System

Abstract

An object of a system according to an embodiment is to monitor the health condition of a pregnant woman in real time and immediately notify a doctor of an abnormality.SOLUTION: A system includes a wearable device, an AI analysis unit, and a notification unit. The wearable device collects health data. The AI analyzer analyzes the health data collected by the wearable device in real time. The notification unit notifies a doctor when an abnormality is detected by the AI 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] Conventional technology has the problem of making it difficult to monitor the health condition of pregnant women in real time and immediately notify doctors of any abnormalities.

[0005] The system according to the embodiment aims to monitor the health condition of a pregnant woman in real time and immediately notify a doctor of any abnormalities. [Means for solving the problem]

[0006] The system according to the embodiment includes a wearable device, an AI analysis unit, and a notification unit. The wearable device collects health data. The AI ​​analysis unit analyzes the health data collected by the wearable device in real time. The notification unit notifies a doctor when an abnormality is detected by the AI ​​analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the health condition of a pregnant woman in real time and immediately notify a doctor of any abnormalities. [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 monitoring system according to an embodiment of the present invention monitors the health data of a pregnant woman in real time and promptly notifies a doctor when an abnormality is detected. This allows the health monitoring system to constantly monitor the health status of the pregnant woman and respond quickly when an abnormality occurs.

[0029] A health monitoring system according to an embodiment includes a wearable device, an AI analysis unit, and a notification unit. The wearable device collects health data from a pregnant woman. For example, it constantly monitors data such as heart rate, blood pressure, body temperature, and fetal heart rate. The wearable device is lightweight and comfortable to wear, designed to not interfere with the pregnant woman's daily life. For example, a wristwatch-type or belt-type device is conceivable. The AI ​​analysis unit analyzes the health data collected by the wearable device in real time. For example, the AI ​​analysis unit detects abnormalities such as a sudden increase in heart rate or abnormally high blood pressure. The AI ​​analysis unit uses a generative AI (e.g., a text generation AI or a multimodal generation AI) to analyze the pregnant woman's health data and detect abnormalities. The generative AI receives a prompt containing the pregnant woman's health data as input and detects abnormalities based on the prompt. The notification unit notifies a doctor when an abnormality is detected by the AI ​​analysis unit. For example, the notification unit sends a real-time notification to the doctor's smartphone or tablet. The notification content includes specific details of the abnormality. For example, the message may include, "Pregnant woman A's heart rate has suddenly increased. Please check immediately." This allows the health monitoring system according to the embodiment to monitor the health data of pregnant women in real time, and to promptly notify a doctor if an abnormality is detected.

[0030] The wearable device is equipped with a sensor that measures the stress level of a pregnant woman and can suggest relaxation methods when the stress level increases. For example, the wearable device is equipped with a sensor that measures the stress level and suggests relaxation methods when the stress level increases. For example, instructions for breathing exercises or light stretching are displayed on the device's display. The wearable device also adds a function that uses the sensor that measures the stress level to automatically play relaxing music when the stress level of the pregnant woman increases. For example, the device connects to earphones via Bluetooth and plays relaxing music. The wearable device is also equipped with a sensor that measures the stress level and adds a function that automatically sprays a relaxing aroma when the stress level of the pregnant woman increases. For example, the device connects to an aroma diffuser and sprays an aroma with a relaxing effect. In this way, the mental health of pregnant women can be supported by measuring their stress level and suggesting relaxation methods.

[0031] The wearable device is equipped with a voice recognition function, allowing pregnant women to verbally report their health status. For example, a wearable device may be equipped with a voice recognition function, allowing pregnant women to verbally report their health status. For example, they may report "my stomach hurts" or "I'm feeling unwell." The wearable device also adds a function that uses the voice recognition function to automatically record and send to a doctor when a pregnant woman verbally reports her health status. For example, the device may record a report such as "I've had a headache since this morning" and notify the doctor. The wearable device also adds a function that provides appropriate advice when a pregnant woman verbally reports her health status. For example, the device may provide advice such as "drink plenty of fluids." This allows pregnant women to verbally report their health status, enabling more detailed health management.

[0032] Wearable devices are equipped with sensors that monitor fetal movements, allowing them to simultaneously collect health data on both the pregnant woman and the fetus. For example, a wearable device could add a sensor to monitor fetal movements and simultaneously collect health data on both the pregnant woman and the fetus. For example, it could monitor the fetal heart rate and movements in real time. Furthermore, wearable devices could be equipped with sensors that monitor fetal movements and add a function to notify the pregnant woman if an abnormality is detected. For example, an alert could be issued if there is little fetal movement. Furthermore, wearable devices could simultaneously collect health data on both the pregnant woman and the fetus, building a system that integrates and analyzes the data. For example, it could evaluate the impact of stress on the pregnant woman on the fetus. By simultaneously collecting health data on both the pregnant woman and the fetus, more comprehensive health management becomes possible.

