System

Abstract

An object of a system according to an embodiment is to collect and analyze health data of a user and provide appropriate feedback.SOLUTION: A system includes a data collection unit, an analysis unit, and a notification unit. The data collection unit collects health data of a user. The analysis unit analyzes the health data collected by the data collection unit. The notification unit notifies the user of the result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Existing technologies are not yet capable of effectively collecting and analyzing users' health data and providing appropriate feedback, and there is room for improvement.

[0005] The system according to the embodiment aims to collect and analyze a user's health data and provide appropriate feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a notification unit. The data collection unit collects health data of a user. The analysis unit analyzes the health data collected by the data collection unit. The notification unit notifies the user of the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect and analyze the user's health data and provide appropriate feedback. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The health management system according to an embodiment of the present invention collects data on issues and actual conditions related to sleep, health, and posture, and uses generative AI to analyze and visualize them. This system suggests ways to improve mental stability, concentration, and lifestyle habits. This allows the health management system to comprehensively manage and improve the user's health condition and lifestyle habits.

[0029] A health management system according to an embodiment includes a data collection unit, an analysis unit, and a notification unit. The data collection unit collects health data from a user. For example, the data collection unit uses sensors built into smart glasses to collect data such as heart rate, body temperature, and step count. The data collection unit can also acquire data from smartphones and other wearable devices. For example, the data collection unit acquires heart rate data from a smartwatch and step count data from a smartphone. The data collection unit can also collect environmental data (temperature, humidity, weather, etc.). For example, the data collection unit measures temperature and humidity using a weather sensor to acquire weather data. The analysis unit uses a generation AI to analyze the health data collected by the data collection unit. For example, the generation AI may analyze data using a text generation AI (e.g., LLM) to detect abnormalities in health status. The generation AI may also integrate and analyze multiple data sources using a multimodal generation AI. The generation AI may also analyze data trends and predict future health risks. For example, the generation AI may analyze fluctuations in heart rate data to detect abnormal patterns. The multimodal generative AI integrates and analyzes heart rate data and body temperature data to more accurately assess health status. The generative AI predicts future health risks based on past data and suggests preventive measures. The notification unit notifies the user of the results of the analysis by the analysis unit. For example, the notification unit may send a notification to a smartphone to notify the user of an abnormality in their health status. The notification unit may also notify the user using an audio alert. The notification unit may also display a notification on the smartglasses display. For example, the notification unit may send a notification through a smartphone app to notify the user of an abnormality. The audio alert notifies the user by playing an audio message. The smartglasses display may overlay a notification on the user's field of view to notify the user of an abnormality. This allows the health management system according to the embodiment to comprehensively manage and improve the user's health status and lifestyle habits. For example, the system collects the user's health data, analyzes it with the generative AI, and, if an abnormality is detected, sends a notification to the smartphone.Audio alerts can be used to alert the user to any abnormalities, and notifications can also be displayed on the smart glasses display to alert the user to any abnormalities.

[0030] The data collection unit can use a camera built into the smart glasses to track the user's gaze, evaluate the user's concentration level in real time based on the gaze movement, and provide appropriate feedback. The data collection unit, for example, uses a camera built into the smart glasses to track the user's gaze and evaluate the user's concentration level in real time based on the gaze movement. For example, if the gaze remains within a certain range, it is determined that the user is concentrating, and if the gaze moves frequently, it is determined that the user's concentration is declining. The data collection unit also evaluates the user's concentration level based on the gaze tracking data and provides appropriate feedback if the user's concentration is declining. For example, if it determines that the user's concentration is declining, it notifies the user to take a break. The data collection unit also collects gaze tracking data over a long period of time and analyzes fluctuations in the user's concentration level. For example, if the user's concentration tends to decline during certain time periods or environmental conditions, it suggests that the user avoid those time periods or environmental conditions. This allows the user's concentration level to be evaluated in real time and provides appropriate feedback, which is expected to improve the user's concentration.

[0031] The data collection unit can detect changes in the user's body temperature using a temperature sensor built into the smart glasses and suggest adjustments to heating and cooling. For example, the data collection unit has a temperature sensor built into the smart glasses and monitors the user's body temperature in real time. For example, if the body temperature rises, it suggests increasing the air conditioning, and if the body temperature drops, it suggests increasing the heating. The data collection unit also uses the temperature sensor to detect changes in the user's body temperature and suggests appropriate adjustments to heating and cooling. For example, if the body temperature exceeds a certain range, it notifies the user to change the heating and cooling settings. The data collection unit also collects body temperature data over a long period of time and analyzes the user's body temperature fluctuation pattern. For example, if body temperature tends to fluctuate depending on a specific time of day or activity status, it suggests adjusting the heating and cooling settings to suit that time of day or activity status. In this way, the system detects changes in the user's body temperature and suggests appropriate adjustments to heating and cooling, providing a comfortable environment.

[0032] The data collection unit adds AR functionality to the smart glasses, enabling it to overlay health information and notifications on the user's field of view. The data collection unit, for example, adds AR functionality to the smart glasses and overlays health information on the user's field of view. For example, it displays data such as heart rate, step count, and body temperature in real time. The data collection unit also uses the AR functionality to overlay notifications on the user's field of view. For example, it displays emergency alerts, reminders, and messages on the user's field of view. The data collection unit also develops an interface for overlaying health information and notifications, allowing the user to intuitively check the information within their field of view. For example, it allows the user to select and operate information with eye movements. This allows the user to intuitively check the information by overlaying health information and notifications on the user's field of view.

[0033] The data collection unit can link the smart glasses with other wearable devices to realize comprehensive health management. The data collection unit, for example, links the smart glasses with a smart watch to realize comprehensive health management. For example, heart rate and step count data obtained from the smart watch can be displayed on the smart glasses to grasp the overall health condition. The data collection unit also links the smart glasses with a smart ring to realize comprehensive health management. For example, body temperature and sleep data obtained from the smart ring can be displayed on the smart glasses to grasp the overall health condition. The data collection unit also develops a platform for linking the smart glasses with other wearable devices and integrates and analyzes the data. For example, data obtained from multiple devices can be centrally managed to realize comprehensive health management. This makes it possible to link the smart glasses with other wearable devices to realize comprehensive health management.

[0034] Combining a gyro sensor and an accelerometer can detect a user's subtle movements and perform posture analysis. Combining a gyro sensor and an accelerometer, for example, using a gyro sensor and an accelerometer built into smart glasses, can detect a user's subtle movements in real time. For example, it can detect subtle movements of the head and neck and analyze posture changes with high accuracy. Combining a gyro sensor and an accelerometer can also record and analyze a user's posture in detail. For example, it can detect subtle changes in posture while working at a desk and notify the user to maintain appropriate posture. Furthermore, an algorithm for detecting subtle movements can be developed, and data from the gyro sensor and accelerometer can be integrated and analyzed. For example, it can detect posture changes from subtle movements with high accuracy and provide feedback to improve posture. Combining a gyro sensor and an accelerometer enables more accurate posture analysis.

[0035] Posture data can be collected over a long period of time, changes in the user's posture can be analyzed, and an individually optimized posture improvement program can be proposed. Posture data can be collected over a long period of time, for example, using a gyro sensor built into smart glasses. For example, posture data can be recorded over several weeks to several months, and changes in posture can be analyzed. The posture data collected over a long period of time can be analyzed to understand changes in the user's posture. For example, trends in posture changes depending on specific time periods or activity status can be identified. Furthermore, an individually optimized posture improvement program can be proposed based on the results of the posture data analysis. For example, stretches and exercises can be suggested in accordance with changes in the user's posture to improve posture. In this way, by collecting and analyzing posture data over a long period of time, an individually optimized posture improvement program can be proposed.

