system

Abstract

The system according to this embodiment aims to analyze the user's biometric data in real time and immediately notify them of any abnormalities. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a detection unit, a notification unit, and a provision unit. The collection unit collects the user's biometric data. The analysis unit analyzes the data collected by the collection unit. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The notification unit notifies the user of the abnormalities detected by the detection unit. The provision unit provides appropriate health management advice based on the abnormalities notified by the notification unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, real-time analysis of user biometric data and immediate notification of abnormalities have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze user biometric data in real time and immediately notify abnormalities.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, a notification unit, and a provision unit. The collection unit collects the user's biometric data. The analysis unit analyzes the data collected by the collection unit. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The notification unit notifies the user of the abnormalities detected by the detection unit. The provision unit provides appropriate health management advice based on the abnormalities notified by the notification unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's biometric data in real time and immediately notify of any abnormalities. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) A health monitoring system according to an embodiment of the present invention is a system that analyzes a user's biometric data in real time and monitors their health status. This health monitoring system collects the user's biometric data, analyzes it with AI, and monitors their health status. If an abnormality is detected, it immediately notifies the user and provides appropriate health management advice. For example, the health monitoring system collects the user's biometric data. For example, the health monitoring system collects data such as heart rate, blood pressure, and body temperature. Next, the health monitoring system analyzes the collected data with AI and monitors the health status. The AI ​​analyzes the data using machine learning and detects abnormalities. For example, the AI ​​detects deviations from the normal range and specific patterns. If an abnormality is detected, the health monitoring system immediately notifies the user. For example, the health monitoring system provides notifications through a smartphone app. The health monitoring system also provides appropriate health management advice based on the abnormality. For example, the health monitoring system provides advice such as recommending exercise, improving diet, and seeing a doctor. As a result, the health monitoring system enables disease prevention and early detection, contributing to extending the user's healthy lifespan and reducing medical expenses. This allows the health monitoring system to analyze the user's biometric data in real time and monitor their health status.

[0029] The health monitoring system according to the embodiment comprises a data collection unit, an analysis unit, a detection unit, a notification unit, and a data provision unit. The data collection unit collects the user's biometric data. The data collection unit collects data such as heart rate, blood pressure, and body temperature. The data collection unit collects biometric data using, for example, a wearable device. The data collection unit can also collect biometric data using a smartphone sensor. Furthermore, the data collection unit can also collect biometric data using a medical device. For example, the data collection unit monitors heart rate in real time using a wearable device. The data collection unit can measure blood pressure using a smartphone sensor. The data collection unit can measure body temperature using a medical device. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using, for example, machine learning. The analysis unit can analyze the data using, for example, deep learning. Furthermore, the analysis unit can analyze the data using a support vector machine. Furthermore, the analysis unit can analyze the data using a random forest. For example, the analysis unit analyzes heart rate data using deep learning. The analysis unit can analyze blood pressure data using a support vector machine. The analysis unit can analyze body temperature data using a random forest. The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit can, for example, detect deviations from the normal range. The detection unit can, for example, detect specific patterns. The detection unit can also detect the frequency of anomaly occurrences. For example, the detection unit can detect deviations from the normal range in heart rate data. The detection unit can detect specific patterns in blood pressure data. The detection unit can detect the frequency of anomalies in body temperature data. The notification unit notifies the user of anomalies detected by the detection unit. The notification unit can, for example, send notifications via a smartphone app. The notification unit can, for example, send notifications via email. The notification unit can also send notifications via SMS. For example, the notification unit can notify of heart rate anomalies via a smartphone app. The notification unit can notify of blood pressure anomalies via email. The notification unit can notify of body temperature anomalies via SMS.The providing unit provides appropriate health management advice based on abnormalities notified by the notification unit. For example, the providing unit may recommend exercise. For example, the providing unit may suggest dietary improvements. The providing unit may also recommend seeing a doctor. For example, the providing unit may recommend exercise in response to an abnormal heart rate. For example, the providing unit may suggest dietary improvements in response to an abnormal blood pressure. For example, the providing unit may recommend seeing a doctor in response to an abnormal body temperature. In this way, the health monitoring system according to the embodiment can analyze the user's biometric data in real time and monitor their health status.