[0033] Wearable devices can be worn by the pregnant woman's family and partners, and their health data can be collected and analyzed simultaneously. For example, they can monitor the heart rates and stress levels of all family members. Wearable devices can also collect health data from family members and partners, creating a system for managing the health of the entire household. For example, they can integrate and analyze data from all family members and provide health advice. Wearable devices can also be used by the pregnant woman's family and partners to collect health data from the entire household and add a notification function when an abnormality is detected. For example, an alert can be issued if any family member shows an abnormality. This makes it possible to manage the health of the entire household by collecting and analyzing the health data of the pregnant woman's family and partners.

[0034] When analyzing health data, the AI ​​analysis unit can introduce an algorithm that compares it with past data to detect abnormalities. For example, when analyzing health data, the AI ​​analysis unit introduces an algorithm that compares it with past data to detect abnormalities. For example, it compares past heart rate data with current data to detect abnormalities. The AI ​​analysis unit also develops an algorithm that detects abnormalities based on past health data. For example, it compares past blood pressure data with current data to determine abnormalities. The AI ​​analysis unit also builds a system that compares it with past data to detect abnormalities when analyzing health data. For example, it compares past body temperature data with current data to detect abnormalities. This enables more accurate health management by detecting abnormalities by comparing it with past data.

[0035] When an abnormality is detected, the AI ​​analysis unit can identify the cause of the abnormality and suggest specific countermeasures. For example, the AI ​​analysis unit will add a function that, when an abnormality is detected, identifies the cause of the abnormality and suggests specific countermeasures. For example, if an abnormal heart rate is caused by stress, it will suggest relaxation methods. The AI ​​analysis unit will also build a system that, when an abnormality is detected, identifies the cause of the abnormality and suggests specific countermeasures. For example, if an abnormal blood pressure is caused by diet, it will suggest improving the diet. The AI ​​analysis unit will also develop an algorithm that, when an abnormality is detected, identifies the cause of the abnormality and suggests specific countermeasures. For example, if an abnormal body temperature is caused by lack of exercise, it will suggest exercise. This will enable quick and appropriate responses by identifying the cause of the abnormality and suggesting specific countermeasures.

[0036] The AI ​​analysis unit can include the diet and exercise records of pregnant women in the data to enable comprehensive health management. For example, the AI ​​analysis unit could build a system for comprehensive health management by including the diet and exercise records of pregnant women in the data. For example, it could record the contents of meals and frequency of exercise to comprehensively evaluate the health condition. The AI ​​analysis unit could also add the diet and exercise records to the data to be analyzed to enable comprehensive health management. For example, it could evaluate the nutritional balance of meals and the effectiveness of exercise. The AI ​​analysis unit could also analyze the diet and exercise records of pregnant women to develop algorithms for comprehensive health management. For example, it could provide health advice based on the diet and exercise data. This would enable comprehensive health management that includes the diet and exercise records of pregnant women.

[0037] The AI ​​analysis unit can also share the analysis results with the pregnant woman's family and partner so that they can provide support. For example, the AI ​​analysis unit builds a system that shares the analysis results with the pregnant woman's family and partner so that they can provide support. For example, it sends health status reports to the family. The AI ​​analysis unit also shares the analysis results with the family and partner so that they can support the pregnant woman. For example, it provides health advice to the family. The AI ​​analysis unit also adds a function that shares the analysis results with the pregnant woman's family and partner so that they can provide support. For example, it sends health status notifications to the family. This allows the analysis results to be shared with the pregnant woman's family and partner so that they can provide support.

[0038] The notification unit can send notifications not only to doctors but also to the pregnant woman's family and partner when an abnormality is detected. For example, the notification unit can add a function to send notifications not only to doctors but also to the pregnant woman's family and partner when an abnormality is detected. For example, the notification unit can notify the family of details of the abnormality and request support. The notification unit can also build a system that sends notifications to both doctors and family when an abnormality is detected. For example, it can make an emergency contact with the family and urge them to take action. The notification unit can also add a function to send notifications not only to doctors but also to the pregnant woman's family and partner when an abnormality is detected, allowing them to take action quickly. For example, it can notify the family of details of the abnormality and how to deal with it. This allows for a quick response by sending notifications not only to doctors but also to the pregnant woman's family and partner when an abnormality is detected.

[0039] The notification unit can include emergency measures along with a detailed explanation of the abnormality in the notification content when an abnormality is detected. For example, the notification unit includes emergency measures along with a detailed explanation of the abnormality in the notification content when an abnormality is detected. For example, specific instructions such as, "Your heart rate has suddenly increased. Please take deep breaths" are included. The notification unit also adds a detailed explanation of the abnormality to the notification content, building a system that provides emergency measures. For example, instructions such as, "Your blood pressure is abnormally high. Please lie down immediately." The notification unit also adds a function to include a detailed explanation of the abnormality and emergency measures in the notification when an abnormality is detected. For example, specific measures such as, "Your body temperature has suddenly increased. Please use a cooling sheet" are provided. In this way, by including a detailed explanation and emergency measures in the notification content when an abnormality is detected, quick and appropriate responses are possible.