[0036] By adding a vibration feedback function to smart glasses, it is possible to notify users by vibration if their posture becomes poor. Smart glasses are equipped with a built-in vibration feedback function, which can notify users by vibration if their posture becomes poor. For example, if their posture becomes poor during long periods of desk work, the glasses can vibrate to warn them. The vibration feedback function can also be used to notify users in real time if their posture becomes poor. For example, a vibration can be used to notify them if their back becomes rounded or their neck is tilted forward. The vibration feedback function can also be used to provide training programs for improving posture. For example, when performing exercises to maintain good posture, the glasses can vibrate to notify users of the appropriate timing. This is expected to encourage users to correct their posture by notifying them by vibration if their posture becomes poor.

[0037] By linking smart glasses with fitness apps, it is possible to provide training programs based on posture data. By linking smart glasses with fitness apps, for example, training programs based on posture data can be provided. For example, stretches and exercises to improve posture can be suggested. In addition, by linking smart glasses with fitness apps, posture data can be analyzed in real time to customize training programs. For example, individual training menus can be provided based on the user's posture. In addition, a platform for linking smart glasses with fitness apps can be developed to integrate and analyze data. For example, data obtained from multiple devices can be centrally managed to provide a comprehensive training program. This will allow linking smart glasses with fitness apps and providing training programs based on posture data, which is expected to improve users' posture.

[0038] It is possible to build a system that collects health data in real time, and uses AI to instantly detect abnormalities and notify the user. Health data is collected in real time, for example, using sensors built into smart glasses to collect health data such as heart rate, body temperature, and number of steps in real time. For example, if the heart rate is abnormally high, the user is immediately notified. In addition, an AI algorithm is developed to analyze health data in real time and detect abnormalities. For example, if body temperature rises suddenly, the abnormality is detected and the user is notified. In addition, a system is built that sends health data collected in real time to the cloud, where AI instantly analyzes and detects abnormalities. For example, if an abnormality is detected, a notification is sent to a smartphone. This allows health data to be collected in real time, and AI instantly detects and notifies the user, enabling early health management.

[0039] Based on the health data, a user's lifestyle habits can be analyzed in detail and an individually optimized health improvement plan can be proposed. Based on the health data, a user's lifestyle data can be collected, for example, using sensors built into smart glasses. For example, data such as the number of steps taken, sleep time, and meal timing can be recorded. AI can then analyze the collected lifestyle data to evaluate the user's health condition. For example, if a user's health condition is deteriorating due to lack of exercise or sleep, an improvement plan can be proposed. A system can also be built that proposes individually optimized health improvement plans based on the lifestyle data. For example, appropriate exercise and dietary advice can be provided based on the user's data. This allows for a detailed analysis of lifestyle habits based on the health data and proposes individually optimized health improvement plans, which is expected to improve the user's health condition.

[0040] Health data can be stored in the cloud, enabling remote diagnosis in collaboration with medical institutions. Health data can be stored in the cloud and, for example, transmitted to the cloud in real time using sensors built into smart glasses. For example, data such as heart rate, body temperature, and number of steps can be stored in the cloud. A system can also be built to share health data stored in the cloud with medical institutions and enable remote diagnosis. For example, a doctor can check the data on the cloud and notify the user of the diagnosis results. A platform can also be developed to store health data in the cloud and perform remote diagnosis in collaboration with medical institutions. For example, a system can be provided that allows users to consult with medical institutions online. This allows users to manage their health more efficiently by storing health data in the cloud and enabling remote diagnosis in collaboration with medical institutions.

[0041] It can be linked to apps that provide dietary and exercise advice to users based on health data. Based on health data, for example, sensors built into smart glasses can be used to collect health data and link to apps that provide dietary and exercise advice. For example, an appropriate exercise plan can be suggested based on step count and heart rate data. In addition, a system can be built that links to apps that provide dietary and exercise advice to users based on health data. For example, an appropriate diet plan can be suggested based on body temperature and sleep data. In addition, a platform can be developed to collect health data and link to apps that provide dietary and exercise advice. For example, data obtained from multiple devices can be centrally managed to achieve comprehensive health management. This will allow users to manage their health more effectively by linking to apps that provide dietary and exercise advice based on health data.

[0042] A camera built into smart glasses can be used to monitor a user's eye movements and detect signs of drowsiness early. Using a camera built into smart glasses, a user's eye movements can be monitored in real time. For example, blinking frequency and eye opening / closing status can be analyzed to detect signs of drowsiness early. Furthermore, an algorithm for monitoring eye movements can be developed to build a system that detects signs of drowsiness early. For example, if the eyes remain closed for a certain period of time, the system will detect drowsiness and notify the user. Furthermore, eye movement data can be collected over a long period of time and analyzed for signs of drowsiness. For example, if a user tends to fall asleep more easily during certain times or situations, the system will suggest avoiding those times or situations. By monitoring a user's eye movements and detecting signs of drowsiness early, it is expected that drowsiness can be prevented and concentration can be maintained.

[0043] By improving the dozing detection algorithm and learning the user's individual dozing patterns, highly accurate detection can be achieved. By improving the dozing detection algorithm, for example, a system can be constructed that learns the user's individual dozing patterns. For example, individual dozing patterns can be identified based on past dozing data. Furthermore, by learning the user's individual dozing patterns, highly accurate dozing detection can be achieved. For example, a tendency for a user to doze off during specific time periods or situations can be learned, and dozing can be detected based on that pattern. Furthermore, by improving the dozing detection algorithm and learning the user's individual dozing patterns, a system can be developed that learns individual dozing patterns in real time. For example, if the user's dozing pattern changes, the algorithm automatically adapts. In this way, by improving the dozing detection algorithm and learning the user's individual dozing patterns, highly accurate dozing detection can be achieved.

[0044] By linking the drowsiness detection function with an in-vehicle system, it is possible to prevent drowsiness while driving. By linking the drowsiness detection function with an in-vehicle system, for example, a system can be built to prevent drowsiness while driving. For example, if drowsiness is detected while driving, the in-vehicle system will sound an alarm. Furthermore, by linking with the in-vehicle system, drowsiness while driving can be detected in real time. For example, if smart glasses detect drowsiness, the in-vehicle system will vibrate to alert the driver. Furthermore, a platform for linking the drowsiness detection function with the in-vehicle system will be developed to integrate and analyze data. For example, data on drowsiness while driving can be sent to the in-vehicle system, which will issue a warning in real time. By linking the drowsiness detection function with the in-vehicle system, it is expected that drowsiness while driving can be prevented and safer driving can be supported.

[0045] Drowsiness detection can be introduced into office environments and used as a tool to maintain employee concentration. For example, if a system detects employees dozing off while working at their desks, it can vibrate to notify them. Furthermore, by introducing this system into office environments, a system can be built to monitor employees' concentration in real time. For example, if dozing is detected, it can suggest a break to restore concentration. Furthermore, a platform for introducing this system into office environments can be developed to integrate and analyze data. For example, data on employees' dozing can be collected and specific advice provided on how to maintain concentration. Introducing this system into office environments is expected to maintain employee concentration and improve work efficiency.

[0046] The eye-tracking function of smart glasses can be used to develop a system that controls a cursor with eye movements. Using the eye-tracking function of smart glasses, for example, a system can be developed that controls a cursor with eye movements. For example, an eye-tracking function can be implemented using a camera built into smart glasses to develop a system that controls a cursor with eye movements. For example, the cursor can be moved by moving the gaze, and a click can be performed by fixing the gaze. In addition, eye-tracking data can be analyzed in real time to develop an algorithm that controls the cursor according to eye movements. For example, the speed and direction of the cursor can be adjusted according to eye movements. In addition, the eye-tracking function can be used to develop a user interface that controls the cursor with eye movements. For example, menus can be selected or scrolling can be performed according to eye movements. This makes it possible to operate a PC without using your hands by using the eye-tracking function to control the cursor with eye movements, which is expected to improve work efficiency.