[0030] The data collection unit collects the user's biometric data. For example, the data collection unit collects data such as heart rate, blood pressure, and body temperature. The data collection unit collects biometric data using, for example, a wearable device. Specifically, the wearable device has a built-in heart rate sensor, blood pressure monitor, and thermometer, and these sensors are in close contact with the user's body to acquire data. The heart rate sensor uses an optical sensor to detect changes in blood flow and measure heart rate. The blood pressure monitor measures blood pressure using a pressure sensor, and the thermometer measures the temperature of the skin surface using an infrared sensor. This data is transmitted to a smartphone or cloud server via Bluetooth® or Wi-Fi. The data collection unit can also collect biometric data using the sensors of a smartphone. Smartphones are equipped with accelerometers, gyroscopes, GPS, etc., and these sensors are used to acquire the user's activity level and location information. For example, the accelerometer measures the user's steps and exercise intensity, the gyroscope detects the user's posture and movement, and GPS acquires the user's location information and measures distance traveled and speed. Furthermore, the data collection unit can also collect biometric data using medical devices. For example, it can obtain more accurate data using medical devices such as electrocardiographs, blood pressure monitors, and thermometers used in hospitals and clinics. This allows the data collection unit to collect biometric data from a variety of devices, including wearable devices, smartphones, and medical devices, and comprehensively monitor the user's health status.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses machine learning to analyze the data. Specifically, it inputs collected data such as heart rate, blood pressure, and body temperature into a machine learning algorithm to detect anomalies and patterns. The analysis unit can also analyze data using deep learning. Deep learning uses multi-layered neural networks to analyze data and extract complex patterns and features. For example, by inputting heart rate data into a deep learning model and analyzing heart rhythm and variability, arrhythmias and heart rate abnormalities can be detected. The analysis unit can also analyze data using support vector machines. Support vector machines are used for data classification and regression analysis, and can analyze collected blood pressure data to detect deviations from the normal range. Furthermore, the analysis unit can also analyze data using random forests. Random forests are ensemble learning algorithms using numerous decision trees, and can analyze body temperature data to detect abnormal temperature fluctuations. This allows the analysis unit to comprehensively analyze collected biometric data and evaluate the user's health status in detail. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term health trend and risk assessments. For example, it can analyze a user's heart rate variability patterns based on past heart rate data and predict future heart disease risk. This allows the analysis unit to contribute not only to real-time data analysis but also to long-term health management.

[0032] The detection unit detects abnormalities based on data analyzed by the analysis unit. For example, the detection unit detects deviations from the normal range. Specifically, based on the analysis results obtained by the analysis unit, it evaluates whether data such as heart rate, blood pressure, and body temperature are within the normal range. For example, if heart rate data exceeds the normal range, the detection unit detects an abnormality and notifies the user. The detection unit can also detect specific patterns. For example, it can detect arrhythmia patterns in heart rate data. The detection unit can also detect the frequency of abnormalities. For example, if abnormal temperature fluctuations occur frequently in body temperature data, the detection unit records the frequency and notifies the user. This allows the detection unit to quickly and accurately detect abnormalities in the user's health condition and respond early. Furthermore, the detection unit can issue different levels of warnings depending on the type and severity of the abnormality. For example, it can issue a cautionary notification for minor abnormalities and an emergency notification for severe abnormalities. This allows the detection unit to support appropriate responses according to the user's health condition.

[0033] The notification unit notifies the user of any abnormalities detected by the detection unit. The notification unit can, for example, send notifications via a smartphone app. Specifically, it sends push notifications to the smartphone app to inform the user of the abnormality. The notification unit can also send notifications via email. The email contains detailed information about the abnormality and recommended countermeasures, allowing the user to understand the situation by checking the email. The notification unit can also send notifications via SMS. SMS is an effective means of quickly notifying users of abnormalities in a short message format. For example, the notification unit can notify users of abnormal heart rates via a smartphone app. The app displays the abnormal heart rate and recommended countermeasures, allowing the user to understand the situation and take appropriate action by checking the app. The notification unit can also notify users of abnormal blood pressure via email. The email contains the abnormal blood pressure and recommended countermeasures, allowing the user to understand the situation by checking the email. The notification unit can also notify users of abnormal body temperature via SMS. SMS is an effective means of quickly notifying users of abnormalities in a short message format. This allows the notification unit to quickly and reliably notify users of anomalies and support them in taking appropriate action.