[0040] The notification unit can send notifications not only to doctors but also to local medical institutions and emergency services when an abnormality is detected. For example, the notification unit adds a function to send notifications not only to doctors but also to local medical institutions and emergency services when an abnormality is detected. For example, the notification unit notifies local hospitals of details of the abnormality and requests an emergency response. The notification unit also builds a system that sends notifications to both doctors and local medical institutions and emergency services when an abnormality is detected. For example, the notification unit makes an emergency contact with an emergency service and urges them to respond. The notification unit also adds a function to send notifications not only to doctors but also to local medical institutions and emergency services when an abnormality is detected, enabling a rapid response. For example, the notification unit notifies local medical institutions of details of the abnormality and how to deal with it. This enables a rapid response by sending notifications not only to doctors but also to local medical institutions and emergency services when an abnormality is detected.

[0041] The notification unit can visually display the notification content when an abnormality is detected as a graph or chart of health data, making it easier to understand intuitively. The notification unit, for example, visually displays the notification content when an abnormality is detected as a graph or chart of health data. For example, heart rate fluctuations may be displayed in a graph, making it easier to understand the abnormality intuitively. The notification unit also adds health data graphs and charts to the notification content, building a system that makes it easier to understand visually. For example, blood pressure fluctuations may be displayed in a chart, making it easier to understand the abnormality intuitively. The notification unit also adds a function to include health data graphs and charts in the notification when an abnormality is detected. For example, body temperature fluctuations may be displayed in a graph, making it easier to understand the abnormality intuitively. In this way, visually displaying the notification content when an abnormality is detected makes it easier to understand intuitively.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The health monitoring system includes a wearable device that collects health data from pregnant women, an AI analysis unit that analyzes the collected data in real time, and a notification unit that notifies doctors when abnormalities are detected. The wearable device constantly monitors data such as heart rate, blood pressure, body temperature, and fetal heart rate. It can be a wristwatch or a belt-type device, for example. The AI ​​analysis unit analyzes the collected health data in real time and detects abnormalities. For example, it detects abnormalities when the heart rate rises suddenly or blood pressure becomes abnormally high. The notification unit sends a real-time notification to the doctor's smartphone or tablet when an abnormality is detected. The notification content includes specific details of the abnormality. For example, it could say, "Pregnant woman A's heart rate has risen sharply. Please check immediately." This allows for real-time monitoring of pregnant women's health data and prompt notification to doctors when an abnormality is detected.

[0044] The wearable device is equipped with a sensor that measures the stress level of pregnant women and can suggest relaxation methods when stress levels rise. For example, instructions for breathing exercises or light stretching can be displayed on the device's display. It is also possible to add a function that automatically plays relaxing music when stress levels rise. For example, the device connects to earphones via Bluetooth and plays relaxing music. It is also possible to add a function that automatically sprays relaxing aromas. For example, the device connects to an aroma diffuser and sprays aromas with a relaxing effect. In this way, it is possible to support the mental health of pregnant women by measuring their stress levels and suggesting relaxation methods.

[0045] Wearable devices are equipped with voice recognition functionality, allowing pregnant women to verbally report their health status. For example, they can report things like "I have a stomachache" or "I'm feeling unwell." It is also possible to add a function that automatically records and sends to a doctor when a pregnant woman verbally reports her health status using the voice recognition function. For example, a report such as "I've had a headache since this morning" can be recorded and notified to the doctor. The voice recognition function can also be added to provide appropriate advice when a pregnant woman verbally reports her health status. For example, advice such as "Drink plenty of fluids" can be provided. This allows pregnant women to verbally report their health status, enabling more detailed health management.

[0046] Wearable devices are equipped with sensors that monitor fetal movements, making it possible to simultaneously collect health data on both the pregnant woman and the fetus. For example, fetal heart rate and movements can be monitored in real time. It is also possible to add a function that notifies the pregnant woman when abnormalities are detected. For example, an alert can be issued if there is little fetal movement. It is also possible to build a system that simultaneously collects health data on both the pregnant woman and the fetus and integrates and analyzes the data. For example, the impact of stress on the pregnant woman on the fetus can be evaluated. By collecting health data on both the pregnant woman and the fetus simultaneously, more comprehensive health management becomes possible.

[0047] Wearable devices can be worn by the pregnant woman's family and partners, and their health data can be collected and analyzed at the same time. For example, the heart rates and stress levels of all family members can be monitored. It is also possible to build a system that collects health data from family members and partners to manage the health of the entire household. For example, the system can integrate and analyze data from all family members and provide health advice. It is also possible to add a function that notifies users when an abnormality is detected. For example, an alert can be issued if any family member shows signs of an abnormality. This makes it possible to manage the health of the entire household by collecting and analyzing the health data of the pregnant woman's family and partners.

[0048] When analyzing health data, the AI ​​analysis unit can introduce algorithms that compare data with past data to detect abnormalities. For example, it can compare past heart rate data with current data to detect abnormalities. It can also develop algorithms to detect abnormalities based on past health data. For example, it can compare past blood pressure data with current data to determine abnormalities. It can also build a system that compares past body temperature data with current data to detect abnormalities. This allows for more accurate health management by detecting abnormalities by comparing data with past data.

