system

Abstract

The system according to this embodiment aims to provide early detection and countermeasures for vision loss and visual impairment. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a detection unit, a notification unit, and a suggestion 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 to understand the individual's visual state. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The notification unit notifies the abnormalities detected by the detection unit in real time. The suggestion unit proposes preventive measures and corrective measures based on the abnormalities detected by the detection 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 and countermeasures for vision loss and visual impairment have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide early detection of vision loss and visual impairment and countermeasures therefor.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, a notification unit, and a suggestion 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 to understand the individual's visual state. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The notification unit notifies the abnormalities detected by the detection unit in real time. The suggestion unit proposes preventive and corrective measures based on the abnormalities detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide early detection and 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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The visual health management system according to an embodiment of the present invention is a platform that analyzes health data related to vision and provides early detection and countermeasures for vision loss and visual impairment. This visual health management system analyzes vision test data, eye photographs, and lifestyle data to understand the individual's visual condition and notifies the user in real time if an abnormality is detected. It also proposes preventive and corrective measures for visual impairment. This platform aims to prevent vision loss caused by the widespread use of digital devices. First, the user inputs vision test data, eye photographs, and lifestyle data. For example, they input the results of a vision test, eye photographs, and information about their daily lifestyle. This information is input to the AI. Next, the AI ​​analyzes the input data. The AI ​​analyzes the vision test data and eye photographs to understand the individual's visual condition. For example, it can detect vision loss from the results of a vision test or find abnormalities from eye photographs. It also analyzes lifestyle data to assess the risk of vision loss. If an abnormality is detected, the AI ​​notifies the user in real time. For example, if vision loss is detected from the results of a vision test, the user is notified so that they can take countermeasures early. Similarly, if an abnormality is found in an eye photograph, the user is notified. Furthermore, the AI ​​suggests preventative and corrective measures for visual impairment. For example, it suggests specific preventative measures to reduce the risk of vision loss and measures to improve visual impairment. This allows users to take concrete steps to prevent vision loss and improve visual impairment. This platform enables early detection of vision loss and visual impairment, allowing for appropriate countermeasures. It can prevent vision loss caused by the widespread use of digital devices and maintain healthy vision. For example, it suggests specific preventative measures to prevent vision loss due to prolonged use of digital devices, which users can implement in their daily lives. In addition, it can detect abnormalities early for users at high risk of visual impairment and encourage them to seek appropriate medical attention. In this way, the visual health management system can provide early detection and countermeasures for vision loss and visual impairment.

[0029] The visual health management system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, a notification unit, and a suggestion unit. The collection unit collects visual acuity test data, eye photographs, and lifestyle data. For example, the collection unit can use a visual acuity measuring device to collect visual acuity test data. The collection unit can also use a digital camera or a smartphone camera to take eye photographs. Furthermore, the collection unit can acquire information entered by the user to collect lifestyle data. For example, the collection unit can collect visual acuity test data using a visual acuity measuring device. The collection unit can also take eye photographs using a digital camera. Furthermore, the collection unit can acquire lifestyle data entered by the user. The analysis unit analyzes the data collected by the collection unit to understand the individual's visual state. For example, the analysis unit can analyze visual acuity test data to detect a decline in visual acuity. The analysis unit can also analyze eye photographs to detect abnormalities. Furthermore, the analysis unit can analyze lifestyle data to assess the risk of vision loss. For example, the analysis unit can analyze visual acuity test data to detect a decline in visual acuity. Furthermore, the analysis unit can analyze eye photographs to detect abnormalities. The analysis unit can also analyze lifestyle data to assess the risk of vision loss. The detection unit detects abnormalities based on the data analyzed by the analysis unit. For example, the detection unit can detect vision loss from vision test data. It can also detect abnormalities from eye photographs. Furthermore, the detection unit can assess the risk of vision loss from lifestyle data. For example, the detection unit can detect vision loss from vision test data. It can also detect abnormalities from eye photographs. Furthermore, the detection unit can assess the risk of vision loss from lifestyle data. The notification unit notifies the user in real time of abnormalities detected by the detection unit. For example, the notification unit can notify the user if vision loss is detected from vision test data. It can also notify the user if an abnormality is detected from an eye photograph. Furthermore, the notification unit can notify the user if the risk of vision loss is assessed from lifestyle data.For example, the notification unit can notify the user if a decline in visual acuity is detected from the visual acuity test data. The notification unit can also notify the user if an abnormality is detected from an eye photograph. Furthermore, the notification unit can notify the user if the risk of visual acuity decline is assessed from lifestyle data. The suggestion unit proposes preventive and corrective measures based on the abnormalities detected by the detection unit. For example, the suggestion unit can propose specific preventive measures to reduce the risk of visual acuity decline. It can also propose measures to improve visual impairment. Furthermore, the suggestion unit can make suggestions for improving lifestyle habits. Thus, the visual health management system according to this embodiment can provide early detection and countermeasures for visual acuity decline and visual impairment.

[0030] The data collection unit collects vision test data, eye photographs, and lifestyle data. To collect vision test data, a vision testing device can be used. This device includes, for example, an autorefractometer or a digital vision tester using a vision chart. These devices can accurately measure the user's vision and collect the data digitally. To take eye photographs, a digital camera or smartphone camera can be used. Digital cameras can capture high-resolution images, clearly recording even the finest details of the eye. Smartphone cameras are also easy to use, allowing users to easily take eye photographs at home. To collect lifestyle data, information entered by the user can be obtained. For example, users can input information such as their daily diet, exercise habits, sleep duration, and screen time into the application. This allows the data collection unit to centrally manage vision test data, eye photographs, and lifestyle data, building a comprehensive database related to visual health. Furthermore, the data collection unit stores this data on a cloud server, making it accessible to the analysis and detection units. By adjusting the data collection frequency and accuracy, 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 data collected by the data collection unit to understand individual visual conditions. It can detect vision loss by analyzing visual acuity test data. For example, by analyzing visual acuity test data over time and graphing changes in visual acuity, it can visually grasp trends in vision decline. It can also detect abnormalities by analyzing eye photographs. Using image analysis technology, it can detect abnormalities such as cataracts, glaucoma, and macular degeneration from eye photographs. Specifically, it uses image recognition algorithms to analyze changes in eye structure and color to identify abnormal areas. It can assess the risk of vision loss by analyzing lifestyle data. For example, it can analyze data such as diet, exercise habits, and sleep duration to identify risk factors related to vision loss. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term trends in visual health. This allows the analysis unit to quickly and accurately analyze collected data and understand individual visual conditions in real time. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term visual health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The detection unit detects abnormalities based on data analyzed by the analysis unit. It can detect a decline in visual acuity from visual acuity test data. For example, based on visual acuity test data, it can detect an abnormality if visual acuity falls below a certain standard value. It can also detect abnormalities from eye photographs. Using image analysis technology, it can detect abnormal patterns or color changes from eye photographs and identify the location of the abnormality. It can assess the risk of vision loss from lifestyle data. For example, by analyzing lifestyle data, it can detect an abnormality if risk factors related to vision loss exceed a certain standard value. As a result, the detection unit can quickly and accurately detect abnormalities related to visual health based on visual acuity test data, eye photographs, and lifestyle data. Furthermore, when an abnormality is detected, the detection unit can send information to the notification unit in real time to inform the user of the abnormality. As a result, the detection unit can provide information to enable early detection of abnormalities related to visual health and take appropriate measures.