[0034] The service provider will provide appropriate health management advice based on the abnormalities notified by the notification unit. For example, the service provider may recommend exercise. Specifically, if an abnormality in heart rate is detected, the service provider will recommend appropriate exercise to the user. For example, light aerobic exercise or stretching can stabilize the heart rate. The service provider may also suggest dietary improvements. If an abnormality in blood pressure is detected, the service provider will suggest that the user reduce their salt intake. In addition, consuming more fruits and vegetables can stabilize blood pressure. Furthermore, the service provider may also recommend that the user see a doctor. If an abnormality in body temperature is detected, the service provider will recommend that the user see a doctor. This will allow the user to receive medical attention early and appropriate treatment. The service provider can provide individualized advice tailored to the user's health condition. For example, it may provide more specific advice considering the user's past data and lifestyle. This allows the service provider to support the user's health management and promote improvement in their health condition. Furthermore, the service provider can collect user feedback and continuously improve the content of the advice. This allows the service provider to offer users optimal health management advice and support them in maintaining and improving their health.

[0035] The data collection unit can collect the user's biometric data in real time. For example, the data collection unit can collect heart rate in real time using a wearable device. For example, the data collection unit can collect blood pressure in real time using a smartphone sensor. For example, the data collection unit can collect body temperature in real time using a medical device. This allows for the collection of the user's biometric data in real time, enabling an understanding of their latest health status. The specific definition of real time includes the frequency and delay time of data collection. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input heart rate data acquired from a wearable device into a generating AI and have the generating AI perform real-time data collection.

[0036] The analysis unit can analyze the collected data using machine learning. For example, the analysis unit can analyze heart rate data using deep learning. For example, the analysis unit can analyze blood pressure data using support vector machines. For example, the analysis unit can analyze body temperature data using random forests. This improves the accuracy of data analysis by using machine learning. Specific machine learning algorithms and techniques include deep learning, support vector machines, and random forests. Some or all of the above-mentioned processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform data analysis using machine learning.

[0037] The detection unit can detect anomalies based on the analyzed data. For example, the detection unit can detect deviations from the normal range in heart rate data. For example, the detection unit can detect specific patterns in blood pressure data. For example, the detection unit can detect the frequency of anomalies in body temperature data. This allows for the early detection of health abnormalities by detecting anomalies based on the analyzed data. Specific criteria and definitions of anomalies include deviations from the normal range and detection of specific patterns. Some or all of the above-described processes in the detection unit may be performed using AI or not. For example, the detection unit can input the analyzed data into a generating AI and have the generating AI perform anomaly detection.

[0038] The notification unit can immediately notify the user if an abnormality is detected. For example, the notification unit can notify of an abnormal heart rate via a smartphone app. For example, the notification unit can notify of an abnormal blood pressure via email. For example, the notification unit can notify of an abnormal body temperature via SMS. This enables a quick response by notifying immediately when an abnormality is detected. The specific definition of "immediately" includes the delay time and timing of the notification. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can have an AI generate notifications of abnormalities.

[0039] The service provider can provide appropriate health management advice based on the notified abnormality. For example, the service provider may recommend exercise in response to an abnormal heart rate. For example, the service provider may suggest dietary improvements in response to an abnormal blood pressure. For example, the service provider may recommend seeing a doctor in response to an abnormal body temperature. In this way, the service provider supports the user's health management by providing appropriate health management advice based on the notified abnormality. Specific examples of appropriate health management advice include recommendations for exercise, dietary improvements, and consultations with a doctor. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider may have an AI generate health management advice based on the abnormality.

[0040] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can analyze the user's past heart rate data and determine the optimal placement of heart rate sensors. For example, the data collection unit can analyze the user's past sleep data and select the optimal sleep tracking method. For example, the data collection unit can analyze the user's past exercise data and propose the optimal exercise data collection method. In this way, the optimal data collection method can be selected by analyzing the user's past health data. Specific criteria and methods for the optimal data collection method include sensor placement and data collection frequency. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past health data into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter biometric data based on the user's current activity level. For example, if the user is exercising, the data collection unit can collect only data related to exercise. For example, if the user is resting, the data collection unit can collect only data related to rest. For example, if the user is working, the data collection unit can collect only data related to work. This allows for the collection of highly relevant data by filtering the data based on the user's current activity level. Specific criteria and methods for filtering include the type of activity level and the filtering algorithm. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input current activity level data into a generating AI and have the generating AI perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting biometric data. For example, if the user is at high altitude, the data collection unit can prioritize the collection of biometric data related to high altitude. For example, if the user is at the beach, the data collection unit can prioritize the collection of biometric data related to the beach. For example, if the user is in an urban area, the data collection unit can prioritize the collection of biometric data related to the urban area. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Specific methods for acquiring and using geographical location information include GPS data and location information services. Some or all of the above-described processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data when collecting biometric data. For example, if a user is experiencing stress on social media, the data collection unit can collect stress-related biometric data. For example, if a user is relaxing on social media, the data collection unit can collect relaxation-related biometric data. For example, if a user is posting about exercise on social media, the data collection unit can collect exercise-related biometric data. In this way, relevant data can be collected by analyzing a user's social media activity. Specific methods and criteria for analyzing social media activity include post content, activity frequency, and follower count. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Specific evaluation criteria and methods for data importance include data type, frequency of occurrence, and impact. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data importance into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a heart rate analysis algorithm to heart rate data. For example, the analysis unit can apply a sleep analysis algorithm to sleep data. For example, the analysis unit can apply an exercise analysis algorithm to exercise data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Specific classification methods and criteria for data categories include health data, activity data, environmental data, etc. Some or all of the above-described processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an analysis algorithm according to the category.