[0049] When the AI ​​analysis unit detects an abnormality, it can identify the cause and suggest specific countermeasures. For example, if an abnormal heart rate is caused by stress, it can suggest relaxation methods. Also, if an abnormal blood pressure is caused by diet, it can suggest dietary improvements. For example, if an abnormal body temperature is caused by lack of exercise, it can suggest exercise. This allows for quick and appropriate response by identifying the cause of the abnormality and suggesting specific countermeasures.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The wearable device collects health data from the pregnant woman. For example, it constantly monitors data such as heart rate, blood pressure, body temperature, and fetal heart rate. The wearable device is designed to be lightweight and comfortable to wear, so as not to interfere with the pregnant woman's daily life. For example, a wristwatch-type or belt-type device is conceivable. Step 2: The AI ​​analysis unit analyzes the health data collected by the wearable device in real time. For example, the AI ​​analysis unit detects abnormalities such as a sudden increase in heart rate or abnormally high blood pressure. Generative AI (e.g., text generation AI or multimodal generation AI) is used to analyze the pregnant woman's health data and detect abnormalities. The generative AI receives a prompt containing the pregnant woman's health data as input and detects abnormalities based on the prompt. Step 3: The notification unit notifies the doctor when an abnormality is detected by the AI ​​analysis unit. For example, the notification unit sends a real-time notification to the doctor's smartphone or tablet. The notification content includes specific details of the abnormality. For example, it may include content such as, "Pregnant woman A's heart rate has risen sharply. Please check immediately."

[0052] (Example 2) The health monitoring system according to an embodiment of the present invention monitors the health data of a pregnant woman in real time and promptly notifies a doctor when an abnormality is detected. This allows the health monitoring system to constantly monitor the health status of the pregnant woman and respond quickly when an abnormality occurs.

[0053] A health monitoring system according to an embodiment includes a wearable device, an AI analysis unit, and a notification unit. The wearable device collects health data from a pregnant woman. For example, it constantly monitors data such as heart rate, blood pressure, body temperature, and fetal heart rate. The wearable device is lightweight and comfortable to wear, designed to not interfere with the pregnant woman's daily life. For example, a wristwatch-type or belt-type device is conceivable. The AI ​​analysis unit analyzes the health data collected by the wearable device in real time. For example, the AI ​​analysis unit detects abnormalities such as a sudden increase in heart rate or abnormally high blood pressure. The AI ​​analysis unit uses a generative AI (e.g., a text generation AI or a multimodal generation AI) to analyze the pregnant woman's health data and detect abnormalities. The generative AI receives a prompt containing the pregnant woman's health data as input and detects abnormalities based on the prompt. The notification unit notifies a doctor when an abnormality is detected by the AI ​​analysis unit. For example, the notification unit sends a real-time notification to the doctor's smartphone or tablet. The notification content includes specific details of the abnormality. For example, the message may include, "Pregnant woman A's heart rate has suddenly increased. Please check immediately." This allows the health monitoring system according to the embodiment to monitor the health data of pregnant women in real time, and to promptly notify a doctor if an abnormality is detected.

[0054] The wearable device is equipped with a sensor that measures the stress level of a pregnant woman and can suggest relaxation methods when the stress level increases. For example, the wearable device is equipped with a sensor that measures the stress level and suggests relaxation methods when the stress level increases. For example, instructions for breathing exercises or light stretching are displayed on the device's display. The wearable device also adds a function that uses the sensor that measures the stress level to automatically play relaxing music when the stress level of the pregnant woman increases. For example, the device connects to earphones via Bluetooth and plays relaxing music. The wearable device is also equipped with a sensor that measures the stress level and adds a function that automatically sprays a relaxing aroma when the stress level of the pregnant woman increases. For example, the device connects to an aroma diffuser and sprays an aroma with a relaxing effect. In this way, the mental health of pregnant women can be supported by measuring their stress level and suggesting relaxation methods.

[0055] The wearable device is equipped with a voice recognition function, allowing pregnant women to verbally report their health status. For example, a wearable device may be equipped with a voice recognition function, allowing pregnant women to verbally report their health status. For example, they may report "my stomach hurts" or "I'm feeling unwell." The wearable device also adds a function that uses the voice recognition function to automatically record and send to a doctor when a pregnant woman verbally reports her health status. For example, the device may record a report such as "I've had a headache since this morning" and notify the doctor. The wearable device also adds a function that provides appropriate advice when a pregnant woman verbally reports her health status. For example, the device may provide advice such as "drink plenty of fluids." This allows pregnant women to verbally report their health status, enabling more detailed health management.

[0056] The wearable device is equipped with an emotion estimation function and can monitor the emotional state of a pregnant woman in real time and provide health advice according to emotional changes. For example, the wearable device uses the emotion estimation function to monitor the emotional state of a pregnant woman in real time and provide health advice according to emotional changes. For example, it can suggest relaxation methods when stress levels rise. The wearable device is also equipped with an emotion estimation function and monitors the emotional state of a pregnant woman to provide dietary and exercise advice according to emotional changes. For example, it can suggest meals that have a relaxing effect when stress levels rise. The wearable device also uses the emotion estimation function to monitor the emotional state of a pregnant woman and provide mental health care advice according to emotional changes. For example, it can recommend counseling when stress levels rise. In this way, the mental health of pregnant women can be supported by monitoring their emotional state in real time and providing appropriate health advice.