[0047] Intuitive PC operation can be achieved by combining voice commands with head and neck movements. We will develop a system that uses voice commands in addition to head and neck movements. For example, we will develop a system that recognizes voice commands using a microphone built into smart glasses and uses head and neck movements in combination to operate a PC. For example, operations can be performed by giving voice commands such as "click" or "scroll." We will also develop an algorithm that combines voice commands with head and neck movements to achieve intuitive PC operation. For example, moving the cursor by moving your head to the right while giving a voice command such as "move right." We will also develop a user interface that uses voice commands to complement PC operation. For example, opening a menu or launching a specific application by voice. This will enable intuitive PC operation by combining voice commands with head and neck movements, which is expected to improve work efficiency.

[0048] Smartglasses can be linked with VR systems to enable operation in virtual space. We are developing a system that enables operation in virtual space by linking smartglasses with VR systems. For example, by linking smartglasses with VR systems, cursor operation and object selection in virtual space can be performed using the smartglasses. Furthermore, by linking smartglasses with VR systems, operation in virtual space can be performed in real time using the smartglasses. For example, the viewpoint in virtual space can be moved by moving the head or neck, and objects can be selected with the line of sight. Furthermore, we are developing a platform for linking smartglasses with VR systems to integrate and analyze data. For example, operation data in virtual space can be collected and the user's operation patterns analyzed. By linking smartglasses with VR systems, operation in virtual space becomes possible, and more intuitive operation can be expected.

[0049] By linking smart glasses with smart home devices, it will be possible to control devices in the home with head and neck movements. We will develop a system that links smart glasses with smart home devices, allowing users to control devices in the home with head and neck movements. For example, they can turn lights on and off or adjust the temperature by moving their head. Furthermore, by linking smart glasses with smart home devices, they can control devices in the home in real time. For example, they can change the TV channel or adjust the volume by moving their neck. Furthermore, we will develop a platform for linking smart glasses with smart home devices, and integrate and analyze data. For example, they will collect device operation data in the home and analyze user operation patterns. By linking smart glasses with smart home devices, users will be able to control devices in the home with head and neck movements, improving convenience.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The data collection unit collects the user's health data. For example, the data collection unit uses sensors built into the smart glasses to collect data such as heart rate, body temperature, and step count. The data collection unit can also acquire data from smartphones and other wearable devices. For example, the data collection unit acquires heart rate data from a smartwatch and step count data from a smartphone. The data collection unit can also collect environmental data (temperature, humidity, weather, etc.). For example, the data collection unit measures temperature and humidity using a weather sensor to acquire weather data. In the analysis unit, the generation AI analyzes the health data collected by the data collection unit. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM) to detect abnormalities in health status. The generation AI can also integrate and analyze multiple data sources using multimodal generation AI. The generation AI can also analyze data trends and predict future health risks. For example, the generation AI analyzes fluctuations in heart rate data to detect abnormal patterns. The multimodal generative AI integrates and analyzes heart rate data and body temperature data to more accurately assess a user's health condition. The generative AI predicts future health risks based on past data and suggests preventive measures. The notification unit notifies the user of the results of the analysis performed by the analysis unit. For example, the notification unit may send a notification to a smartphone to notify the user of an abnormality in their health condition. The notification unit may also notify the user using an audio alert. The notification unit may also display a notification on the smartglasses display. For example, the notification unit may send a notification through a smartphone app to notify the user of an abnormality. The audio alert notifies the user by playing an audio message. The smartglasses display may overlay a notification on the user's field of view to notify the user of an abnormality. This allows the health management system according to the embodiment to comprehensively manage and improve a user's health condition and lifestyle habits. For example, the system collects a user's health data, analyzes it, and if an abnormality is detected, the system may send a notification to a smartphone. An audio alert may also be used to notify the user of an abnormality.Notifications can also be displayed on the smart glasses display to notify the user of any abnormalities.

[0052] The data collection unit uses a camera built into the smart glasses to track the user's gaze and evaluate the user's concentration level in real time based on their gaze movements. For example, if the gaze remains within a certain range, it is determined that the user is concentrating, and if the gaze moves frequently, it is determined that the user's concentration is declining. The data collection unit also evaluates the user's concentration level based on the gaze tracking data and provides appropriate feedback if the user's concentration is declining. For example, if it determines that the user's concentration is declining, it notifies the user to take a break. The data collection unit also collects gaze tracking data over a long period of time and analyzes fluctuations in the user's concentration level. For example, if the user's concentration tends to decline during certain times of the day or under certain environmental conditions, it suggests that the user avoid those times of day or environmental conditions. This allows the system to evaluate the user's concentration level in real time and provide appropriate feedback, which is expected to improve the user's concentration.

[0053] The data collection unit, for example, has a temperature sensor built into the smart glasses and monitors the user's body temperature in real time. For example, if the body temperature rises, it suggests increasing the air conditioning, and if the body temperature drops, it suggests increasing the heating. The data collection unit also uses the temperature sensor to detect changes in the user's body temperature and suggests appropriate adjustments to the heating and cooling. For example, if the body temperature exceeds a certain range, it notifies the user to change the heating and cooling settings. The data collection unit also collects body temperature data over a long period of time and analyzes the user's body temperature fluctuation patterns. For example, if body temperature tends to fluctuate depending on specific time periods or activity levels, it suggests adjusting the heating and cooling settings to suit those time periods and activity levels. In this way, the system detects changes in the user's body temperature and suggests appropriate adjustments to the heating and cooling settings, providing a comfortable environment.

[0054] For example, the data collection unit adds AR functionality to smart glasses and overlays health information on the user's field of view. For example, it displays data such as heart rate, step count, and body temperature in real time. The data collection unit also uses AR functionality to overlay notifications on the user's field of view. For example, it displays emergency alerts, reminders, and messages in the field of view. The data collection unit also develops an interface for overlaying health information and notifications, allowing the user to intuitively check the information within their field of view. For example, it allows the user to select and operate information with eye movements. This allows the health information and notifications to be overlaid on the user's field of view, allowing the user to intuitively check the information.

[0055] The data collection unit, for example, links the smart glasses with a smart watch to realize comprehensive health management. For example, heart rate and step count data obtained from the smart watch can be displayed on the smart glasses to grasp the overall health condition. The data collection unit also links the smart glasses with a smart ring to realize comprehensive health management. For example, body temperature and sleep data obtained from the smart ring can be displayed on the smart glasses to grasp the overall health condition. The data collection unit also develops a platform for linking the smart glasses with other wearable devices and integrates and analyzes the data. For example, data obtained from multiple devices can be centrally managed to realize comprehensive health management. This makes it possible to link the smart glasses with other wearable devices to realize comprehensive health management.

[0056] Combining gyro sensors and accelerometers, for example, those built into smart glasses, can detect a user's subtle movements in real time. For example, they can detect subtle movements of the head and neck and analyze changes in posture with high precision. Combining gyro sensors and accelerometers can also record and analyze a user's posture in detail. For example, they can detect subtle changes in posture while working at a desk and notify the user to maintain appropriate posture. Furthermore, an algorithm for detecting subtle movements can be developed, and data from the gyro sensors and accelerometers can be integrated and analyzed. For example, changes in posture can be detected with high precision from subtle movements and feedback provided to improve posture. As a result, combining gyro sensors and accelerometers enables more accurate posture analysis.