[0033] The notification unit notifies users in real time of abnormalities detected by the detection unit. If a decline in visual acuity is detected from vision test data, the user can be notified. For example, if visual acuity falls below a certain standard, a notification can be sent to the user's smartphone to inform them of the risk of further vision loss. If an abnormality is found in an eye photograph, the user can be notified. For example, if an abnormal pattern or color change is detected in an eye photograph, the user can be notified to encourage them to see an ophthalmologist. If the risk of vision loss is assessed from lifestyle data, the user can be notified. For example, if risk factors related to vision loss exceed a certain standard based on lifestyle data, the user can be notified to encourage lifestyle improvements. This allows the notification unit to inform users of abnormalities related to visual health in real time and encourage early intervention. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of notifications. For example, the content and timing of notifications can be optimized based on the user's actions and feedback after receiving a notification. This allows the notification unit to quickly and reliably inform users of abnormalities, contributing to the maintenance of visual health.

[0034] The suggestion unit proposes preventive and corrective measures based on the abnormalities detected by the detection unit. It can propose specific preventive measures to reduce the risk of vision loss. For example, it can provide users at high risk of vision loss with advice on regular vision checkups and diet and exercise to maintain eye health. It can propose corrective measures for visual impairment. For example, it can suggest the use of appropriate glasses or contact lenses and vision recovery training to users with declining vision. It can also propose improvements to lifestyle habits. For example, it can suggest specific ways to reduce prolonged screen time and how to take breaks to reduce eye strain. In this way, the suggestion unit can provide users with specific preventive and corrective measures regarding visual health, thereby reducing the risk of vision loss and visual impairment. Furthermore, the suggestion unit can continuously improve the accuracy and effectiveness of its suggestions based on user feedback. For example, it can collect results and feedback after users have implemented the suggestions and optimize the suggestions. In this way, the suggestion unit can provide users with effective preventive and corrective measures and contribute to maintaining visual health.

[0035] The data collection unit can collect vision test data, eye photographs, and lifestyle data. For example, the data collection unit can use a vision measuring device to collect vision test data. It can also use a digital camera or a smartphone camera to take eye photographs. Furthermore, the data collection unit can acquire information entered by the user to collect lifestyle data. For example, the data collection unit can collect vision test data using a vision measuring device. It can also take eye photographs using a digital camera. Furthermore, it can acquire lifestyle data entered by the user. This allows for an understanding of the visual state by collecting vision test data, eye photographs, and lifestyle data. 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 vision test data acquired by a vision measuring device into a generating AI and have the generating AI perform analysis of the vision test data.

[0036] The analysis unit can analyze the data collected by the collection unit to understand the individual's visual state. For example, the analysis unit can analyze visual acuity test data to detect a decline in visual acuity. It can also analyze eye photographs to detect abnormalities. Furthermore, the analysis unit can analyze lifestyle data to assess the risk of vision loss. For example, the analysis unit can analyze visual acuity test data to detect a decline in visual acuity. It can also analyze eye photographs to detect abnormalities. Furthermore, the analysis unit can analyze lifestyle data to assess the risk of vision loss. In this way, by analyzing the collected data, the individual's visual state can be understood. 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 visual acuity test data acquired by the collection unit into a generating AI and have the generating AI perform the analysis of the visual acuity test data.

[0037] The detection unit can detect anomalies based on data analyzed by the analysis unit. For example, the detection unit can detect a decline in visual acuity from visual acuity test data. Furthermore, the detection unit can detect anomalies from eye photographs. In addition, the detection unit can assess the risk of visual acuity decline from lifestyle data. This enables early detection of visual impairment by detecting anomalies based on analyzed data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the visual acuity test data analyzed by the analysis unit into a generating AI and have the generating AI perform anomaly detection.

[0038] The notification unit can notify the user in real time of any abnormalities detected by the detection unit. For example, the notification unit can notify the user if a decrease in visual acuity is detected from visual acuity test data. It can also notify the user if an abnormality is found in an eye photograph. Furthermore, the notification unit can notify the user if a risk of visual acuity deterioration is assessed from lifestyle data. This enables early countermeasures by notifying the detected abnormalities in real time. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input abnormality data detected by the detection unit into a generating AI and have the generating AI generate the notification content.

[0039] The suggestion unit can propose preventive and corrective measures based on the abnormalities detected by the detection unit. For example, the suggestion unit can propose specific preventive measures to reduce the risk of vision loss. It can also propose measures to improve visual impairment. Furthermore, the suggestion unit can make suggestions for improving lifestyle habits. This makes it possible to improve vision loss and visual impairment by proposing preventive and corrective measures based on the detected abnormalities. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input abnormal data detected by the detection unit into a generating AI and have the generating AI execute suggestions for preventive and corrective measures.

[0040] The data collection unit can analyze the user's past vision test history and select the optimal data collection method. For example, the data collection unit can analyze the time periods when the user previously underwent vision tests and collect data at the same time periods. Furthermore, the data collection unit can focus on specific test items based on the user's past vision test results. In addition, the data collection unit can select the most effective data collection method from the user's past vision test history. For example, the data collection unit can analyze the time periods when the user previously underwent vision tests and collect data at the same time periods. Furthermore, the data collection unit can focus on specific test items based on the user's past vision test results. Furthermore, the data collection unit can select the most effective data collection method from the user's past vision test history. This allows for the selection of the optimal data collection method 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 data into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user frequently uses digital devices, the data collection unit can collect data based on device usage time. Furthermore, if the user participates in sports, the data collection unit can prioritize collecting post-exercise vision data. Additionally, if the user has a specific health problem, the data collection unit can prioritize collecting data related to that problem. This allows for the collection of more relevant data by filtering data based on the user's 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 about the user's lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in an urban area, the data collection unit can collect data related to the risk of vision deterioration specific to urban areas. Also, if the user is in a natural environment, the data collection unit can collect data that takes into account the effects of natural light. Furthermore, if the user is in a specific region, the data collection unit can collect data that takes into account the environmental factors of that region. For example, if the user is in an urban area, the data collection unit can collect data related to the risk of vision deterioration specific to urban areas. Also, if the user is in a natural environment, the data collection unit can collect data that takes into account the effects of natural light. Furthermore, if the user is in a specific region, the data collection unit can collect data that takes into account the environmental factors of that region. This allows for the priority collection of highly relevant data by considering the user's geographical location information. 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 collect highly relevant data.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts about their eyesight on social media, the data collection unit can collect data based on that content. Furthermore, if the data collection unit determines from the user's social media activity that there is a high risk of vision deterioration, it can prioritize the collection of relevant data. In addition, the data collection unit can collect data based on lifestyle information shared by the user on social media. For example, if a user posts about their eyesight on social media, the data collection unit can collect data based on that content. Furthermore, if the data collection unit determines from the user's social media activity that there is a high risk of vision deterioration, it can prioritize the collection of relevant data. Furthermore, the data collection unit can collect data based on lifestyle information shared by the user on social media. This allows for the collection of relevant 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.