[0046] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of recently collected data. For example, the analysis unit may postpone the analysis of previously collected data. For example, the analysis unit may prioritize the analysis of data collected during a specific period. This allows for the prioritization of the latest data by determining the priority of analysis based on the data collection period. Specific evaluation criteria and methods for the data collection period include the collection date and time, and the collection frequency. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the priority of analysis based on the collection period.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Specific evaluation criteria and methods for data relevance include correlation and causation. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order based on the relevance.

[0048] The detection unit can improve the accuracy of anomaly detection by considering the interrelationships between data during detection. For example, the detection unit can detect anomalies by considering the interrelationships between heart rate and blood pressure data. For example, the detection unit can detect anomalies by considering the interrelationships between sleep data and exercise data. For example, the detection unit can detect anomalies by considering the interrelationships between meal data and blood glucose level data. This improves the accuracy of anomaly detection by considering the interrelationships between data. Specific evaluation criteria and methods for data interrelationships include correlation and causation. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the interrelationships between data into a generating AI and have the generating AI perform an improvement in the accuracy of anomaly detection based on the interrelationships.

[0049] The detection unit can perform anomaly detection by considering the user's attribute information during detection. For example, the detection unit can detect anomalies by considering the user's age. For example, the detection unit can detect anomalies by considering the user's gender. For example, the detection unit can detect anomalies by considering the user's medical history. This makes it possible to perform more individualized anomaly detection by considering the user's attribute information. Specific methods for acquiring and using the user's attribute information include age, gender, occupation, etc. Some or all of the above processing in the detection unit may be performed using AI, or it may be performed without using AI. For example, the detection unit can input the user's attribute information into a generating AI and have the generating AI perform anomaly detection based on the attribute information.

[0050] The detection unit can perform anomaly detection while considering the geographical distribution of the data. For example, if the user is in a high-altitude area, the detection unit can detect anomalies related to high altitude. For example, if the user is on a beach, the detection unit can detect anomalies related to the beach. For example, if the user is in an urban area, the detection unit can detect anomalies related to urban areas. In this way, by considering the geographical distribution of the data, region-specific anomalies can be detected. Specific evaluation criteria and methods for geographical distribution include the data distribution for each region and geographical characteristics. Some or all of the above processing in the detection unit may be performed using AI, or it may be performed without AI. For example, the detection unit can input geographical distribution data into a generating AI and have the generating AI perform anomaly detection based on geographical distribution.

[0051] The detection unit can improve the accuracy of anomaly detection by referring to relevant literature during detection. For example, the detection unit can update the criteria for anomaly detection by referring to the latest medical literature. For example, the detection unit can improve the anomaly detection algorithm by referring to relevant research papers. For example, the detection unit can improve the accuracy of anomaly detection by referring to medical guidelines. As a result, the accuracy of anomaly detection is improved by referring to relevant literature. Specific methods and criteria for referring to relevant literature include literature databases and citation standards. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input relevant literature data into a generating AI and have the generating AI perform literature-based anomaly detection accuracy improvement.

[0052] The notification unit can adjust the level of detail of the notification based on the severity of the anomaly. For example, the notification unit can provide detailed notifications for highly severe anomalies. For example, it can provide simplified notifications for less severe anomalies. For example, it can provide notifications with an appropriate level of detail for moderately severe anomalies. By adjusting the level of detail of the notification based on the severity of the anomaly, appropriate notifications can be provided for important anomalies. Specific evaluation criteria and methods for the severity of anomalies include the degree of impact and frequency of occurrence of the anomaly. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the severity of the anomaly into a generating AI and have the generating AI perform the adjustment of the level of detail of the notification based on the severity.