[0057] Posture data is collected over a long period of time, for example, using a gyro sensor built into smart glasses to collect user posture data over a long period of time. For example, posture data is recorded over a period of several weeks to several months, and changes in posture are analyzed. The posture data collected over a long period of time is also analyzed to understand changes in the user's posture. For example, trends in posture changes depending on specific time periods or activity status are identified. Furthermore, based on the results of the posture data analysis, an individually optimized posture improvement program is proposed. For example, stretches and exercises are proposed in accordance with the user's posture changes, aiming to improve posture. In this way, by collecting and analyzing posture data over a long period of time, an individually optimized posture improvement program can be proposed.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The data collection unit collects the user's health data. For example, the data collection unit uses sensors built into the smart glasses to collect data such as heart rate, body temperature, and number of steps. The data collection unit can also acquire data from smartphones and other wearable devices. For example, the data collection unit acquires heart rate data from a smartwatch and number of steps from a smartphone. The data collection unit can also collect environmental data (temperature, humidity, weather, etc.). For example, the data collection unit measures temperature and humidity using a weather sensor to acquire weather data. Step 2: In the analysis unit, the generation AI analyzes the health data collected by the data collection unit. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the data and detect abnormalities in health status. The generation AI may also use a multimodal generation AI to integrate and analyze multiple data sources. The generation AI may also analyze data trends and predict future health risks. For example, the generation AI may analyze fluctuations in heart rate data and detect abnormal patterns. The multimodal generation AI may integrate and analyze heart rate data and body temperature data to more accurately assess health status. The generation AI may then predict future health risks based on past data and suggest preventive measures. Step 3: The notification unit notifies the user of the results of the analysis by the analysis unit. For example, the notification unit may send a notification to a smartphone to notify the user of an abnormality in their health condition. The notification unit may also notify the user using an audio alert. The notification unit may also display the notification on the display of the smart glasses. For example, the notification unit may send a notification through a smartphone app to notify the user of an abnormality. The audio alert notifies the user by playing an audio message. The display of the smart glasses overlays the notification on the user's field of vision to notify the user of an abnormality.

[0060] (Example 2) The health management system according to an embodiment of the present invention collects data on issues and actual conditions related to sleep, health, and posture, and uses generative AI to analyze and visualize them. This system suggests ways to improve mental stability, concentration, and lifestyle habits. This allows the health management system to comprehensively manage and improve the user's health condition and lifestyle habits.

[0061] A health management system according to an embodiment includes a data collection unit, an analysis unit, and a notification unit. The data collection unit collects health data from a user. For example, the data collection unit uses sensors built into smart glasses to collect data such as heart rate, body temperature, and step count. The data collection unit can also acquire data from smartphones and other wearable devices. For example, the data collection unit acquires heart rate data from a smartwatch and step count data from a smartphone. The data collection unit can also collect environmental data (temperature, humidity, weather, etc.). For example, the data collection unit measures temperature and humidity using a weather sensor to acquire weather data. The analysis unit uses a generation AI to analyze the health data collected by the data collection unit. For example, the generation AI may analyze data using a text generation AI (e.g., LLM) to detect abnormalities in health status. The generation AI may also integrate and analyze multiple data sources using a multimodal generation AI. The generation AI may also analyze data trends and predict future health risks. For example, the generation AI may analyze fluctuations in heart rate data to detect abnormal patterns. The multimodal generative AI integrates and analyzes heart rate data and body temperature data to more accurately assess health status. The generative AI predicts future health risks based on past data and suggests preventive measures. The notification unit notifies the user of the results of the analysis by the analysis unit. For example, the notification unit may send a notification to a smartphone to notify the user of an abnormality in their health status. The notification unit may also notify the user using an audio alert. The notification unit may also display a notification on the smartglasses display. For example, the notification unit may send a notification through a smartphone app to notify the user of an abnormality. The audio alert notifies the user by playing an audio message. The smartglasses display may overlay a notification on the user's field of view to notify the user of an abnormality. This allows the health management system according to the embodiment to comprehensively manage and improve the user's health status and lifestyle habits. For example, the system collects the user's health data, analyzes it with the generative AI, and, if an abnormality is detected, sends a notification to the smartphone.Audio alerts can be used to alert the user to any abnormalities, and notifications can also be displayed on the smart glasses display to alert the user to any abnormalities.

[0062] The data collection unit can use a camera built into the smart glasses to track the user's gaze, evaluate the user's concentration level in real time based on the gaze movement, and provide appropriate feedback. The data collection unit, for example, uses a camera built into the smart glasses to track the user's gaze and evaluate the user's concentration level in real time based on the gaze movement. For example, if the gaze remains within a certain range, it is determined that the user is concentrating, and if the gaze moves frequently, it is determined that the user's concentration is declining. The data collection unit also evaluates the user's concentration level based on the gaze tracking data and provides appropriate feedback if the user's concentration is declining. For example, if it determines that the user's concentration is declining, it notifies the user to take a break. The data collection unit also collects gaze tracking data over a long period of time and analyzes fluctuations in the user's concentration level. For example, if the user's concentration tends to decline during certain time periods or environmental conditions, it suggests that the user avoid those time periods or environmental conditions. This allows the user's concentration level to be evaluated in real time and provides appropriate feedback, which is expected to improve the user's concentration.

[0063] The data collection unit can detect changes in the user's body temperature using a temperature sensor built into the smart glasses and suggest adjustments to heating and cooling. For example, the data collection unit has a temperature sensor built into the smart glasses and monitors the user's body temperature in real time. For example, if the body temperature rises, it suggests increasing the air conditioning, and if the body temperature drops, it suggests increasing the heating. The data collection unit also uses the temperature sensor to detect changes in the user's body temperature and suggests appropriate adjustments to heating and cooling. For example, if the body temperature exceeds a certain range, it notifies the user to change the heating and cooling settings. The data collection unit also collects body temperature data over a long period of time and analyzes the user's body temperature fluctuation pattern. For example, if body temperature tends to fluctuate depending on a specific time of day or activity status, it suggests adjusting the heating and cooling settings to suit that time of day or activity status. In this way, the system detects changes in the user's body temperature and suggests appropriate adjustments to heating and cooling, providing a comfortable environment.

[0064] The data collection unit can use the emotion estimation function to estimate the user's emotion from their facial expression and automatically play relaxing music or videos if they are feeling stressed. The data collection unit, for example, uses a camera built into the smart glasses to analyze the user's facial expression and estimate their emotion. For example, if the user's brow is furrowed, it can determine that the user is feeling stressed and automatically play relaxing music. The data collection unit also uses the emotion estimation function to estimate the user's emotion from their facial expression in real time and automatically play relaxing videos if the user is feeling stressed. For example, it can display natural landscapes or soothing videos. The data collection unit also collects facial expression data over a long period of time and analyzes the user's emotional fluctuation patterns. For example, if the user tends to feel stressed during certain times of the day or in certain situations, it can suggest that the user avoid those times or situations. In this way, stress reduction can be expected by estimating the user's emotion and automatically playing relaxing music or videos if the user is feeling stressed.

[0065] The data collection unit adds AR functionality to the smart glasses, enabling it to overlay health information and notifications on the user's field of view. The data collection unit, for example, adds AR functionality to the smart glasses and overlays health information on the user's field of view. For example, it displays data such as heart rate, step count, and body temperature in real time. The data collection unit also uses the AR functionality to overlay notifications on the user's field of view. For example, it displays emergency alerts, reminders, and messages on the user's field of view. The data collection unit also develops an interface for overlaying health information and notifications, allowing the user to intuitively check the information within their field of view. For example, it allows the user to select and operate information with eye movements. This allows the user to intuitively check the information by overlaying health information and notifications on the user's field of view.

