediction unit applies the optimal prediction algorithm depending on the category of the vision test data. The prediction unit can apply different prediction algorithms based on the category of the vision test data. The prediction unit can also select an appropriate prediction algorithm depending on the category of the vision test data. This allows for the provision of optimal prediction results by applying different prediction algorithms depending on the category of the vision test data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the category of the vision test data into a generating AI and cause the generating AI to apply different prediction algorithms.

[0103] The prediction unit estimates the user's emotions and adjusts the length of the prediction based on the estimated emotions. For example, if the user is stressed, the prediction unit can provide a short, concise prediction. If the user is relaxed, the prediction unit can provide a detailed prediction. If the user is in a hurry, the prediction unit can provide a brief prediction. By adjusting the length of the prediction based on the user's emotions, the system can provide a prediction of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI adjust the length of the prediction.

[0104] The prediction unit determines the prediction priority based on the submission timing of the vision test data. For example, the prediction unit can prioritize predicting vision test data that has been submitted earlier. It can also postpone predicting vision test data that has been submitted later. In this way, by determining the prediction priority based on the submission timing of the vision test data, it is possible to prioritize predicting data that has been submitted earlier. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the submission timing of the vision test data into a generating AI and have the generating AI perform the determination of the prediction priority.

[0105] The prediction unit adjusts the order of predictions based on the relevance of the vision test data during prediction. For example, the prediction unit adjusts the order of predictions based on the relevance of the vision test data. The prediction unit can prioritize predicting vision test data that is highly relevant. The prediction unit can also postpone predicting vision test data that is less relevant. In this way, by adjusting the order of predictions based on the relevance of the vision test data, highly relevant data can be predicted preferentially. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the relevance of the vision test data into a generating AI and have the generating AI perform the adjustment of the prediction order.

[0106] The service provider estimates the user's emotions and adjusts the way the solutions are presented based on the estimated emotions. For example, if the user is stressed, the service provider can provide solutions in a simple manner. If the user is relaxed, the service provider can provide detailed solutions. If the user is in a hurry, the service provider can provide solutions that are to the point. In this way, by adjusting the way the solutions are presented based on the user's emotions, solutions that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way the solutions are presented.

[0107] The service provider adjusts the level of detail of the countermeasures provided based on the importance of the vision test data at the time of provision. For example, the service provider can provide detailed countermeasures for important vision test data. For less important vision test data, the service provider can provide simplified countermeasures. The service provider can also adjust the level of detail of the countermeasures provided based on the importance of the vision test data. This allows for the provision of detailed countermeasures for important data by adjusting the level of detail of the countermeasures provided based on the importance of the vision test data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of the vision test data into a generating AI and have the generating AI perform the adjustment of the level of detail of the countermeasures provided.

[0108] The service provider applies different countermeasure algorithms depending on the category of the vision test data at the time of provision. For example, the service provider applies the optimal countermeasure algorithm depending on the category of the vision test data. The service provider can apply different countermeasure algorithms based on the category of the vision test data. Furthermore, the service provider can select an appropriate countermeasure algorithm depending on the category of the vision test data. This allows for the provision of optimal countermeasures by applying different countermeasure algorithms depending on the category of the vision test data. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the category of the vision test data into a generating AI and have the generating AI execute the application of different countermeasure algorithms.

[0109] The service provider estimates the user's emotions and adjusts the length of the countermeasures provided based on the estimated emotions. For example, if the user is stressed, the service provider can provide short, concise countermeasures. If the user is relaxed, the service provider can provide detailed countermeasures. If the user is in a hurry, the service provider can provide concise countermeasures. In this way, by adjusting the length of the countermeasures provided based on the user's emotions, the service provider can provide countermeasures of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the length of the countermeasures provided.

[0110] The service provider determines the priority of the countermeasures to be provided based on the submission date of the vision test data. For example, the service provider can prioritize countermeasures for vision test data submitted earlier. The service provider can also postpone countermeasures for vision test data submitted later. This allows for prioritizing countermeasures for data submitted earlier by determining the priority of countermeasures based on the submission date of the vision test data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the submission date of the vision test data into a generating AI and have the generating AI determine the priority of the countermeasures to be provided.