[0053] The notification unit can apply different notification methods depending on the category of the anomaly when it issues a notification. For example, the notification unit can provide an audio notification for an abnormal heart rate. For example, it can provide a text notification for an abnormal blood pressure. For example, it can provide a visual notification for an abnormal sleep. By applying different notification methods depending on the category of the anomaly, appropriate notifications can be provided according to the content of the anomaly. Specific classification methods and criteria for the categories of anomalies include health anomalies, environmental anomalies, etc. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the category of the anomaly into a generating AI and have the generating AI execute the application of a notification method according to the category.

[0054] The notification unit can determine the priority of notifications based on when the anomaly occurred. For example, the notification unit may prioritize notifications for recently occurring anomalies. For example, the notification unit may postpone notifications for anomalies that occurred in the past. For example, the notification unit may prioritize notifications for anomalies that occurred during a specific period. This allows for priority notification of the latest anomalies by determining the priority of notifications based on when the anomaly occurred. Specific evaluation criteria and methods for determining the timing of an anomaly include the date and time of occurrence and frequency of occurrence. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the timing of the anomaly into a generating AI and have the generating AI determine the priority of notifications based on the timing of occurrence.

[0055] The notification unit can adjust the order of notifications based on the relevance of the anomalies. For example, the notification unit may prioritize notifying of highly relevant anomalies. For example, the notification unit may postpone notifying of less relevant anomalies. For example, the notification unit can dynamically adjust the order of notifications according to the relevance of the anomalies. This allows important anomalies to be notified preferentially by adjusting the order of notifications based on the relevance of the anomalies. Specific evaluation criteria and methods for the relevance of anomalies include correlation and causation. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the relevance of anomalies into a generating AI and have the generating AI perform the adjustment of the notification order based on the relevance.

[0056] The service provider can provide optimal advice by referring to the user's past health data when providing advice. For example, the service provider can provide advice on heart rate management by referring to the user's past heart rate data. For example, the service provider can provide advice on improving sleep by referring to the user's past sleep data. For example, the service provider can provide advice on enhancing the effects of exercise by referring to the user's past exercise data. In this way, optimal advice can be provided by referring to the user's past health data. Specific criteria and methods for optimal advice include personalized advice and advice based on past data. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input past health data into a generating AI and have the generating AI perform the task of providing optimal advice based on the data.

[0057] The service provider can customize the means of providing advice based on the user's current lifestyle. For example, if the user is busy, the service provider can provide advice that can be implemented in a short amount of time. If the user is relaxed, the service provider can provide advice to help them maintain that relaxation. If the user is exercising, the service provider can provide advice to enhance the effects of the exercise. By customizing the means of advice based on the user's current lifestyle, more actionable advice can be provided. Specific evaluation criteria and methods for the current lifestyle include lifestyle habits and activity levels. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input current lifestyle data into a generating AI and have the generating AI customize the means of advice based on the situation.

[0058] The service provider can provide optimal advice by considering the user's geographical location when providing advice. For example, if the user is at high altitude, the service provider can provide health management advice suitable for high altitude. For example, if the user is at the beach, the service provider can provide health management advice suitable for the beach. For example, if the user is in an urban area, the service provider can provide health management advice suitable for the urban area. In this way, optimal advice can be provided by considering the user's geographical location. Specific methods for acquiring and using geographical location information include GPS data and location information services. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input geographical location data into a generating AI and have the generating AI perform the task of providing optimal advice based on location information.

[0059] The service provider can analyze a user's social media activity and suggest methods for providing advice. For example, if a user is experiencing stress on social media, the service provider can offer advice on stress reduction. If a user is relaxing on social media, the service provider can offer advice on maintaining that relaxation. If a user is posting about exercise on social media, the service provider can offer advice on enhancing the effectiveness of their exercise. By analyzing a user's social media activity, the service provider can suggest more appropriate methods for providing advice. Specific methods and criteria for analyzing social media activity include post content, activity frequency, and follower count. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input social media activity data into a generating AI and have the generating AI suggest methods for providing advice based on that activity.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user is at high altitude, biometric data related to high altitude can be prioritized. If the user is at the beach, biometric data related to the beach can be prioritized. If the user is in an urban area, biometric data related to the urban area can be prioritized. In this way, by considering the user's geographical location, highly relevant data can be prioritized for collection. Specific methods for acquiring and using geographical location information include GPS data and location information services. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0062] The service provider can provide optimal advice by referring to the user's past health data when providing advice. For example, it can provide advice on heart rate management by referring to the user's past heart rate data. It can provide advice on improving sleep by referring to the user's past sleep data. It can provide advice on enhancing the effects of exercise by referring to the user's past exercise data. In this way, optimal advice can be provided by referring to the user's past health data. Specific criteria and methods for optimal advice include personalized advice and advice based on past data. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input past health data into a generating AI and have the generating AI perform the task of providing optimal advice based on the data.