[0044] The analysis unit can adjust 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 can perform a detailed analysis on important visual acuity test data. Furthermore, the analysis unit can perform a basic analysis on general visual acuity test data. In addition, the analysis unit can perform a specialized analysis on specific visual acuity test data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance 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 importance of the visual acuity test data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the eye photograph during analysis. For example, if the eye photograph is normal, the analysis unit can apply a basic analysis algorithm. If an abnormality is observed in the eye photograph, the analysis unit can apply a detailed analysis algorithm. Furthermore, if the eye photograph suggests a specific disease, the analysis unit can apply a specialized analysis algorithm. This allows for more accurate analysis by applying different analysis algorithms depending on the category of the eye photograph. 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 category data of the eye photographs into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data submission date during analysis. For example, the analysis unit can prioritize the analysis of recently submitted data. It can also prioritize the analysis of data with high urgency. Furthermore, the analysis unit can postpone the analysis of older data. For example, the analysis unit can prioritize the analysis of recently submitted data. It can also prioritize the analysis of data with high urgency. Furthermore, the analysis unit can postpone the analysis of older data. This allows for the prioritization of analysis based on the data submission date, thereby prioritizing the analysis of data with high urgency. 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 data submission date into a generating AI and have the generating AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows the analysis unit to prioritize the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the 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 relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The detection unit can improve detection accuracy by considering the interrelationships of vision test data during detection. For example, the detection unit can detect anomalies by combining multiple vision test data. Furthermore, the detection unit can detect anomalies by combining vision test data with eye photographs. Additionally, the detection unit can detect anomalies by combining vision test data with lifestyle data. This improves detection accuracy by considering the interrelationships of vision test data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the interrelationships of vision test data into a generating AI and have the generating AI perform the detection accuracy improvement.

[0049] The detection unit can perform detection while considering the attribute information of the person submitting the eye photograph. For example, the detection unit can detect anomalies by considering the age of the submitter. The detection unit can also detect anomalies by considering the gender of the submitter. Furthermore, the detection unit can detect anomalies by considering the health status of the submitter. For example, the detection unit can detect anomalies by considering the age of the submitter. The detection unit can also detect anomalies by considering the gender of the submitter. Furthermore, the detection unit can detect anomalies by considering the health status of the submitter. This makes it possible to detect anomalies with higher accuracy by considering the attribute information of the submitter. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the submitter's attribute information into a generating AI and have the generating AI perform anomaly detection.

[0050] The detection unit can perform detection while considering the geographical distribution of the data. For example, the detection unit can prioritize the detection of anomalous data in a specific region. The detection unit can also detect anomalies by combining geographically related data. Furthermore, the detection unit can detect anomalies by comparing data from different regions. For example, the detection unit can prioritize the detection of anomalous data in a specific region. The detection unit can also detect anomalies by combining geographically related data. Furthermore, the detection unit can detect anomalies by comparing data from different regions. This allows for the detection of region-specific anomalies by considering the geographical distribution of the data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the geographical distribution of the data into a generating AI and have the generating AI perform anomaly detection.

[0051] The detection unit can improve the accuracy of detection by referring to relevant literature during detection. For example, the detection unit can adjust the anomaly detection algorithm based on relevant literature. The detection unit can also detect anomalies by referring to data in relevant literature. Furthermore, the detection unit can improve the accuracy of anomaly detection by utilizing knowledge from relevant literature. For example, the detection unit can adjust the anomaly detection algorithm based on relevant literature. The detection unit can also detect anomalies by referring to data in relevant literature. Furthermore, the detection unit can improve the accuracy of anomaly detection by utilizing knowledge from relevant literature. As a result, the accuracy of detection is improved by referring to relevant literature. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data from relevant literature into a generating AI and have the generating AI perform anomaly detection accuracy improvement.

[0052] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can prioritize using notification methods that the user has preferred in the past. Furthermore, the notification unit can select the most effective notification method from the user's past notification history. In addition, the notification unit can analyze the user's past notification history to select the optimal notification timing. This allows the notification unit to select the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0053] The notification unit can determine the priority of notifications based on the severity of the anomaly when a notification is sent. For example, if a major anomaly is detected, the notification unit can send a notification immediately. If a minor anomaly is detected, the notification unit can postpone sending a notification. Furthermore, the notification unit can adjust the timing of notifications according to the severity of the anomaly. For example, if a major anomaly is detected, the notification unit can send a notification immediately. If a minor anomaly is detected, the notification unit can postpone sending a notification. Furthermore, the notification unit can adjust the timing of notifications according to the severity of the anomaly. This allows important anomalies to be notified preferentially by determining the priority of notifications based on the severity of the anomaly. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly severity data into a generating AI and have the generating AI determine the priority of notifications.

[0054] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit can send a push notification. If the user is using a tablet, the notification unit can send an in-app notification. Furthermore, if the user is using a smartwatch, the notification unit can send a vibration notification. For example, if the user is using a smartphone, the notification unit can send a push notification. Furthermore, if the user is using a tablet, the notification unit can send an in-app notification. Furthermore, if the user is using a smartwatch, the notification unit can send a vibration notification. This allows the system to select the optimal notification method by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI and have the generating AI select the optimal notification method.

[0055] The notification unit can adjust the timing of notifications by taking into account the user's schedule information. For example, the notification unit can refer to the user's calendar information and send notifications during free time. Also, if the user is in a meeting, the notification unit can send a notification after the meeting has ended. Furthermore, if the user is exercising, the notification unit can send a notification after the exercise has finished. For example, the notification unit can refer to the user's calendar information and send notifications during free time. Also, if the user is in a meeting, the notification unit can send a notification after the meeting has ended. Furthermore, if the user is exercising, the notification unit can send a notification after the exercise has finished. This allows notifications to be sent at the optimal time by taking into account the user's schedule information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's schedule information into a generating AI and have the generating AI adjust the timing of notifications.

[0056] The suggestion unit can adjust the level of detail of its suggestions based on the risk of vision loss. For example, in the case of high risk, the suggestion unit can propose detailed preventive and corrective measures. In the case of medium risk, the suggestion unit can propose basic preventive and corrective measures. Furthermore, in the case of low risk, the suggestion unit can propose simple advice. By adjusting the level of detail of the suggestions based on the risk of vision loss, more effective suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input vision loss risk data into a generating AI and have the generating AI adjust the level of detail of the suggestions.

[0057] The proposal unit can apply different proposal algorithms depending on the category of visual impairment when making a proposal. For example, in the case of myopia, the proposal unit can propose preventive and corrective measures specifically for myopia. Also, in the case of hyperopia, the proposal unit can propose preventive and corrective measures specifically for hyperopia. Furthermore, in the case of presbyopia, the proposal unit can propose preventive and corrective measures specifically for presbyopia. This makes it possible to make more appropriate proposals by applying different proposal algorithms depending on the category of visual impairment. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input visual impairment category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0058] The proposal department can prioritize proposals based on the timing of submission of vision loss risk data. For example, the proposal department can prioritize recently submitted risk data. It can also prioritize risk data with high urgency. Furthermore, the proposal department can postpone older risk data. This allows for a rapid response to high-urgency risks by prioritizing proposals based on the timing of submission of vision loss risk data. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input vision loss risk submission timing data into a generating AI and have the generating AI determine the priority of proposals.

[0059] The proposal unit can adjust the order of proposals based on the relevance of the risks of vision loss when making proposals. For example, the proposal unit can prioritize proposing highly relevant risk data. It can also postpone proposing less relevant risk data. Furthermore, the proposal unit can dynamically adjust the order of proposals according to the relevance of the risk data. For example, the proposal unit can prioritize proposing highly relevant risk data. It can also postpone proposing less relevant risk data. Furthermore, the proposal unit can dynamically adjust the order of proposals according to the relevance of the risk data. This allows for priority addressing of highly relevant risks by adjusting the order of proposals based on the relevance of the risks of vision loss. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance data of the risks of vision loss into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0060] The suggestion unit can make optimal suggestions by considering the user's lifestyle data. For example, if the user uses digital devices excessively, the suggestion unit can suggest reducing device usage time. Also, if the user is not getting enough exercise, the suggestion unit can suggest increasing exercise. Furthermore, if the user has an irregular lifestyle, the suggestion unit can suggest adopting a regular lifestyle. This makes it possible to make more appropriate suggestions by considering the user's lifestyle data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle data into a generating AI and have the generating AI execute the optimal suggestion.