[0111] The service provider adjusts the order of the countermeasures provided based on the relevance of the vision test data at the time of provision. For example, the service provider adjusts the order of the countermeasures provided based on the relevance of the vision test data. The service provider can prioritize providing countermeasures for highly relevant vision test data. The service provider can also postpone providing countermeasures for less relevant vision test data. In this way, by adjusting the order of countermeasures provided based on the relevance of the vision test data, highly relevant data can be prioritized. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the relevance of the vision test data into a generating AI and have the generating AI perform the adjustment of the order of the countermeasures provided.

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

[0113] The data collection unit can suggest the optimal collection method by referring to the user's past vision test data when collecting user vision test data. For example, it can select the optimal collection timing and method based on the user's past vision test data. Furthermore, when collecting user lifestyle data, the data collection unit can filter the data based on the user's current lifestyle and areas of interest. This allows for the priority collection of highly relevant data. In addition, the data collection unit can prioritize the collection of highly relevant vision test data by considering the user's geographical location. This allows for the acquisition of more accurate data based on the user's current location.

[0114] The analysis unit can adjust the level of detail in the analysis of visual acuity test data based on its importance. For example, it can perform a detailed analysis on important visual acuity test data and a simplified analysis on less important data. Furthermore, the analysis unit can apply different analysis algorithms depending on the category of the visual acuity test data, thereby providing optimal analysis results. In addition, the analysis unit can determine the priority of analysis based on when the visual acuity test data was submitted. Prioritizing the analysis of data submitted earlier enables a faster response.

[0115] The communication department can estimate the user's emotions and adjust the way it communicates based on those emotions. For example, if the user is stressed, it will communicate in a simple manner, while if the user is relaxed, it will communicate in detail. The communication department can also adjust the level of detail in communication based on the importance of the vision test data. It will communicate in detail for important data and in a simplified manner for less important data. Furthermore, the communication department can determine the priority of communication based on when the vision test data was submitted. Prioritizing communication for data submitted earlier enables a quicker response.

[0116] The prediction unit can adjust the level of detail in its predictions based on the importance of the vision test data. For example, it can perform detailed predictions for important vision test data and simplified predictions for less important data. Furthermore, the prediction unit can apply different prediction algorithms depending on the category of the vision test data, thereby providing optimal prediction results. In addition, the prediction unit can prioritize predictions based on when the vision test data was submitted. Prioritizing earlier submissions enables a quicker response.

[0117] The service provider can estimate the user's emotions and adjust the way the countermeasures are presented based on those emotions. For example, if the user is stressed, the countermeasures will be presented in a simple manner, while if the user is relaxed, more detailed countermeasures will be provided. The service provider can also adjust the level of detail of the countermeasures provided based on the importance of the vision test data at the time of delivery. Detailed countermeasures will be provided for important data, and simplified countermeasures will be provided for less important data. Furthermore, the service provider can determine the priority of the countermeasures provided based on when the vision test data was submitted. Prioritizing countermeasures for data submitted earlier enables a quicker response.

[0118] The data collection unit can estimate the user's emotions and adjust the timing of eye exam data collection based on those emotions. For example, if the user is feeling stressed, the collection timing can be adjusted to allow them to take the eye exam in a relaxed state. The data collection unit can also analyze the user's past eye exam history and select the optimal collection method. This allows it to suggest the optimal collection timing and method based on the user's past data. Furthermore, the data collection unit can filter eye exam data based on the user's current lifestyle and areas of interest. This allows it to prioritize the collection of highly relevant data.

[0119] The analysis unit can adjust the level of detail in the analysis of visual acuity test data based on its importance. For example, it can perform a detailed analysis on important visual acuity test data and a simplified analysis on less important data. Furthermore, the analysis unit can apply different analysis algorithms depending on the category of the visual acuity test data, thereby providing optimal analysis results. In addition, the analysis unit can determine the priority of analysis based on when the visual acuity test data was submitted. Prioritizing the analysis of data submitted earlier enables a faster response.