[0066] The data collection unit can link the smart glasses with other wearable devices to realize comprehensive health management. The data collection unit, for example, links the smart glasses with a smart watch to realize comprehensive health management. For example, heart rate and step count data obtained from the smart watch can be displayed on the smart glasses to grasp the overall health condition. The data collection unit also links the smart glasses with a smart ring to realize comprehensive health management. For example, body temperature and sleep data obtained from the smart ring can be displayed on the smart glasses to grasp the overall health condition. The data collection unit also develops a platform for linking the smart glasses with other wearable devices and integrates and analyzes the data. For example, data obtained from multiple devices can be centrally managed to realize comprehensive health management. This makes it possible to link the smart glasses with other wearable devices to realize comprehensive health management.

[0067] The data collection unit can use the emotion estimation function to analyze the emotions of the user when wearing the smart glasses in real time and suggest environmental settings that will elicit positive emotions. The data collection unit, for example, uses a camera built into the smart glasses to analyze the user's facial expressions and analyze the emotions in real time. For example, the data collection unit estimates emotions from the facial expressions when wearing the smart glasses and suggests environmental settings that will elicit positive emotions. The data collection unit also uses the emotion estimation function to analyze the user's emotions in real time and suggest music or videos that will elicit positive emotions. For example, music or videos that are relaxing to the user are automatically played. The data collection unit also collects facial expression data over a long period of time and analyzes the user's emotional fluctuation patterns. For example, environmental settings that are likely to elicit positive emotions under specific time periods or circumstances are suggested. In this way, by analyzing the user's emotions in real time and suggesting environmental settings that will elicit positive emotions, the user's mental stability can be expected.

[0068] Combining a gyro sensor and an accelerometer can detect a user's subtle movements and perform posture analysis. Combining a gyro sensor and an accelerometer, for example, using a gyro sensor and an accelerometer built into smart glasses, can detect a user's subtle movements in real time. For example, it can detect subtle movements of the head and neck and analyze posture changes with high accuracy. Combining a gyro sensor and an accelerometer can also record and analyze a user's posture in detail. For example, it can detect subtle changes in posture while working at a desk and notify the user to maintain appropriate posture. Furthermore, an algorithm for detecting subtle movements can be developed, and data from the gyro sensor and accelerometer can be integrated and analyzed. For example, it can detect posture changes from subtle movements with high accuracy and provide feedback to improve posture. Combining a gyro sensor and an accelerometer enables more accurate posture analysis.

[0069] Posture data can be collected over a long period of time, changes in the user's posture can be analyzed, and an individually optimized posture improvement program can be proposed. Posture data can be collected over a long period of time, for example, using a gyro sensor built into smart glasses. For example, posture data can be recorded over several weeks to several months, and changes in posture can be analyzed. The posture data collected over a long period of time can be analyzed to understand changes in the user's posture. For example, trends in posture changes depending on specific time periods or activity status can be identified. Furthermore, an individually optimized posture improvement program can be proposed based on the results of the posture data analysis. For example, stretches and exercises can be suggested in accordance with changes in the user's posture to improve posture. In this way, by collecting and analyzing posture data over a long period of time, an individually optimized posture improvement program can be proposed.

[0070] The emotion estimation function can be used to analyze emotional fluctuations associated with changes in posture and provide advice for maintaining an emotionally stable posture. For example, the emotion estimation function can be combined with a gyro sensor built into smart glasses to analyze emotional fluctuations associated with changes in posture in real time. For example, if poor posture increases stress, a notification can be sent to correct posture. Furthermore, posture data and emotion data can be integrated to analyze emotional fluctuations associated with changes in posture. For example, if a particular posture is emotionally stable, advice can be given to maintain that posture. Furthermore, the emotion estimation function can be used to collect and analyze emotional fluctuations associated with changes in posture over a long period of time. For example, an emotionally stable posture can be identified and specific advice can be provided for maintaining that posture. This analysis of emotional fluctuations associated with changes in posture and advice for maintaining an emotionally stable posture can be provided, which is expected to contribute to the user's mental stability.

[0071] By adding a vibration feedback function to smart glasses, it is possible to notify users by vibration if their posture becomes poor. Smart glasses are equipped with a built-in vibration feedback function, which can notify users by vibration if their posture becomes poor. For example, if their posture becomes poor during long periods of desk work, the glasses can vibrate to warn them. The vibration feedback function can also be used to notify users in real time if their posture becomes poor. For example, a vibration can be used to notify them if their back becomes rounded or their neck is tilted forward. The vibration feedback function can also be used to provide training programs for improving posture. For example, when performing exercises to maintain good posture, the glasses can vibrate to notify users of the appropriate timing. This is expected to encourage users to correct their posture by notifying them by vibration if their posture becomes poor.

[0072] By linking smart glasses with fitness apps, it is possible to provide training programs based on posture data. By linking smart glasses with fitness apps, for example, training programs based on posture data can be provided. For example, stretches and exercises to improve posture can be suggested. In addition, by linking smart glasses with fitness apps, posture data can be analyzed in real time to customize training programs. For example, individual training menus can be provided based on the user's posture. In addition, a platform for linking smart glasses with fitness apps can be developed to integrate and analyze data. For example, data obtained from multiple devices can be centrally managed to provide a comprehensive training program. This will allow linking smart glasses with fitness apps and providing training programs based on posture data, which is expected to improve users' posture.

[0073] The emotion estimation function can be used to analyze the impact of posture on emotions and suggest posture improvements to elicit positive emotions. Using the emotion estimation function, for example, by combining the gyro sensor built into smart glasses with the emotion estimation function, the impact of posture on emotions can be analyzed in real time. For example, if good posture increases positive emotions, the system can notify the user to maintain that posture. Furthermore, posture data and emotion data can be integrated to analyze the impact of posture on emotions. For example, if a specific posture elicits positive emotions, the system can provide specific advice for maintaining that posture. The emotion estimation function can also be used to collect and analyze the impact of posture on emotions over a long period of time. For example, it can identify emotionally positive postures and suggest training programs to maintain those postures. This analysis of the impact of posture on emotions and suggest posture improvements to elicit positive emotions can be expected to improve the user's mental stability.

[0074] It is possible to build a system that collects health data in real time, and uses AI to instantly detect abnormalities and notify the user. Health data is collected in real time, for example, using sensors built into smart glasses to collect health data such as heart rate, body temperature, and number of steps in real time. For example, if the heart rate is abnormally high, the user is immediately notified. In addition, an AI algorithm is developed to analyze health data in real time and detect abnormalities. For example, if body temperature rises suddenly, the abnormality is detected and the user is notified. In addition, a system is built that sends health data collected in real time to the cloud, where AI instantly analyzes and detects abnormalities. For example, if an abnormality is detected, a notification is sent to a smartphone. This allows health data to be collected in real time, and AI instantly detects and notifies the user, enabling early health management.

[0075] Based on the health data, a user's lifestyle habits can be analyzed in detail and an individually optimized health improvement plan can be proposed. Based on the health data, a user's lifestyle data can be collected, for example, using sensors built into smart glasses. For example, data such as the number of steps taken, sleep time, and meal timing can be recorded. AI can then analyze the collected lifestyle data to evaluate the user's health condition. For example, if a user's health condition is deteriorating due to lack of exercise or sleep, an improvement plan can be proposed. A system can also be built that proposes individually optimized health improvement plans based on the lifestyle data. For example, appropriate exercise and dietary advice can be provided based on the user's data. This allows for a detailed analysis of lifestyle habits based on the health data and proposes individually optimized health improvement plans, which is expected to improve the user's health condition.