[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 visual health management system can further analyze the user's past vision test data to understand patterns of vision fluctuations. For example, it can detect a trend of declining vision from past vision test data and propose countermeasures early on. If vision is stable, it can provide advice to maintain the current level. Furthermore, if vision is improving, it can identify the factors contributing to this improvement and propose measures for continued improvement. This allows for the provision of appropriate measures tailored to the user's vision fluctuations.

[0063] The visual health management system can further predict the risk of vision loss based on the user's lifestyle data. For example, if prolonged use of digital devices or irregular lifestyle habits increase the risk of vision loss, the system can notify the user of this risk and suggest corrective measures. It can also provide advice on maintaining vision for users who maintain healthy lifestyle habits. Furthermore, it can analyze the impact of specific lifestyle habits on vision and suggest individualized countermeasures. This enables vision loss risk management based on the user's lifestyle.

[0064] The visual health management system can further consider the user's geographical location to assess region-specific risks of vision deterioration. For example, it can suggest measures to address urban-specific risks of vision deterioration (e.g., air pollution and prolonged use of digital devices) for users living in urban areas. It can also suggest vision protection measures that take into account the effects of natural light for users living in natural environments. Furthermore, it can analyze the risks of vision deterioration in specific regions and provide region-specific countermeasures. This allows for the provision of vision protection measures based on the user's geographical location.

[0065] The visual health management system can further analyze users' social media activity to understand their interests and risks related to vision. For example, if a user frequently posts about vision on social media, the system can suggest vision protection measures based on that content. Furthermore, if a user is identified as being at high risk of vision deterioration, the system can prioritize the collection of relevant data and suggest countermeasures. In addition, vision protection measures can be customized based on lifestyle information shared by the user. This allows the system to provide vision protection measures tailored to the user's social media activity.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects vision test data, eye photographs, and lifestyle data. The data collection unit collects vision test data using a vision measuring device, takes eye photographs using a digital camera or smartphone camera, and obtains lifestyle data entered by the user. Step 2: The analysis unit analyzes the data collected by the data collection unit to understand the individual's visual condition. The analysis unit analyzes visual acuity test data to detect vision loss, analyzes eye photographs to identify abnormalities, and analyzes lifestyle data to assess the risk of vision loss. Step 3: The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit detects a decrease in visual acuity from visual acuity test data, identifies anomalies from eye photographs, and assesses the risk of visual acuity decline from lifestyle data. Step 4: The notification unit notifies the user in real time of any abnormalities detected by the detection unit. The notification unit notifies the user when a decrease in visual acuity is detected from the visual acuity test data, when an abnormality is found from the eye photograph, or when the risk of visual acuity deterioration is assessed from the lifestyle data. Step 5: The proposal unit proposes preventive and corrective measures based on the abnormalities detected by the detection unit. The proposal unit provides specific preventive measures to reduce the risk of vision loss, measures to improve visual impairment, and suggestions for improving lifestyle habits.

[0068] (Example of form 2) The visual health management system according to an embodiment of the present invention is a platform that analyzes health data related to vision and provides early detection and countermeasures for vision loss and visual impairment. This visual health management system analyzes vision test data, eye photographs, and lifestyle data to understand the individual's visual condition and notifies the user in real time if an abnormality is detected. It also proposes preventive and corrective measures for visual impairment. This platform aims to prevent vision loss caused by the widespread use of digital devices. First, the user inputs vision test data, eye photographs, and lifestyle data. For example, they input the results of a vision test, eye photographs, and information about their daily lifestyle. This information is input to the AI. Next, the AI ​​analyzes the input data. The AI ​​analyzes the vision test data and eye photographs to understand the individual's visual condition. For example, it can detect vision loss from the results of a vision test or find abnormalities from eye photographs. It also analyzes lifestyle data to assess the risk of vision loss. If an abnormality is detected, the AI ​​notifies the user in real time. For example, if vision loss is detected from the results of a vision test, the user is notified so that they can take countermeasures early. Similarly, if an abnormality is found in an eye photograph, the user is notified. Furthermore, the AI ​​suggests preventative and corrective measures for visual impairment. For example, it suggests specific preventative measures to reduce the risk of vision loss and measures to improve visual impairment. This allows users to take concrete steps to prevent vision loss and improve visual impairment. This platform enables early detection of vision loss and visual impairment, allowing for appropriate countermeasures. It can prevent vision loss caused by the widespread use of digital devices and maintain healthy vision. For example, it suggests specific preventative measures to prevent vision loss due to prolonged use of digital devices, which users can implement in their daily lives. In addition, it can detect abnormalities early for users at high risk of visual impairment and encourage them to seek appropriate medical attention. In this way, the visual health management system can provide early detection and countermeasures for vision loss and visual impairment.

[0069] The visual health management system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, a notification unit, and a suggestion unit. The collection unit collects visual acuity test data, eye photographs, and lifestyle data. For example, the collection unit can use a visual acuity measuring device to collect visual acuity test data. The collection unit can also use a digital camera or a smartphone camera to take eye photographs. Furthermore, the collection unit can acquire information entered by the user to collect lifestyle data. For example, the collection unit can collect visual acuity test data using a visual acuity measuring device. The collection unit can also take eye photographs using a digital camera. Furthermore, the collection unit can acquire lifestyle data entered by the user. The analysis unit analyzes the data collected by the collection unit to understand the individual's visual state. For example, the analysis unit can analyze visual acuity test data to detect a decline in visual acuity. The analysis unit can also analyze eye photographs to detect abnormalities. Furthermore, the analysis unit can analyze lifestyle data to assess the risk of vision loss. For example, the analysis unit can analyze visual acuity test data to detect a decline in visual acuity. Furthermore, the analysis unit can analyze eye photographs to detect abnormalities. The analysis unit can also analyze lifestyle data to assess the risk of vision loss. The detection unit detects abnormalities based on the data analyzed by the analysis unit. For example, the detection unit can detect vision loss from vision test data. It can also detect abnormalities from eye photographs. Furthermore, the detection unit can assess the risk of vision loss from lifestyle data. For example, the detection unit can detect vision loss from vision test data. It can also detect abnormalities from eye photographs. Furthermore, the detection unit can assess the risk of vision loss from lifestyle data. The notification unit notifies the user in real time of abnormalities detected by the detection unit. For example, the notification unit can notify the user if vision loss is detected from vision test data. It can also notify the user if an abnormality is detected from an eye photograph. Furthermore, the notification unit can notify the user if the risk of vision loss is assessed from lifestyle data.For example, the notification unit can notify the user if a decline in visual acuity is detected from the visual acuity test data. The notification unit can also notify the user if an abnormality is detected from an eye photograph. Furthermore, the notification unit can notify the user if the risk of visual acuity decline is assessed from lifestyle data. The suggestion unit proposes preventive and corrective measures based on the abnormalities detected by the detection unit. For example, the suggestion unit can propose specific preventive measures to reduce the risk of visual acuity decline. It can also propose measures to improve visual impairment. Furthermore, the suggestion unit can make suggestions for improving lifestyle habits. Thus, the visual health management system according to this embodiment can provide early detection and countermeasures for visual acuity decline and visual impairment.