[0120] The communication department can estimate the user's emotions and adjust the way it communicates based on those emotions. For example, if the user is stressed, it will communicate in a simple manner, while if the user is relaxed, it will communicate in detail. The communication department can also adjust the level of detail in communication based on the importance of the vision test data. It will communicate in detail for important data and in a simplified manner for less important data. Furthermore, the communication department can determine the priority of communication based on when the vision test data was submitted. Prioritizing communication for data submitted earlier enables a quicker response.

[0121] The prediction unit can adjust the level of detail in its predictions based on the importance of the vision test data. For example, it can perform detailed predictions for important vision test data and simplified predictions for less important data. Furthermore, the prediction unit can apply different prediction algorithms depending on the category of the vision test data, thereby providing optimal prediction results. In addition, the prediction unit can prioritize predictions based on when the vision test data was submitted. Prioritizing earlier submissions enables a quicker response.

[0122] The service provider can estimate the user's emotions and adjust the way the countermeasures are presented based on those emotions. For example, if the user is stressed, the countermeasures will be presented in a simple manner, while if the user is relaxed, more detailed countermeasures will be provided. The service provider can also adjust the level of detail of the countermeasures provided based on the importance of the vision test data at the time of delivery. Detailed countermeasures will be provided for important data, and simplified countermeasures will be provided for less important data. Furthermore, the service provider can determine the priority of the countermeasures provided based on when the vision test data was submitted. Prioritizing countermeasures for data submitted earlier enables a quicker response.