[0076] The emotion estimation function can be used to analyze the relationship between health data and emotions and provide advice for maintaining an emotionally stable state of health. For example, by combining the emotion estimation function with sensors built into smart glasses, the relationship between health data and emotions can be analyzed in real time. For example, if a person's heart rate is high and they are feeling stressed, advice to relax can be provided. Furthermore, a system can be built that integrates health data and emotional data to provide advice for maintaining an emotionally stable state of health. For example, if a person's body temperature is low and positive emotions are intensified, advice for maintaining that state can be provided. The emotion estimation function can also be used to collect and analyze the relationship between health data and emotions over a long period of time. For example, specific advice can be provided for maintaining an emotionally stable state of health. This analysis of the relationship between health data and emotions and the provision of advice for maintaining an emotionally stable state of health can be expected to improve the user's mental and physical health.

[0077] Health data can be stored in the cloud, enabling remote diagnosis in collaboration with medical institutions. Health data can be stored in the cloud and, for example, transmitted to the cloud in real time using sensors built into smart glasses. For example, data such as heart rate, body temperature, and number of steps can be stored in the cloud. A system can also be built to share health data stored in the cloud with medical institutions and enable remote diagnosis. For example, a doctor can check the data on the cloud and notify the user of the diagnosis results. A platform can also be developed to store health data in the cloud and perform remote diagnosis in collaboration with medical institutions. For example, a system can be provided that allows users to consult with medical institutions online. This allows users to manage their health more efficiently by storing health data in the cloud and enabling remote diagnosis in collaboration with medical institutions.

[0078] It can be linked to apps that provide dietary and exercise advice to users based on health data. Based on health data, for example, sensors built into smart glasses can be used to collect health data and link to apps that provide dietary and exercise advice. For example, an appropriate exercise plan can be suggested based on step count and heart rate data. In addition, a system can be built that links to apps that provide dietary and exercise advice to users based on health data. For example, an appropriate diet plan can be suggested based on body temperature and sleep data. In addition, a platform can be developed to collect health data and link to apps that provide dietary and exercise advice. For example, data obtained from multiple devices can be centrally managed to achieve comprehensive health management. This will allow users to manage their health more effectively by linking to apps that provide dietary and exercise advice based on health data.

[0079] The emotion estimation function can be used to analyze emotional fluctuations based on health data and make health management suggestions to elicit positive emotions. For example, by combining the emotion estimation function with sensors built into smart glasses, emotional fluctuations based on health data can be analyzed in real time. For example, if positive emotions are strengthened when the heart rate is stable, advice can be provided to maintain that state. Furthermore, a system can be built that integrates health data and emotional data to analyze emotional fluctuations. For example, if positive emotions are strengthened when the body temperature is low, specific advice can be provided to maintain that state. Furthermore, the emotion estimation function can be used to collect and analyze emotional fluctuations based on health data over a long period of time. For example, specific health management suggestions can be made to maintain an emotionally positive health state. This analysis of emotional fluctuations based on health data and health management suggestions to elicit positive emotions can be expected to improve the user's mental and physical health.

[0080] A camera built into smart glasses can be used to monitor a user's eye movements and detect signs of drowsiness early. Using a camera built into smart glasses, a user's eye movements can be monitored in real time. For example, blinking frequency and eye opening / closing status can be analyzed to detect signs of drowsiness early. Furthermore, an algorithm for monitoring eye movements can be developed to build a system that detects signs of drowsiness early. For example, if the eyes remain closed for a certain period of time, the system will detect drowsiness and notify the user. Furthermore, eye movement data can be collected over a long period of time and analyzed for signs of drowsiness. For example, if a user tends to fall asleep more easily during certain times or situations, the system will suggest avoiding those times or situations. By monitoring a user's eye movements and detecting signs of drowsiness early, it is expected that drowsiness can be prevented and concentration can be maintained.

[0081] By improving the dozing detection algorithm and learning the user's individual dozing patterns, highly accurate detection can be achieved. By improving the dozing detection algorithm, for example, a system can be constructed that learns the user's individual dozing patterns. For example, individual dozing patterns can be identified based on past dozing data. Furthermore, by learning the user's individual dozing patterns, highly accurate dozing detection can be achieved. For example, a tendency for a user to doze off during specific time periods or situations can be learned, and dozing can be detected based on that pattern. Furthermore, by improving the dozing detection algorithm and learning the user's individual dozing patterns, a system can be developed that learns individual dozing patterns in real time. For example, if the user's dozing pattern changes, the algorithm automatically adapts. In this way, by improving the dozing detection algorithm and learning the user's individual dozing patterns, highly accurate dozing detection can be achieved.

[0082] The emotion estimation function can be used to analyze emotional fluctuations before and after dozing off and suggest emotional approaches to prevent dozing off. For example, by combining the emotion estimation function with a camera built into smart glasses, emotional fluctuations before and after dozing off can be analyzed in real time. For example, if stress levels increase before dozing off, advice on how to relax can be provided. Furthermore, a system can be constructed that analyzes emotional fluctuations before and after dozing off and suggests emotional approaches to prevent dozing off. For example, music or videos can be suggested to elicit positive emotions before dozing off. Furthermore, the emotion estimation function can be used to collect and analyze emotional fluctuations before and after dozing off over a long period of time. For example, specific advice can be provided on how to maintain an emotionally stable state. This analysis of emotional fluctuations before and after dozing off and suggesting emotional approaches can be expected to prevent dozing off and maintain concentration.

[0083] By linking the drowsiness detection function with an in-vehicle system, it is possible to prevent drowsiness while driving. By linking the drowsiness detection function with an in-vehicle system, for example, a system can be built to prevent drowsiness while driving. For example, if drowsiness is detected while driving, the in-vehicle system will sound an alarm. Furthermore, by linking with the in-vehicle system, drowsiness while driving can be detected in real time. For example, if smart glasses detect drowsiness, the in-vehicle system will vibrate to alert the driver. Furthermore, a platform for linking the drowsiness detection function with the in-vehicle system will be developed to integrate and analyze data. For example, data on drowsiness while driving can be sent to the in-vehicle system, which will issue a warning in real time. By linking the drowsiness detection function with the in-vehicle system, it is expected that drowsiness while driving can be prevented and safer driving can be supported.

[0084] Drowsiness detection can be introduced into office environments and used as a tool to maintain employee concentration. For example, if a system detects employees dozing off while working at their desks, it can vibrate to notify them. Furthermore, by introducing this system into office environments, a system can be built to monitor employees' concentration in real time. For example, if dozing is detected, it can suggest a break to restore concentration. Furthermore, a platform for introducing this system into office environments can be developed to integrate and analyze data. For example, data on employees' dozing can be collected and specific advice provided on how to maintain concentration. Introducing this system into office environments is expected to maintain employee concentration and improve work efficiency.

[0085] The emotion estimation function can be used to identify the emotional factors that cause drowsiness and make suggestions to resolve them. For example, by combining the emotion estimation function with a camera built into smart glasses, the emotional factors that cause drowsiness can be identified in real time. For example, if drowsiness is caused by stress or fatigue, advice can be provided to resolve the cause. We also build a system that identifies the emotional factors that cause drowsiness and makes specific suggestions to resolve them. For example, it can suggest relaxing music or videos. Furthermore, the emotion estimation function can be used to collect and analyze the emotional factors that cause drowsiness over a long period of time. For example, if stress increases during a particular time period or situation, it can suggest avoiding that time period or situation. This can be expected to prevent drowsiness and maintain concentration by identifying the emotional factors that cause drowsiness and making suggestions to resolve them.