[0070] The data collection unit collects vision test data, eye photographs, and lifestyle data. To collect vision test data, a vision testing device can be used. This device includes, for example, an autorefractometer or a digital vision tester using a vision chart. These devices can accurately measure the user's vision and collect the data digitally. To take eye photographs, a digital camera or smartphone camera can be used. Digital cameras can capture high-resolution images, clearly recording even the finest details of the eye. Smartphone cameras are also easy to use, allowing users to easily take eye photographs at home. To collect lifestyle data, information entered by the user can be obtained. For example, users can input information such as their daily diet, exercise habits, sleep duration, and screen time into the application. This allows the data collection unit to centrally manage vision test data, eye photographs, and lifestyle data, building a comprehensive database related to visual health. Furthermore, the data collection unit stores this data on a cloud server, making it accessible to the analysis and detection units. By adjusting the data collection frequency and accuracy, 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.

[0071] The analysis unit analyzes data collected by the data collection unit to understand individual visual conditions. It can detect vision loss by analyzing visual acuity test data. For example, by analyzing visual acuity test data over time and graphing changes in visual acuity, it can visually grasp trends in vision decline. It can also detect abnormalities by analyzing eye photographs. Using image analysis technology, it can detect abnormalities such as cataracts, glaucoma, and macular degeneration from eye photographs. Specifically, it uses image recognition algorithms to analyze changes in eye structure and color to identify abnormal areas. It can assess the risk of vision loss by analyzing lifestyle data. For example, it can analyze data such as diet, exercise habits, and sleep duration to identify risk factors related to vision loss. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term trends in visual health. This allows the analysis unit to quickly and accurately analyze collected data and understand individual visual conditions in real time. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term visual health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0072] The detection unit detects abnormalities based on data analyzed by the analysis unit. It can detect a decline in visual acuity from visual acuity test data. For example, based on visual acuity test data, it can detect an abnormality if visual acuity falls below a certain standard value. It can also detect abnormalities from eye photographs. Using image analysis technology, it can detect abnormal patterns or color changes from eye photographs and identify the location of the abnormality. It can assess the risk of vision loss from lifestyle data. For example, by analyzing lifestyle data, it can detect an abnormality if risk factors related to vision loss exceed a certain standard value. As a result, the detection unit can quickly and accurately detect abnormalities related to visual health based on visual acuity test data, eye photographs, and lifestyle data. Furthermore, when an abnormality is detected, the detection unit can send information to the notification unit in real time to inform the user of the abnormality. As a result, the detection unit can provide information to enable early detection of abnormalities related to visual health and take appropriate measures.

[0073] The notification unit notifies users in real time of abnormalities detected by the detection unit. If a decline in visual acuity is detected from vision test data, the user can be notified. For example, if visual acuity falls below a certain standard, a notification can be sent to the user's smartphone to inform them of the risk of further vision loss. If an abnormality is found in an eye photograph, the user can be notified. For example, if an abnormal pattern or color change is detected in an eye photograph, the user can be notified to encourage them to see an ophthalmologist. If the risk of vision loss is assessed from lifestyle data, the user can be notified. For example, if risk factors related to vision loss exceed a certain standard based on lifestyle data, the user can be notified to encourage lifestyle improvements. This allows the notification unit to inform users of abnormalities related to visual health in real time and encourage early intervention. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of notifications. For example, the content and timing of notifications can be optimized based on the user's actions and feedback after receiving a notification. This allows the notification unit to quickly and reliably inform users of abnormalities, contributing to the maintenance of visual health.

[0074] The suggestion unit proposes preventive and corrective measures based on the abnormalities detected by the detection unit. It can propose specific preventive measures to reduce the risk of vision loss. For example, it can provide users at high risk of vision loss with advice on regular vision checkups and diet and exercise to maintain eye health. It can propose corrective measures for visual impairment. For example, it can suggest the use of appropriate glasses or contact lenses and vision recovery training to users with declining vision. It can also propose improvements to lifestyle habits. For example, it can suggest specific ways to reduce prolonged screen time and how to take breaks to reduce eye strain. In this way, the suggestion unit can provide users with specific preventive and corrective measures regarding visual health, thereby reducing the risk of vision loss and visual impairment. Furthermore, the suggestion unit can continuously improve the accuracy and effectiveness of its suggestions based on user feedback. For example, it can collect results and feedback after users have implemented the suggestions and optimize the suggestions. In this way, the suggestion unit can provide users with effective preventive and corrective measures and contribute to maintaining visual health.

[0075] The data collection unit can collect vision test data, eye photographs, and lifestyle data. For example, the data collection unit can use a vision measuring device to collect vision test data. It can also use a digital camera or a smartphone camera to take eye photographs. Furthermore, the data collection unit can acquire information entered by the user to collect lifestyle data. For example, the data collection unit can collect vision test data using a vision measuring device. It can also take eye photographs using a digital camera. Furthermore, it can acquire lifestyle data entered by the user. This allows for an understanding of the visual state by collecting vision test data, eye photographs, and lifestyle data. 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 vision test data acquired by a vision measuring device into a generating AI and have the generating AI perform analysis of the vision test data.

[0076] The analysis unit can analyze the data collected by the collection unit to understand the individual's visual state. For example, the analysis unit can analyze visual acuity test data to detect a decline in visual acuity. It can also analyze eye photographs to detect abnormalities. Furthermore, the analysis unit can analyze lifestyle data to assess the risk of vision loss. For example, the analysis unit can analyze visual acuity test data to detect a decline in visual acuity. It can also analyze eye photographs to detect abnormalities. Furthermore, the analysis unit can analyze lifestyle data to assess the risk of vision loss. In this way, by analyzing the collected data, the individual's visual state can be understood. 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 visual acuity test data acquired by the collection unit into a generating AI and have the generating AI perform the analysis of the visual acuity test data.

[0077] The detection unit can detect anomalies based on data analyzed by the analysis unit. For example, the detection unit can detect a decline in visual acuity from visual acuity test data. Furthermore, the detection unit can detect anomalies from eye photographs. In addition, the detection unit can assess the risk of visual acuity decline from lifestyle data. This enables early detection of visual impairment by detecting anomalies based on analyzed data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the visual acuity test data analyzed by the analysis unit into a generating AI and have the generating AI perform anomaly detection.

[0078] The notification unit can notify the user in real time of any abnormalities detected by the detection unit. For example, the notification unit can notify the user if a decrease in visual acuity is detected from visual acuity test data. It can also notify the user if an abnormality is found in an eye photograph. Furthermore, the notification unit can notify the user if a risk of visual acuity deterioration is assessed from lifestyle data. This enables early countermeasures by notifying the detected abnormalities in real time. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input abnormality data detected by the detection unit into a generating AI and have the generating AI generate the notification content.

[0079] The suggestion unit can propose preventive and corrective measures based on the abnormalities detected by the detection unit. For example, the suggestion unit can propose specific preventive measures to reduce the risk of vision loss. It can also propose measures to improve visual impairment. Furthermore, the suggestion unit can make suggestions for improving lifestyle habits. This makes it possible to improve vision loss and visual impairment by proposing preventive and corrective measures based on the detected abnormalities. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input abnormal data detected by the detection unit into a generating AI and have the generating AI execute suggestions for preventive and corrective measures.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay data collection until the user is relaxed. If the user is relaxed, the data collection unit can start collecting data immediately. Furthermore, if the user is in a hurry, the data collection unit can adjust the timing to complete data collection in a short amount of time. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, 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 without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of data collection.