[0057] Wearable devices are equipped with sensors that monitor fetal movements, allowing them to simultaneously collect health data on both the pregnant woman and the fetus. For example, a wearable device could add a sensor to monitor fetal movements and simultaneously collect health data on both the pregnant woman and the fetus. For example, it could monitor the fetal heart rate and movements in real time. Furthermore, wearable devices could be equipped with sensors that monitor fetal movements and add a function to notify the pregnant woman if an abnormality is detected. For example, an alert could be issued if there is little fetal movement. Furthermore, wearable devices could simultaneously collect health data on both the pregnant woman and the fetus, building a system that integrates and analyzes the data. For example, it could evaluate the impact of stress on the pregnant woman on the fetus. By simultaneously collecting health data on both the pregnant woman and the fetus, more comprehensive health management becomes possible.

[0058] Wearable devices can be worn by the pregnant woman's family and partners, and their health data can be collected and analyzed simultaneously. For example, they can monitor the heart rates and stress levels of all family members. Wearable devices can also collect health data from family members and partners, creating a system for managing the health of the entire household. For example, they can integrate and analyze data from all family members and provide health advice. Wearable devices can also be used by the pregnant woman's family and partners to collect health data from the entire household and add a notification function when an abnormality is detected. For example, an alert can be issued if any family member shows an abnormality. This makes it possible to manage the health of the entire household by collecting and analyzing the health data of the pregnant woman's family and partners.

[0059] A wearable device is equipped with an emotion estimation function, which analyzes the emotions of pregnant women when they wear the device, and can be used to improve the comfort of wearing the device and improve the design. For example, a wearable device can be equipped with an emotion estimation function and analyze the emotions of pregnant women when they wear the device. For example, it can detect discomfort when wearing the device and use this information to improve the design. A wearable device can also use the emotion estimation function to monitor the emotions of pregnant women when they wear the device in real time, which can be used to improve the comfort of wearing the device. For example, it can propose a design that reduces stress when wearing the device. A wearable device can also be equipped with an emotion estimation function, which can analyze the emotions of pregnant women when they wear the device, and a system can be built that can be used to improve the comfort of wearing the device and improve the design. For example, the design can be optimized based on the emotional data when wearing the device. This can analyze the emotions of pregnant women when they wear the device and use this information to improve the comfort of wearing the device and improve the design, thereby improving the comfort of pregnant women.

[0060] When analyzing health data, the AI ​​analysis unit can introduce an algorithm that compares it with past data to detect abnormalities. For example, when analyzing health data, the AI ​​analysis unit introduces an algorithm that compares it with past data to detect abnormalities. For example, it compares past heart rate data with current data to detect abnormalities. The AI ​​analysis unit also develops an algorithm that detects abnormalities based on past health data. For example, it compares past blood pressure data with current data to determine abnormalities. The AI ​​analysis unit also builds a system that compares it with past data to detect abnormalities when analyzing health data. For example, it compares past body temperature data with current data to detect abnormalities. This enables more accurate health management by detecting abnormalities by comparing it with past data.

[0061] When an abnormality is detected, the AI ​​analysis unit can identify the cause of the abnormality and suggest specific countermeasures. For example, the AI ​​analysis unit will add a function that, when an abnormality is detected, identifies the cause of the abnormality and suggests specific countermeasures. For example, if an abnormal heart rate is caused by stress, it will suggest relaxation methods. The AI ​​analysis unit will also build a system that, when an abnormality is detected, identifies the cause of the abnormality and suggests specific countermeasures. For example, if an abnormal blood pressure is caused by diet, it will suggest improving the diet. The AI ​​analysis unit will also develop an algorithm that, when an abnormality is detected, identifies the cause of the abnormality and suggests specific countermeasures. For example, if an abnormal body temperature is caused by lack of exercise, it will suggest exercise. This will enable quick and appropriate responses by identifying the cause of the abnormality and suggesting specific countermeasures.

[0062] The AI ​​analysis unit is equipped with an emotion estimation function and can analyze the emotional state of a pregnant woman and evaluate the impact of emotional changes on health data. For example, the AI ​​analysis unit uses the emotion estimation function to analyze the emotional state of a pregnant woman and evaluate the impact of emotional changes on health data. For example, it analyzes the impact of stress on heart rate. The AI ​​analysis unit is also equipped with an emotion estimation function and analyzes the emotional state of a pregnant woman to build a system that evaluates the impact of emotional changes on health data. For example, it evaluates the impact of emotional changes on blood pressure. The AI ​​analysis unit also uses the emotion estimation function to analyze the emotional state of a pregnant woman and develop an algorithm that evaluates the impact of emotional changes on health data. For example, it evaluates the impact of emotional changes on body temperature. This enables more comprehensive health management by analyzing the emotional state of a pregnant woman and evaluating the impact of emotional changes on health data.