[0086] The eye-tracking function of smart glasses can be used to develop a system that controls a cursor with eye movements. Using the eye-tracking function of smart glasses, for example, a system can be developed that controls a cursor with eye movements. For example, an eye-tracking function can be implemented using a camera built into smart glasses to develop a system that controls a cursor with eye movements. For example, the cursor can be moved by moving the gaze, and a click can be performed by fixing the gaze. In addition, eye-tracking data can be analyzed in real time to develop an algorithm that controls the cursor according to eye movements. For example, the speed and direction of the cursor can be adjusted according to eye movements. In addition, the eye-tracking function can be used to develop a user interface that controls the cursor with eye movements. For example, menus can be selected or scrolling can be performed according to eye movements. This makes it possible to operate a PC without using your hands by using the eye-tracking function to control the cursor with eye movements, which is expected to improve work efficiency.

[0087] Intuitive PC operation can be achieved by combining voice commands with head and neck movements. We will develop a system that uses voice commands in addition to head and neck movements. For example, we will develop a system that recognizes voice commands using a microphone built into smart glasses and uses head and neck movements in combination to operate a PC. For example, operations can be performed by giving voice commands such as "click" or "scroll." We will also develop an algorithm that combines voice commands with head and neck movements to achieve intuitive PC operation. For example, moving the cursor by moving your head to the right while giving a voice command such as "move right." We will also develop a user interface that uses voice commands to complement PC operation. For example, opening a menu or launching a specific application by voice. This will enable intuitive PC operation by combining voice commands with head and neck movements, which is expected to improve work efficiency.

[0088] The emotion estimation function can be used to suggest operation methods based on the user's emotional state, reducing stress. For example, by combining the emotion estimation function with a camera built into smart glasses, the emotion estimation function can analyze the user's emotional state in real time. For example, if the user is feeling stressed, a simplified operation method can be suggested. Furthermore, a system can be built using the emotion estimation function to suggest operation methods based on the user's emotional state. For example, if the user is relaxed, more complex operations can be suggested, and if the user is feeling stressed, simpler operations can be suggested. Furthermore, based on the emotion estimation data, operation methods based on the user's emotional state can be collected and analyzed over a long period of time. For example, if a specific operation has the effect of reducing stress, that operation method can be suggested preferentially. This suggests operation methods based on the user's emotional state using the emotion estimation function, which is expected to reduce stress and improve work efficiency.

[0089] Smartglasses can be linked with VR systems to enable operation in virtual space. We are developing a system that enables operation in virtual space by linking smartglasses with VR systems. For example, by linking smartglasses with VR systems, cursor operation and object selection in virtual space can be performed using the smartglasses. Furthermore, by linking smartglasses with VR systems, operation in virtual space can be performed in real time using the smartglasses. For example, the viewpoint in virtual space can be moved by moving the head or neck, and objects can be selected with the line of sight. Furthermore, we are developing a platform for linking smartglasses with VR systems to integrate and analyze data. For example, operation data in virtual space can be collected and the user's operation patterns analyzed. By linking smartglasses with VR systems, operation in virtual space becomes possible, and more intuitive operation can be expected.

[0090] By linking smart glasses with smart home devices, it will be possible to control devices in the home with head and neck movements. We will develop a system that links smart glasses with smart home devices, allowing users to control devices in the home with head and neck movements. For example, they can turn lights on and off or adjust the temperature by moving their head. Furthermore, by linking smart glasses with smart home devices, they can control devices in the home in real time. For example, they can change the TV channel or adjust the volume by moving their neck. Furthermore, we will develop a platform for linking smart glasses with smart home devices, and integrate and analyze data. For example, they will collect device operation data in the home and analyze user operation patterns. By linking smart glasses with smart home devices, users will be able to control devices in the home with head and neck movements, improving convenience.

[0091] Using emotion estimation, PC operations can be customized according to the user's emotional state, improving work efficiency. For example, by combining emotion estimation with a camera built into smart glasses, the emotion estimation function can analyze the user's emotional state in real time. For example, if the user is feeling stressed, the emotion estimation function can be used to suggest simplified operation methods. Furthermore, using the emotion estimation function, a system can be built that customizes PC operations according to the user's emotional state. For example, if the user is relaxed, more complex operations can be suggested, and if the user is feeling stressed, simpler operations can be suggested. Furthermore, based on emotion estimation data, PC operation customization according to the user's emotional state can be collected and analyzed over a long period of time. For example, if a specific operation has the effect of reducing stress, that operation method can be suggested preferentially. This is expected to improve work efficiency by customizing PC operations according to the user's emotional state using the emotion estimation function.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The data collection unit collects the user's health data. For example, the data collection unit uses sensors built into the smart glasses to collect data such as heart rate, body temperature, and step count. The data collection unit can also acquire data from smartphones and other wearable devices. For example, the data collection unit acquires heart rate data from a smartwatch and step count data from a smartphone. The data collection unit can also collect environmental data (temperature, humidity, weather, etc.). For example, the data collection unit measures temperature and humidity using a weather sensor to acquire weather data. In the analysis unit, the generation AI analyzes the health data collected by the data collection unit. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM) to detect abnormalities in health status. The generation AI can also integrate and analyze multiple data sources using multimodal generation AI. The generation AI can also analyze data trends and predict future health risks. For example, the generation AI analyzes fluctuations in heart rate data to detect abnormal patterns. The multimodal generative AI integrates and analyzes heart rate data and body temperature data to more accurately assess a user's health condition. The generative AI predicts future health risks based on past data and suggests preventive measures. The notification unit notifies the user of the results of the analysis performed by the analysis unit. For example, the notification unit may send a notification to a smartphone to notify the user of an abnormality in their health condition. The notification unit may also notify the user using an audio alert. The notification unit may also display a notification on the smartglasses display. For example, the notification unit may send a notification through a smartphone app to notify the user of an abnormality. The audio alert notifies the user by playing an audio message. The smartglasses display may overlay a notification on the user's field of view to notify the user of an abnormality. This allows the health management system according to the embodiment to comprehensively manage and improve a user's health condition and lifestyle habits. For example, the system collects a user's health data, analyzes it, and if an abnormality is detected, the system may send a notification to a smartphone. An audio alert may also be used to notify the user of an abnormality.Notifications can also be displayed on the smart glasses display to notify the user of any abnormalities.

[0094] The data collection unit uses a camera built into the smart glasses to track the user's gaze and evaluate the user's concentration level in real time based on their gaze movements. For example, if the gaze remains within a certain range, it is determined that the user is concentrating, and if the gaze moves frequently, it is determined that the user's concentration is declining. The data collection unit also evaluates the user's concentration level based on the gaze tracking data and provides appropriate feedback if the user's concentration is declining. For example, if it determines that the user's concentration is declining, it notifies the user to take a break. The data collection unit also collects gaze tracking data over a long period of time and analyzes fluctuations in the user's concentration level. For example, if the user's concentration tends to decline during certain times of the day or under certain environmental conditions, it suggests that the user avoid those times of day or environmental conditions. This allows the system to evaluate the user's concentration level in real time and provide appropriate feedback, which is expected to improve the user's concentration.

[0095] The data collection unit, for example, has a temperature sensor built into the smart glasses and monitors the user's body temperature in real time. For example, if the body temperature rises, it suggests increasing the air conditioning, and if the body temperature drops, it suggests increasing the heating. The data collection unit also uses the temperature sensor to detect changes in the user's body temperature and suggests appropriate adjustments to the heating and cooling. For example, if the body temperature exceeds a certain range, it notifies the user to change the heating and cooling settings. The data collection unit also collects body temperature data over a long period of time and analyzes the user's body temperature fluctuation patterns. For example, if body temperature tends to fluctuate depending on specific time periods or activity levels, it suggests adjusting the heating and cooling settings to suit those time periods and activity levels. In this way, the system detects changes in the user's body temperature and suggests appropriate adjustments to the heating and cooling settings, providing a comfortable environment.