[0081] The data collection unit can analyze the user's past vision test history and select the optimal data collection method. For example, the data collection unit can analyze the time periods when the user previously underwent vision tests and collect data at the same time periods. Furthermore, the data collection unit can focus on specific test items based on the user's past vision test results. In addition, the data collection unit can select the most effective data collection method from the user's past vision test history. For example, the data collection unit can analyze the time periods when the user previously underwent vision tests and collect data at the same time periods. Furthermore, the data collection unit can focus on specific test items based on the user's past vision test results. Furthermore, the data collection unit can select the most effective data collection method from the user's past vision test history. This allows for the selection of the optimal data collection method 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 data into a generating AI and have the generating AI select the optimal data collection method.

[0082] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user frequently uses digital devices, the data collection unit can collect data based on device usage time. Furthermore, if the user participates in sports, the data collection unit can prioritize collecting post-exercise vision data. Additionally, if the user has a specific health problem, the data collection unit can prioritize collecting data related to that problem. This allows for the collection of more relevant data by filtering data based on the user's 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 about the user's lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize collecting stress-related data. If the user is relaxed, the data collection unit can collect overall health data in a balanced manner. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting the most important data. For example, if the user is stressed, the data collection unit can prioritize collecting stress-related data. If the user is relaxed, the data collection unit can collect overall health data in a balanced manner. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting the most important data. This allows for the priority collection of important data by determining data priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, 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 without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI determine the priority of the data.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in an urban area, the data collection unit can collect data related to the risk of vision deterioration specific to urban areas. Also, if the user is in a natural environment, the data collection unit can collect data that takes into account the effects of natural light. Furthermore, if the user is in a specific region, the data collection unit can collect data that takes into account the environmental factors of that region. For example, if the user is in an urban area, the data collection unit can collect data related to the risk of vision deterioration specific to urban areas. Also, if the user is in a natural environment, the data collection unit can collect data that takes into account the effects of natural light. Furthermore, if the user is in a specific region, the data collection unit can collect data that takes into account the environmental factors of that region. This allows for the priority collection of highly relevant data by considering the user's geographical location information. 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 collect highly relevant data.

[0085] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts about their eyesight on social media, the data collection unit can collect data based on that content. Furthermore, if the data collection unit determines from the user's social media activity that there is a high risk of vision deterioration, it can prioritize the collection of relevant data. In addition, the data collection unit can collect data based on lifestyle information shared by the user on social media. For example, if a user posts about their eyesight on social media, the data collection unit can collect data based on that content. Furthermore, if the data collection unit determines from the user's social media activity that there is a high risk of vision deterioration, it can prioritize the collection of relevant data. Furthermore, the data collection unit can collect data based on lifestyle information shared by the user on social media. This allows for the collection of relevant 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.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the way the analysis is expressed.

[0087] The analysis unit can adjust 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 can perform a detailed analysis on important visual acuity test data. Furthermore, the analysis unit can perform a basic analysis on general visual acuity test data. In addition, the analysis unit can perform a specialized analysis on specific visual acuity test data. This allows for detailed analysis of important data by adjusting the level of detail based on the importance 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 importance of the visual acuity test data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of the eye photograph during analysis. For example, if the eye photograph is normal, the analysis unit can apply a basic analysis algorithm. If an abnormality is observed in the eye photograph, the analysis unit can apply a detailed analysis algorithm. Furthermore, if the eye photograph suggests a specific disease, the analysis unit can apply a specialized analysis algorithm. This allows for more accurate analysis by applying different analysis algorithms depending on the category of the eye photograph. 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 category data of the eye photographs into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. 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 user emotion data into the generating AI and have the generating AI adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the data submission date during analysis. For example, the analysis unit can prioritize the analysis of recently submitted data. It can also prioritize the analysis of data with high urgency. Furthermore, the analysis unit can postpone the analysis of older data. For example, the analysis unit can prioritize the analysis of recently submitted data. It can also prioritize the analysis of data with high urgency. Furthermore, the analysis unit can postpone the analysis of older data. This allows for the prioritization of analysis based on the data submission date, thereby prioritizing the analysis of data with high urgency. 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 data submission date into a generating AI and have the generating AI determine the analysis priority.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows the analysis unit to prioritize the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the 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 relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0092] The detection unit can estimate the user's emotions and adjust the anomaly detection criteria based on the estimated user emotions. For example, the detection unit can tighten the anomaly detection criteria if the user is tense. Conversely, the detection unit can loosen the anomaly detection criteria if the user is relaxed. Furthermore, the detection unit can set criteria for quickly detecting anomalies if the user is in a hurry. By adjusting the anomaly detection criteria according to the user's emotions, more appropriate anomaly detection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, with 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 detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into the generating AI and have the generating AI adjust the criteria for anomaly detection.

[0093] The detection unit can improve detection accuracy by considering the interrelationships of vision test data during detection. For example, the detection unit can detect anomalies by combining multiple vision test data. Furthermore, the detection unit can detect anomalies by combining vision test data with eye photographs. Additionally, the detection unit can detect anomalies by combining vision test data with lifestyle data. This improves detection accuracy by considering the interrelationships of vision test data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the interrelationships of vision test data into a generating AI and have the generating AI perform the detection accuracy improvement.

[0094] The detection unit can perform detection while considering the attribute information of the person submitting the eye photograph. For example, the detection unit can detect anomalies by considering the age of the submitter. The detection unit can also detect anomalies by considering the gender of the submitter. Furthermore, the detection unit can detect anomalies by considering the health status of the submitter. For example, the detection unit can detect anomalies by considering the age of the submitter. The detection unit can also detect anomalies by considering the gender of the submitter. Furthermore, the detection unit can detect anomalies by considering the health status of the submitter. This makes it possible to detect anomalies with higher accuracy by considering the attribute information of the submitter. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the submitter's attribute information into a generating AI and have the generating AI perform anomaly detection.

[0095] The detection unit can estimate the user's emotions and adjust the display order of the detection results based on the estimated emotions. For example, if the user is nervous, the detection unit can display important detection results first. If the user is relaxed, the detection unit can display detailed detection results sequentially. Furthermore, if the user is in a hurry, the detection unit can display concise detection results first. For example, if the user is nervous, the detection unit can display important detection results first. If the user is relaxed, the detection unit can display detailed detection results sequentially. Furthermore, if the user is in a hurry, the detection unit can display concise detection results first. By adjusting the display order of detection results according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into the generating AI and have the generating AI adjust the display order of the detection results.

[0096] The detection unit can perform detection while considering the geographical distribution of the data. For example, the detection unit can prioritize the detection of anomalous data in a specific region. The detection unit can also detect anomalies by combining geographically related data. Furthermore, the detection unit can detect anomalies by comparing data from different regions. For example, the detection unit can prioritize the detection of anomalous data in a specific region. The detection unit can also detect anomalies by combining geographically related data. Furthermore, the detection unit can detect anomalies by comparing data from different regions. This allows for the detection of region-specific anomalies by considering the geographical distribution of the data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the geographical distribution of the data into a generating AI and have the generating AI perform anomaly detection.