[0063] The AI ​​analysis unit can include the diet and exercise records of pregnant women in the data to enable comprehensive health management. For example, the AI ​​analysis unit could build a system for comprehensive health management by including the diet and exercise records of pregnant women in the data. For example, it could record the contents of meals and frequency of exercise to comprehensively evaluate the health condition. The AI ​​analysis unit could also add the diet and exercise records to the data to be analyzed to enable comprehensive health management. For example, it could evaluate the nutritional balance of meals and the effectiveness of exercise. The AI ​​analysis unit could also analyze the diet and exercise records of pregnant women to develop algorithms for comprehensive health management. For example, it could provide health advice based on the diet and exercise data. This would enable comprehensive health management that includes the diet and exercise records of pregnant women.

[0064] The AI ​​analysis unit can also share the analysis results with the pregnant woman's family and partner so that they can provide support. For example, the AI ​​analysis unit builds a system that shares the analysis results with the pregnant woman's family and partner so that they can provide support. For example, it sends health status reports to the family. The AI ​​analysis unit also shares the analysis results with the family and partner so that they can support the pregnant woman. For example, it provides health advice to the family. The AI ​​analysis unit also adds a function that shares the analysis results with the pregnant woman's family and partner so that they can provide support. For example, it sends health status notifications to the family. This allows the analysis results to be shared with the pregnant woman's family and partner so that they can provide support.

[0065] The AI ​​analysis unit is equipped with an emotion estimation function and can analyze the emotional state of pregnant women and provide health advice based on their emotions. For example, the AI ​​analysis unit uses the emotion estimation function to build a system that analyzes the emotional state of pregnant women and provides health advice based on their emotions. For example, it can suggest relaxation methods when stress levels rise. The AI ​​analysis unit is also equipped with an emotion estimation function and analyzes the emotional state of pregnant women to provide health advice based on their emotions. For example, it can provide advice on diet and exercise according to changes in emotions. The AI ​​analysis unit also uses the emotion estimation function to analyze the emotional state of pregnant women and provide mental health care advice based on their emotions. For example, it can recommend counseling when stress levels rise. In this way, by analyzing the emotional state of pregnant women and providing health advice based on their emotions, it is possible to support their mental health.

[0066] The notification unit can send notifications not only to doctors but also to the pregnant woman's family and partner when an abnormality is detected. For example, the notification unit can add a function to send notifications not only to doctors but also to the pregnant woman's family and partner when an abnormality is detected. For example, the notification unit can notify the family of details of the abnormality and request support. The notification unit can also build a system that sends notifications to both doctors and family when an abnormality is detected. For example, it can make an emergency contact with the family and urge them to take action. The notification unit can also add a function to send notifications not only to doctors but also to the pregnant woman's family and partner when an abnormality is detected, allowing them to take action quickly. For example, it can notify the family of details of the abnormality and how to deal with it. This allows for a quick response by sending notifications not only to doctors but also to the pregnant woman's family and partner when an abnormality is detected.

[0067] The notification unit can include emergency measures along with a detailed explanation of the abnormality in the notification content when an abnormality is detected. For example, the notification unit includes emergency measures along with a detailed explanation of the abnormality in the notification content when an abnormality is detected. For example, specific instructions such as, "Your heart rate has suddenly increased. Please take deep breaths" are included. The notification unit also adds a detailed explanation of the abnormality to the notification content, building a system that provides emergency measures. For example, instructions such as, "Your blood pressure is abnormally high. Please lie down immediately." The notification unit also adds a function to include a detailed explanation of the abnormality and emergency measures in the notification when an abnormality is detected. For example, specific measures such as, "Your body temperature has suddenly increased. Please use a cooling sheet" are provided. In this way, by including a detailed explanation and emergency measures in the notification content when an abnormality is detected, quick and appropriate responses are possible.

[0068] The notification unit is equipped with an emotion estimation function and can analyze the emotional state of the pregnant woman and customize the notification content according to the emotion. The notification unit, for example, uses the emotion estimation function to analyze the emotional state of the pregnant woman and customize the notification content according to the emotion. For example, if stress is high, a notification including relaxation techniques is sent. The notification unit is also equipped with an emotion estimation function and builds a system that analyzes the emotional state of the pregnant woman to provide notification content according to the emotion. For example, if the emotion is unstable, a notification including a reassuring message is sent. The notification unit also uses the emotion estimation function to develop an algorithm that analyzes the emotional state of the pregnant woman and customizes the notification content according to the emotion. For example, if the emotion is calm, a notification including specific coping methods is sent. This makes it possible to customize the notification content according to the emotional state of the pregnant woman and provide more appropriate responses.