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

[0124] Step 1: The data collection unit collects vision test data, eye photographs, and lifestyle data. The data collection unit uses a vision measuring device to acquire vision test data, takes eye photographs using a camera, and provides an interface for inputting information about the user's lifestyle. For example, the user can input information such as eating habits, exercise habits, and sleep patterns. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the visual acuity test data to understand the user's visual acuity, analyzes eye photographs to check the health of the eyes, and analyzes lifestyle data to identify factors that affect visual acuity. Step 3: The communication unit communicates the individual visual condition to the user based on the results analyzed by the analysis unit. The communication unit notifies the user if their vision is deteriorating or if there are problems with their eye health. Text messages or email can be used as notification methods, and the frequency of communication can be adjusted. Step 4: The prediction unit makes predictions about changes over time based on the visual state communicated by the communication unit. Based on the visual acuity fluctuation data, the prediction unit presents the risk of future visual acuity decline and uses a prediction algorithm to predict changes in visual acuity over time. Step 5: The service provider provides long-term solutions based on the results predicted by the forecasting unit. The service provider detects signs of eye strain and dryness in real time, advises the user to take breaks, and provides specific measures to maintain eye health. For example, it may provide advice on eye stretches and setting up an appropriate lighting environment.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, communication unit, prediction unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects vision test data and eye photographs using the camera 42 and vision measuring device of the smart device 14, and provides an interface for inputting lifestyle data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the vision test data, eye photographs, and lifestyle data. The communication unit is implemented in the control unit 46A of the smart device 14 and notifies the user of the analysis results. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts age-related changes in vision. The provision unit is implemented in the control unit 46A of the smart device 14 and detects signs of eye strain and dryness in real time and provides appropriate advice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, communication unit, prediction unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects visual acuity test data and eye photographs using the camera 42 and visual acuity measuring device of the smart glasses 214 and provides an interface for inputting lifestyle data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the visual acuity test data, eye photographs, and lifestyle data. The communication unit is implemented in the control unit 46A of the smart glasses 214 and notifies the user of the analysis results. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts age-related changes in visual acuity. The provision unit is implemented in the control unit 46A of the smart glasses 214 and detects signs of eye strain and dryness in real time and provides appropriate advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, communication unit, prediction unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects vision test data and eye photographs using the camera 42 and vision measuring device of the headset terminal 314, and provides an interface for inputting lifestyle data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the vision test data, eye photographs, and lifestyle data. The communication unit is implemented in the control unit 46A of the headset terminal 314, and notifies the user of the analysis results. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12, and predicts age-related changes in vision. The provision unit is implemented in the control unit 46A of the headset terminal 314, and detects signs of eye strain and dryness in real time and provides appropriate advice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the collection unit, analysis unit, communication unit, prediction unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects visual acuity test data and eye photographs using the camera 42 and visual acuity measuring device of the robot 414 and provides an interface for inputting lifestyle data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the visual acuity test data, eye photographs, and lifestyle data. The communication unit is implemented in the control unit 46A of the robot 414 and notifies the user of the analysis results. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts age-related changes in visual acuity. The provision unit is implemented in the control unit 46A of the robot 414 and detects signs of eye strain and dryness in real time and provides appropriate advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) A data collection unit that collects vision test data, eye photographs, and lifestyle data, An analysis unit analyzes the data collected by the aforementioned collection unit, A communication unit that communicates individual visual states based on the results of analysis by the aforementioned analysis unit, A prediction unit that predicts changes over time based on the visual state communicated by the aforementioned communication unit, The system includes a provisioning unit that provides long-term countermeasures based on the results predicted by the prediction unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect vision test data, eye photographs, and lifestyle data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We assess the state of your vision using eye exam data, check the health of your eyes using eye photographs, and identify factors that affect your vision using lifestyle data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned liaison department, Based on the analysis results, communicate individual visual states to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The prediction unit, It predicts age-related changes and presents the risk of future vision deterioration. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, It detects signs of eye strain and dryness in real time and advises the user to take a break. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, We provide specific measures to maintain eye health. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of eye exam data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past vision test history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting vision test data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting vision test data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting vision test data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the visual acuity test data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the visual acuity test data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the visual acuity test data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the visual acuity test data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned liaison department, It estimates the user's emotions and adjusts the way it communicates based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned liaison department, When contacting you, we will adjust the level of detail in the communication based on the importance of the vision test data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned liaison department, When contacting you, a different contact algorithm will be applied depending on the category of your vision test data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned liaison department, It estimates the user's emotions and adjusts the length of communication based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned liaison department, When contacting you, we will prioritize your contact based on when you submitted your vision test data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned liaison department, When contacting you, we will adjust the order of contact based on the relevance of your vision test data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The prediction unit, It estimates the user's emotions and adjusts how predictions are expressed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The prediction unit, When making predictions, adjust the level of detail based on the importance of the vision test data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The prediction unit, During prediction, different prediction algorithms are applied depending on the category of the vision test data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The prediction unit, It estimates the user's emotions and adjusts the length of the prediction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The prediction unit, When making predictions, the priority of predictions is determined based on when the vision test data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The prediction unit, During prediction, the order of predictions is adjusted based on the relevance of the vision test data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we present the countermeasures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing the data, we adjust the level of detail of the measures provided based on the importance of the vision test data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing the data, different countermeasure algorithms are applied depending on the category of the vision test data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the measures provided based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing the service, we will prioritize the measures to be provided based on when the vision test data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned supply unit is, When providing the data, we adjust the order of the measures provided based on the relevance of the vision test data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A data collection unit that collects vision test data, eye photographs, and lifestyle data, An analysis unit analyzes the data collected by the aforementioned collection unit, A communication unit that communicates individual visual states based on the results of analysis by the aforementioned analysis unit, A prediction unit that predicts changes over time based on the visual state communicated by the aforementioned communication unit, The system includes a provisioning unit that provides long-term countermeasures based on the results predicted by the prediction unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect vision test data, eye photographs, and lifestyle data. The system according to feature 1.

3. The aforementioned analysis unit, We assess the state of your vision using eye exam data, check the health of your eyes using eye photographs, and identify factors that affect your vision using lifestyle data. The system according to feature 1.

4. The aforementioned liaison department, Based on the analysis results, communicate individual visual states to the user. The system according to feature 1.

5. The prediction unit, It predicts age-related changes and presents the risk of future vision deterioration. The system according to feature 1.

6. The aforementioned supply unit is, It detects signs of eye strain and dryness in real time and advises the user to take a break. The system according to feature 1.

7. The aforementioned supply unit is, We provide specific measures to maintain eye health. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of eye exam data collection based on those estimated emotions. The system according to feature 1.

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