[0097] The detection unit can improve the accuracy of detection by referring to relevant literature during detection. For example, the detection unit can adjust the anomaly detection algorithm based on relevant literature. The detection unit can also detect anomalies by referring to data in relevant literature. Furthermore, the detection unit can improve the accuracy of anomaly detection by utilizing knowledge from relevant literature. For example, the detection unit can adjust the anomaly detection algorithm based on relevant literature. The detection unit can also detect anomalies by referring to data in relevant literature. Furthermore, the detection unit can improve the accuracy of anomaly detection by utilizing knowledge from relevant literature. As a result, the accuracy of detection is improved by referring to relevant literature. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data from relevant literature into a generating AI and have the generating AI perform anomaly detection accuracy improvement.

[0098] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is tense, the notification unit can deliver a notification in a calm tone. If the user is relaxed, the notification unit can deliver a notification in a bright tone. Furthermore, if the user is in a hurry, the notification unit can deliver a quick and concise notification. This allows for more appropriate notifications by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into a generating AI and have the generating AI adjust the notification method.

[0099] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can prioritize using notification methods that the user has preferred in the past. Furthermore, the notification unit can select the most effective notification method from the user's past notification history. In addition, the notification unit can analyze the user's past notification history to select the optimal notification timing. This allows the notification unit to select the optimal notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0100] The notification unit can determine the priority of notifications based on the severity of the anomaly when a notification is sent. For example, if a major anomaly is detected, the notification unit can send a notification immediately. If a minor anomaly is detected, the notification unit can postpone sending a notification. Furthermore, the notification unit can adjust the timing of notifications according to the severity of the anomaly. For example, if a major anomaly is detected, the notification unit can send a notification immediately. If a minor anomaly is detected, the notification unit can postpone sending a notification. Furthermore, the notification unit can adjust the timing of notifications according to the severity of the anomaly. This allows important anomalies to be notified preferentially by determining the priority of notifications based on the severity of the anomaly. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly severity data into a generating AI and have the generating AI determine the priority of notifications.

[0101] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated emotions. For example, if the user is nervous, the notification unit can make the content concise and reassuring. If the user is relaxed, the notification unit can make the content more detailed and informative. Furthermore, if the user is in a hurry, the notification unit can make the content to the point. For example, if the user is nervous, the notification unit can make the content concise and reassuring. If the user is relaxed, the notification unit can make the content more detailed and informative. Furthermore, if the user is in a hurry, the notification unit can make the content to the point. By adjusting the content of the notification according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into a generating AI and have the AI ​​adjust the notification content.

[0102] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit can send a push notification. If the user is using a tablet, the notification unit can send an in-app notification. Furthermore, if the user is using a smartwatch, the notification unit can send a vibration notification. For example, if the user is using a smartphone, the notification unit can send a push notification. Furthermore, if the user is using a tablet, the notification unit can send an in-app notification. Furthermore, if the user is using a smartwatch, the notification unit can send a vibration notification. This allows the system to select the optimal notification method by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI and have the generating AI select the optimal notification method.

[0103] The notification unit can adjust the timing of notifications by taking into account the user's schedule information. For example, the notification unit can refer to the user's calendar information and send notifications during free time. Also, if the user is in a meeting, the notification unit can send a notification after the meeting has ended. Furthermore, if the user is exercising, the notification unit can send a notification after the exercise has finished. For example, the notification unit can refer to the user's calendar information and send notifications during free time. Also, if the user is in a meeting, the notification unit can send a notification after the meeting has ended. Furthermore, if the user is exercising, the notification unit can send a notification after the exercise has finished. This allows notifications to be sent at the optimal time by taking into account the user's schedule information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's schedule information into a generating AI and have the generating AI adjust the timing of notifications.

[0104] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can offer concise and reassuring suggestions. If the user is relaxed, the suggestion unit can offer suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can offer suggestions that get straight to the point. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal department can input user emotion data into a generation AI and have the generation AI adjust the way the proposal is expressed.

[0105] The suggestion unit can adjust the level of detail of its suggestions based on the risk of vision loss. For example, in the case of high risk, the suggestion unit can propose detailed preventive and corrective measures. In the case of medium risk, the suggestion unit can propose basic preventive and corrective measures. Furthermore, in the case of low risk, the suggestion unit can propose simple advice. By adjusting the level of detail of the suggestions based on the risk of vision loss, more effective suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input vision loss risk data into a generating AI and have the generating AI adjust the level of detail of the suggestions.

[0106] The proposal unit can apply different proposal algorithms depending on the category of visual impairment when making a proposal. For example, in the case of myopia, the proposal unit can propose preventive and corrective measures specifically for myopia. Also, in the case of hyperopia, the proposal unit can propose preventive and corrective measures specifically for hyperopia. Furthermore, in the case of presbyopia, the proposal unit can propose preventive and corrective measures specifically for presbyopia. This makes it possible to make more appropriate proposals by applying different proposal algorithms depending on the category of visual impairment. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input visual impairment category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0107] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user emotion data into a generating AI and have the AI ​​adjust the length of the suggestion.

[0108] The proposal department can prioritize proposals based on the timing of submission of vision loss risk data. For example, the proposal department can prioritize recently submitted risk data. It can also prioritize risk data with high urgency. Furthermore, the proposal department can postpone older risk data. This allows for a rapid response to high-urgency risks by prioritizing proposals based on the timing of submission of vision loss risk data. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input vision loss risk submission timing data into a generating AI and have the generating AI determine the priority of proposals.

[0109] The proposal unit can adjust the order of proposals based on the relevance of the risks of vision loss when making proposals. For example, the proposal unit can prioritize proposing highly relevant risk data. It can also postpone proposing less relevant risk data. Furthermore, the proposal unit can dynamically adjust the order of proposals according to the relevance of the risk data. For example, the proposal unit can prioritize proposing highly relevant risk data. It can also postpone proposing less relevant risk data. Furthermore, the proposal unit can dynamically adjust the order of proposals according to the relevance of the risk data. This allows for priority addressing of highly relevant risks by adjusting the order of proposals based on the relevance of the risks of vision loss. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance data of the risks of vision loss into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0110] The suggestion unit can make optimal suggestions by considering the user's lifestyle data. For example, if the user uses digital devices excessively, the suggestion unit can suggest reducing device usage time. Also, if the user is not getting enough exercise, the suggestion unit can suggest increasing exercise. Furthermore, if the user has an irregular lifestyle, the suggestion unit can suggest adopting a regular lifestyle. This makes it possible to make more appropriate suggestions by considering the user's lifestyle data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle data into a generating AI and have the generating AI execute the optimal suggestion.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The visual health management system can further estimate the user's emotions and provide visual health advice based on those emotions. For example, if the user is stressed, it can suggest eye exercises or breaks to help them relax. If the user is relaxed, it can suggest a long-term visual health plan to maintain their vision. Furthermore, if the user is in a hurry, it can suggest quick and actionable visual health measures. This allows for the provision of appropriate advice tailored to the user's emotions.

[0113] The visual health management system can further analyze the user's past vision test data to understand patterns of vision fluctuations. For example, it can detect a trend of declining vision from past vision test data and propose countermeasures early on. If vision is stable, it can provide advice to maintain the current level. Furthermore, if vision is improving, it can identify the factors contributing to this improvement and propose measures for continued improvement. This allows for the provision of appropriate measures tailored to the user's vision fluctuations.

[0114] The visual health management system can further predict the risk of vision loss based on the user's lifestyle data. For example, if prolonged use of digital devices or irregular lifestyle habits increase the risk of vision loss, the system can notify the user of this risk and suggest corrective measures. It can also provide advice on maintaining vision for users who maintain healthy lifestyle habits. Furthermore, it can analyze the impact of specific lifestyle habits on vision and suggest individualized countermeasures. This enables vision loss risk management based on the user's lifestyle.