[0069] The notification unit can send notifications not only to doctors but also to local medical institutions and emergency services when an abnormality is detected. For example, the notification unit adds a function to send notifications not only to doctors but also to local medical institutions and emergency services when an abnormality is detected. For example, the notification unit notifies local hospitals of details of the abnormality and requests an emergency response. The notification unit also builds a system that sends notifications to both doctors and local medical institutions and emergency services when an abnormality is detected. For example, the notification unit makes an emergency contact with an emergency service and urges them to respond. The notification unit also adds a function to send notifications not only to doctors but also to local medical institutions and emergency services when an abnormality is detected, enabling a rapid response. For example, the notification unit notifies local medical institutions of details of the abnormality and how to deal with it. This enables a rapid response by sending notifications not only to doctors but also to local medical institutions and emergency services when an abnormality is detected.

[0070] The notification unit can visually display the notification content when an abnormality is detected as a graph or chart of health data, making it easier to understand intuitively. The notification unit, for example, visually displays the notification content when an abnormality is detected as a graph or chart of health data. For example, heart rate fluctuations may be displayed in a graph, making it easier to understand the abnormality intuitively. The notification unit also adds health data graphs and charts to the notification content, building a system that makes it easier to understand visually. For example, blood pressure fluctuations may be displayed in a chart, making it easier to understand the abnormality intuitively. The notification unit also adds a function to include health data graphs and charts in the notification when an abnormality is detected. For example, body temperature fluctuations may be displayed in a graph, making it easier to understand the abnormality intuitively. In this way, visually displaying the notification content when an abnormality is detected makes it easier to understand intuitively.

[0071] The notification unit is equipped with an emotion estimation function and can analyze the emotional state of the pregnant woman and provide emergency response instructions based on her emotions. The notification unit, for example, uses the emotion estimation function to analyze the emotional state of the pregnant woman and build a system that provides emergency response instructions based on her emotions. For example, if stress is high, instructions including relaxation techniques are provided. The notification unit is also equipped with an emotion estimation function and analyzes the emotional state of the pregnant woman to provide emergency response instructions based on her emotions. For example, if her emotions are unstable, instructions including a reassuring message are provided. The notification unit also uses the emotion estimation function to develop an algorithm that analyzes the emotional state of the pregnant woman and provides emergency response instructions based on her emotions. For example, if her emotions are calm, instructions including specific ways to deal with the situation are provided. This enables more appropriate emergency response instructions to be provided based on the emotional state of the pregnant woman.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The health monitoring system includes a wearable device that collects health data from pregnant women, an AI analysis unit that analyzes the collected data in real time, and a notification unit that notifies doctors when abnormalities are detected. The wearable device constantly monitors data such as heart rate, blood pressure, body temperature, and fetal heart rate. It can be a wristwatch or a belt-type device, for example. The AI ​​analysis unit analyzes the collected health data in real time and detects abnormalities. For example, it detects abnormalities when the heart rate rises suddenly or blood pressure becomes abnormally high. The notification unit sends a real-time notification to the doctor's smartphone or tablet when an abnormality is detected. The notification content includes specific details of the abnormality. For example, it could say, "Pregnant woman A's heart rate has risen sharply. Please check immediately." This allows for real-time monitoring of pregnant women's health data and prompt notification to doctors when an abnormality is detected.

[0074] The wearable device is equipped with a sensor that measures the stress level of pregnant women and can suggest relaxation methods when stress levels rise. For example, instructions for breathing exercises or light stretching can be displayed on the device's display. It is also possible to add a function that automatically plays relaxing music when stress levels rise. For example, the device connects to earphones via Bluetooth and plays relaxing music. It is also possible to add a function that automatically sprays relaxing aromas. For example, the device connects to an aroma diffuser and sprays aromas with a relaxing effect. In this way, it is possible to support the mental health of pregnant women by measuring their stress levels and suggesting relaxation methods.

[0075] Wearable devices are equipped with voice recognition functionality, allowing pregnant women to verbally report their health status. For example, they can report things like "I have a stomachache" or "I'm feeling unwell." It is also possible to add a function that automatically records and sends to a doctor when a pregnant woman verbally reports her health status using the voice recognition function. For example, a report such as "I've had a headache since this morning" can be recorded and notified to the doctor. The voice recognition function can also be added to provide appropriate advice when a pregnant woman verbally reports her health status. For example, advice such as "Drink plenty of fluids" can be provided. This allows pregnant women to verbally report their health status, enabling more detailed health management.

[0076] The wearable device is equipped with an emotion estimation function and can monitor the emotional state of pregnant women in real time and provide health advice according to emotional changes. For example, it can suggest relaxation methods when stress levels rise. It can also provide dietary and exercise advice according to emotional changes. For example, it can suggest meals that have a relaxing effect when stress levels rise. It can also provide mental health care advice according to emotional changes. For example, it can recommend counseling when stress levels rise. In this way, it is possible to support the mental health of pregnant women by monitoring their emotional state in real time and providing appropriate health advice.