[0096] The data collection unit, for example, uses a camera built into the smart glasses to analyze the user's facial expressions and infer their emotions. For example, if the user's brow is furrowed, it may determine that the user is feeling stressed and automatically play relaxing music. The data collection unit also uses an emotion estimation function to infer emotions from the user's facial expressions in real time, and automatically plays relaxing videos if the user is feeling stressed. For example, it may display natural landscapes or soothing videos. The data collection unit also collects facial expression data over a long period of time and analyzes the user's emotional fluctuation patterns. For example, if the user tends to feel stressed during certain times of the day or in certain situations, it may suggest avoiding those times or situations. This allows the system to infer the user's emotions and automatically play relaxing music or videos if the user is feeling stressed, which is expected to reduce stress.

[0097] For example, the data collection unit adds AR functionality to smart glasses and overlays health information on the user's field of view. For example, it displays data such as heart rate, step count, and body temperature in real time. The data collection unit also uses AR functionality to overlay notifications on the user's field of view. For example, it displays emergency alerts, reminders, and messages in the field of view. The data collection unit also develops an interface for overlaying health information and notifications, allowing the user to intuitively check the information within their field of view. For example, it allows the user to select and operate information with eye movements. This allows the health information and notifications to be overlaid on the user's field of view, allowing the user to intuitively check the information.

[0098] The data collection unit, for example, links the smart glasses with a smart watch to realize comprehensive health management. For example, heart rate and step count data obtained from the smart watch can be displayed on the smart glasses to grasp the overall health condition. The data collection unit also links the smart glasses with a smart ring to realize comprehensive health management. For example, body temperature and sleep data obtained from the smart ring can be displayed on the smart glasses to grasp the overall health condition. The data collection unit also develops a platform for linking the smart glasses with other wearable devices and integrates and analyzes the data. For example, data obtained from multiple devices can be centrally managed to realize comprehensive health management. This makes it possible to link the smart glasses with other wearable devices to realize comprehensive health management.

[0099] The data collection unit, for example, uses a camera built into the smart glasses to analyze the user's facial expressions and analyze their emotions in real time. For example, it estimates emotions from facial expressions when wearing the smart glasses and suggests environmental settings that will elicit positive emotions. The data collection unit also uses the emotion estimation function to analyze the user's emotions in real time and suggest music or videos that will elicit positive emotions. For example, it automatically plays music or videos that will relax the user. The data collection unit also collects facial expression data over a long period of time and analyzes the user's emotional fluctuation patterns. For example, it suggests environmental settings that are more likely to elicit positive emotions under specific time periods or circumstances. In this way, by analyzing the user's emotions in real time and suggesting environmental settings that will elicit positive emotions, it is possible to expect the user's mental stability.

[0100] Combining gyro sensors and accelerometers, for example, those built into smart glasses, can detect a user's subtle movements in real time. For example, they can detect subtle movements of the head and neck and analyze changes in posture with high precision. Combining gyro sensors and accelerometers can also record and analyze a user's posture in detail. For example, they can detect subtle changes in posture while working at a desk and notify the user to maintain appropriate posture. Furthermore, an algorithm for detecting subtle movements can be developed, and data from the gyro sensors and accelerometers can be integrated and analyzed. For example, changes in posture can be detected with high precision from subtle movements and feedback provided to improve posture. As a result, combining gyro sensors and accelerometers enables more accurate posture analysis.

[0101] Posture data is collected over a long period of time, for example, using a gyro sensor built into smart glasses to collect user posture data over a long period of time. For example, posture data is recorded over a period of several weeks to several months, and changes in posture are analyzed. The posture data collected over a long period of time is also analyzed to understand changes in the user's posture. For example, trends in posture changes depending on specific time periods or activity status are identified. Furthermore, based on the results of the posture data analysis, an individually optimized posture improvement program is proposed. For example, stretches and exercises are proposed in accordance with the user's posture changes, aiming to improve posture. In this way, by collecting and analyzing posture data over a long period of time, an individually optimized posture improvement program can be proposed.

[0102] Using the emotion estimation function, for example, the gyro sensor built into smart glasses can be combined with the emotion estimation function to analyze emotional fluctuations associated with changes in posture in real time. For example, if poor posture increases stress, the system can notify the user to correct their posture. Furthermore, posture data and emotion data can be integrated to analyze emotional fluctuations associated with changes in posture. For example, if a particular posture is emotionally stable, the system can advise the user to maintain that posture. Furthermore, the emotion estimation function can be used to collect and analyze emotional fluctuations associated with changes in posture over a long period of time. For example, it can identify an emotionally stable posture and provide specific advice for maintaining that posture. This analysis of emotional fluctuations associated with changes in posture and the provision of advice for maintaining an emotionally stable posture are expected to contribute to the user's mental stability.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The data collection unit collects the user's health data. For example, the data collection unit uses sensors built into the smart glasses to collect data such as heart rate, body temperature, and number of steps. The data collection unit can also acquire data from smartphones and other wearable devices. For example, the data collection unit acquires heart rate data from a smartwatch and number of steps from a smartphone. The data collection unit can also collect environmental data (temperature, humidity, weather, etc.). For example, the data collection unit measures temperature and humidity using a weather sensor to acquire weather data. Step 2: In the analysis unit, the generation AI analyzes the health data collected by the data collection unit. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the data and detect abnormalities in health status. The generation AI may also use a multimodal generation AI to integrate and analyze multiple data sources. The generation AI may also analyze data trends and predict future health risks. For example, the generation AI may analyze fluctuations in heart rate data and detect abnormal patterns. The multimodal generation AI may integrate and analyze heart rate data and body temperature data to more accurately assess health status. The generation AI may then predict future health risks based on past data and suggest preventive measures. Step 3: The notification unit notifies the user of the results of the analysis by the analysis unit. For example, the notification unit may send a notification to a smartphone to notify the user of an abnormality in their health condition. The notification unit may also notify the user using an audio alert. The notification unit may also display the notification on the display of the smart glasses. For example, the notification unit may send a notification through a smartphone app to notify the user of an abnormality. The audio alert notifies the user by playing an audio message. The display of the smart glasses overlays the notification on the user's field of vision to notify the user of an abnormality.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects health data of a user; an analysis unit that analyzes the health data collected by the data collection unit; a notification unit that notifies a user of the results of the analysis performed by the analysis unit. A system characterized by:

2. The data collection unit Using a camera built into the smart glasses, the user's gaze is tracked, and the level of concentration is evaluated in real time based on the gaze movement, providing feedback.

2. The system of claim 1.

3. The data collection unit Using a temperature sensor built into the smart glasses, the system detects changes in the user's body temperature and suggests adjusting the heating and cooling settings.

2. The system of claim 1.

4. The data collection unit The system estimates the user's emotions from their facial expressions and automatically plays relaxing music or videos if the user is feeling stressed.

2. The system of claim 1.

5. The data collection unit The AR function is added to smart glasses, and health information and notifications are overlaid on the user's field of vision.

2. The system of claim 1.

6. The data collection unit Smart glasses will be linked with other wearable devices to achieve comprehensive health management.

2. The system of claim 1.

7. The data collection unit The system analyzes the user's emotions in real time when wearing the smart glasses and suggests environmental settings to elicit positive emotions.

2. The system of claim 1.

8. A gyro sensor and an acceleration sensor are combined to detect the user's subtle movements and perform posture analysis.

2. The system of claim 1.

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