[0115] The visual health management system can further consider the user's geographical location to assess region-specific risks of vision deterioration. For example, it can suggest measures to address urban-specific risks of vision deterioration (e.g., air pollution and prolonged use of digital devices) for users living in urban areas. It can also suggest vision protection measures that take into account the effects of natural light for users living in natural environments. Furthermore, it can analyze the risks of vision deterioration in specific regions and provide region-specific countermeasures. This allows for the provision of vision protection measures based on the user's geographical location.

[0116] The visual health management system can further analyze users' social media activity to understand their interests and risks related to vision. For example, if a user frequently posts about vision on social media, the system can suggest vision protection measures based on that content. Furthermore, if a user is identified as being at high risk of vision deterioration, the system can prioritize the collection of relevant data and suggest countermeasures. In addition, vision protection measures can be customized based on lifestyle information shared by the user. This allows the system to provide vision protection measures tailored to the user's social media activity.

[0117] The visual health management system can further estimate the user's emotions and adjust the timing of vision tests based on those emotions. For example, if the user is stressed, the vision test can be delayed until they are relaxed. If the user is relaxed, the vision test can be started immediately. Furthermore, if the user is in a hurry, the system can be adjusted to complete the vision test in a short amount of time. This allows for the provision of appropriate timing for vision tests in accordance with the user's emotions.

[0118] The visual health management system can further estimate the user's emotions and adjust the display method of vision test results based on those emotions. For example, if the user is nervous, it can provide simple and highly visible results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise results that get straight to the point. This allows for the display of vision test results in a way that is appropriate to the user's emotions.

[0119] The visual health management system can further estimate the user's emotions and adjust the anomaly detection criteria based on those emotions. For example, if the user is stressed, the anomaly detection criteria can be tightened. Conversely, if the user is relaxed, the criteria can be loosened. Furthermore, if the user is in a hurry, the criteria for rapid anomaly detection can be set. This enables appropriate anomaly detection that is tailored to the user's emotions.

[0120] The visual health management system can further estimate the user's emotions and adjust the notification method based on those emotions. For example, if the user is stressed, notifications can be delivered in a calm tone. If the user is relaxed, notifications can be delivered in a bright tone. Furthermore, if the user is in a hurry, notifications can be delivered quickly and concisely. This allows for the provision of appropriate notification methods tailored to the user's emotions.

[0121] The visual health management system can further estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, it can offer concise and reassuring suggestions. If the user is relaxed, it can offer suggestions that include more detailed information. Furthermore, if the user is in a hurry, it can offer suggestions that are to the point. This allows the system to provide appropriate suggestions tailored to the user's emotions.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The data collection unit collects vision test data, eye photographs, and lifestyle data. The data collection unit collects vision test data using a vision measuring device, takes eye photographs using a digital camera or smartphone camera, and obtains lifestyle data entered by the user. Step 2: The analysis unit analyzes the data collected by the data collection unit to understand the individual's visual condition. The analysis unit analyzes visual acuity test data to detect vision loss, analyzes eye photographs to identify abnormalities, and analyzes lifestyle data to assess the risk of vision loss. Step 3: The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit detects a decrease in visual acuity from visual acuity test data, identifies anomalies from eye photographs, and assesses the risk of visual acuity decline from lifestyle data. Step 4: The notification unit notifies the user in real time of any abnormalities detected by the detection unit. The notification unit notifies the user when a decrease in visual acuity is detected from the visual acuity test data, when an abnormality is found from the eye photograph, or when the risk of visual acuity deterioration is assessed from the lifestyle data. Step 5: The proposal unit proposes preventive and corrective measures based on the abnormalities detected by the detection unit. The proposal unit provides specific preventive measures to reduce the risk of vision loss, measures to improve visual impairment, and suggestions for improving lifestyle habits.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, notification unit, and proposal 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, eye photographs, and lifestyle data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to understand the individual visual state. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and detects abnormalities based on the analysis results. The notification unit is implemented in the specific processing unit 46A of the smart device 14, for example, and notifies the user in real time when an abnormality is detected. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes preventive and corrective measures for vision deterioration. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the data collection unit, analysis unit, detection unit, notification unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects visual acuity test data, eye photographs, and lifestyle data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to understand the individual visual state. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and detects abnormalities based on the analysis results. The notification unit is implemented in the control unit 46A of the smart glasses 214, for example, and notifies the user in real time when an abnormality is detected. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes preventive and corrective measures for vision deterioration. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, notification unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects visual acuity test data, eye photographs, and lifestyle data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to understand the individual visual state. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and detects abnormalities based on the analysis results. The notification unit is implemented in the control unit 46A of the headset terminal 314, for example, and notifies the user in real time when an abnormality is detected. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes preventive and corrective measures for vision deterioration. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, notification unit, and proposal 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, eye photographs, and lifestyle data using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to understand the individual visual state. The detection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and detects abnormalities based on the analysis results. The notification unit is implemented in the control unit 46A of the robot 414, for example, and notifies the user in real time when an abnormality is detected. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes preventive and corrective measures for vision deterioration. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (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 to understand individual visual states, A detection unit that detects anomalies based on the data analyzed by the analysis unit, A notification unit that notifies in real time of the abnormality detected by the detection unit, The system includes a proposal unit that suggests preventive measures and corrective measures based on the abnormalities detected by the detection 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, The data collected by the aforementioned collection unit is analyzed to understand the individual visual state. The system described in Appendix 1, characterized by the features described herein. (Note 4) The detection unit is The analysis unit detects anomalies based on the data it has analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, The detection unit notifies of any abnormalities detected in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Based on the abnormalities detected by the aforementioned detection unit, preventive measures and corrective measures are proposed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) The aforementioned collection unit is During data collection, 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 10) 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 11) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) 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 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the eye photograph. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit is The system estimates the user's emotions and adjusts the anomaly detection criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit is During detection, the accuracy of the detection is improved by considering the interrelationships of the visual acuity test data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit is During detection, the system takes into account the attribute information of the person who submitted the eye photograph. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit is It estimates the user's emotions and adjusts the display order of the detection results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The detection unit is During detection, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The detection unit is During detection, we refer to relevant literature to improve detection accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When a notification is sent, the notification priority is determined based on the severity of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, It estimates the user's emotions and adjusts the content of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending notifications, the timing of the notifications will be adjusted to take into account the user's schedule information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the risk of vision loss. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the category of visual impairment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When submitting a proposal, prioritize proposals based on when the risk of vision loss was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the risk of vision loss. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned proposal section is, When making a proposal, we take into account the user's lifestyle data to provide the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 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 to understand individual visual states, A detection unit that detects anomalies based on the data analyzed by the analysis unit, A notification unit that notifies in real time of the abnormality detected by the detection unit, The system includes a proposal unit that suggests preventive measures and corrective measures based on the abnormalities detected by the detection 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, The data collected by the aforementioned collection unit is analyzed to understand the individual visual state. The system according to feature 1.

4. The detection unit is The analysis unit detects anomalies based on the data it has analyzed. The system according to feature 1.

5. The aforementioned notification unit, The detection unit notifies of any abnormalities detected in real time. The system according to feature 1.

6. The aforementioned proposal section is, Based on the abnormalities detected by the aforementioned detection unit, preventive measures and corrective measures are proposed. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past vision test history and select the optimal data collection method. The system according to feature 1.

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