[0077] Wearable devices are equipped with sensors that monitor fetal movements, making it possible to simultaneously collect health data on both the pregnant woman and the fetus. For example, fetal heart rate and movements can be monitored in real time. It is also possible to add a function that notifies the pregnant woman when abnormalities are detected. For example, an alert can be issued if there is little fetal movement. It is also possible to build a system that simultaneously collects health data on both the pregnant woman and the fetus and integrates and analyzes the data. For example, the impact of stress on the pregnant woman on the fetus can be evaluated. By collecting health data on both the pregnant woman and the fetus simultaneously, more comprehensive health management becomes possible.

[0078] Wearable devices can be worn by the pregnant woman's family and partners, and their health data can be collected and analyzed at the same time. For example, the heart rates and stress levels of all family members can be monitored. It is also possible to build a system that collects health data from family members and partners to manage the health of the entire household. For example, the system can integrate and analyze data from all family members and provide health advice. It is also possible to add a function that notifies users when an abnormality is detected. For example, an alert can be issued if any family member shows signs of an abnormality. This makes it possible to manage the health of the entire household by collecting and analyzing the health data of the pregnant woman's family and partners.

[0079] Wearable devices are equipped with emotion estimation functions, which can analyze the emotions felt by pregnant women when they wear the device and help improve the comfort and design. For example, they can detect discomfort when worn and use this information to improve the design. They can also monitor emotions felt when worn in real time and use this information to improve comfort. For example, they can propose designs that reduce stress when worn. It is also possible to build a system that optimizes the design based on emotional data from when the device is worn. This can analyze the emotions felt by pregnant women when they wear the device and use this information to improve comfort and design, thereby increasing comfort for pregnant women.

[0080] When analyzing health data, the AI ​​analysis unit can introduce algorithms that compare data with past data to detect abnormalities. For example, it can compare past heart rate data with current data to detect abnormalities. It can also develop algorithms to detect abnormalities based on past health data. For example, it can compare past blood pressure data with current data to determine abnormalities. It can also build a system that compares past body temperature data with current data to detect abnormalities. This allows for more accurate health management by detecting abnormalities by comparing data with past data.

[0081] When the AI ​​analysis unit detects an abnormality, it can identify the cause and suggest specific countermeasures. For example, if an abnormal heart rate is caused by stress, it can suggest relaxation methods. Also, if an abnormal blood pressure is caused by diet, it can suggest dietary improvements. For example, if an abnormal body temperature is caused by lack of exercise, it can suggest exercise. This allows for quick and appropriate response by identifying the cause of the abnormality and suggesting specific countermeasures.

[0082] The AI ​​analysis unit is equipped with an emotion estimation function, which can analyze the emotional state of pregnant women and evaluate the impact of emotional changes on health data. For example, it can analyze the impact of stress on heart rate. It can also build a system to evaluate the impact of emotional changes on blood pressure. For example, it can develop an algorithm to evaluate the impact of emotional changes on body temperature. This allows for more comprehensive health management by analyzing the emotional state of pregnant women and evaluating the impact of emotional changes on health data.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The wearable device collects health data from the pregnant woman. For example, it constantly monitors data such as heart rate, blood pressure, body temperature, and fetal heart rate. The wearable device is designed to be lightweight and comfortable to wear, so as not to interfere with the pregnant woman's daily life. For example, a wristwatch-type or belt-type device is conceivable. Step 2: The AI ​​analysis unit analyzes the health data collected by the wearable device in real time. For example, the AI ​​analysis unit detects abnormalities such as a sudden increase in heart rate or abnormally high blood pressure. Generative AI (e.g., text generation AI or multimodal generation AI) is used to analyze the pregnant woman's health data and detect abnormalities. The generative AI receives a prompt containing the pregnant woman's health data as input and detects abnormalities based on the prompt. Step 3: The notification unit notifies the doctor when an abnormality is detected by the AI ​​analysis unit. For example, the notification unit sends a real-time notification to the doctor's smartphone or tablet. The notification content includes specific details of the abnormality. For example, it may include content such as, "Pregnant woman A's heart rate has risen sharply. Please check immediately."

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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. Wearable devices and an AI analysis unit that analyzes the health data collected by the wearable device in real time; and a notification unit that notifies a doctor when an abnormality is detected by the AI ​​analysis unit. A system characterized by:

2. The wearable device is Equipped with a sensor that measures the stress level of pregnant women, Suggest ways to relax when stress levels rise 2. The system of claim 1.

3. The wearable device is Equipped with voice recognition function, Pregnant women can verbally report their health status 2. The system of claim 1.

4. The wearable device is Equipped with emotion estimation function, Real-time monitoring of the emotional state of pregnant women Providing health advice in response to changes in said emotional state 2. The system of claim 1.

5. The wearable device is Equipped with sensors to monitor fetal movements, Collecting maternal and fetal health data simultaneously 2. The system of claim 1.

6. The wearable device is It is worn by the pregnant woman's family or partner, Their health data will also be collected and analyzed at the same time.

2. The system of claim 1.

7. The wearable device is Equipped with emotion estimation function, Analyzing the emotions of pregnant women when wearing the device, Helps improve fit and design 2. The system of claim 1.

8. The AI ​​analysis unit When analyzing the health data, an algorithm is introduced to compare it with past data and detect the abnormality.

2. The system of claim 1.

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