[0063] The data collection unit can analyze a user's social media activity and collect relevant data. For example, if a user is experiencing stress on social media, it can collect biometric data related to stress. If a user is relaxing on social media, it can collect biometric data related to relaxation. If a user is posting about exercise on social media, it can collect biometric data related to exercise. In this way, relevant data can be collected by analyzing a user's social media activity. Specific methods and criteria for analyzing social media activity include post content, activity frequency, and follower count. Some or all of the above-described processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI collect relevant data.

[0064] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on highly important data, a simplified analysis on less important data, and an analysis with an appropriate level of detail on moderately important data. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Specific evaluation criteria and methods for data importance include data type, frequency of occurrence, and impact. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the data importance into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0065] The service provider can customize the means of providing advice based on the user's current lifestyle. For example, if the user is busy, it can provide advice that can be implemented in a short amount of time. If the user is relaxed, it can provide advice to help them maintain that relaxation. If the user is exercising, it can provide advice to enhance the effects of their exercise. By customizing the means of advice based on the user's current lifestyle, more actionable advice can be provided. Specific evaluation criteria and methods for the current lifestyle include lifestyle habits and activity levels. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input current lifestyle data into a generating AI and have the generating AI customize the means of advice based on the situation.

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

[0067] Step 1: The data collection unit collects the user's biometric data. The data collection unit collects data such as heart rate, blood pressure, and body temperature. The data collection unit can collect biometric data using wearable devices, smartphone sensors, and medical devices. For example, the data collection unit can monitor heart rate in real time using a wearable device, measure blood pressure using a smartphone sensor, and measure body temperature using a medical device. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using methods such as machine learning, deep learning, support vector machines, and random forests. For example, the analysis unit can analyze heart rate data using deep learning, blood pressure data using support vector machines, and body temperature data using random forests. Step 3: The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit can detect deviations from the normal range, specific patterns, and the frequency of anomalies. For example, the detection unit can detect deviations from the normal range in heart rate data, specific patterns in blood pressure data, and the frequency of anomalies in body temperature data. Step 4: The notification unit notifies the user of any abnormalities detected by the detection unit. The notification unit can send notifications via smartphone apps, email, SMS, etc. For example, the notification unit can notify of abnormal heart rate via a smartphone app, abnormal blood pressure via email, and abnormal body temperature via SMS. Step 5: The service provider provides appropriate health management advice based on the abnormalities notified by the notification provider. The service provider may recommend exercise, suggest dietary improvements, or advise the user to see a doctor. For example, the service provider may recommend exercise for abnormal heart rate, suggest dietary improvements for abnormal blood pressure, or advise the user to see a doctor for abnormal body temperature.

[0068] (Example of form 2) A health monitoring system according to an embodiment of the present invention is a system that analyzes a user's biometric data in real time and monitors their health status. This health monitoring system collects the user's biometric data, analyzes it with AI, and monitors their health status. If an abnormality is detected, it immediately notifies the user and provides appropriate health management advice. For example, the health monitoring system collects the user's biometric data. For example, the health monitoring system collects data such as heart rate, blood pressure, and body temperature. Next, the health monitoring system analyzes the collected data with AI and monitors the health status. The AI ​​analyzes the data using machine learning and detects abnormalities. For example, the AI ​​detects deviations from the normal range and specific patterns. If an abnormality is detected, the health monitoring system immediately notifies the user. For example, the health monitoring system provides notifications through a smartphone app. The health monitoring system also provides appropriate health management advice based on the abnormality. For example, the health monitoring system provides advice such as recommending exercise, improving diet, and seeing a doctor. As a result, the health monitoring system enables disease prevention and early detection, contributing to extending the user's healthy lifespan and reducing medical expenses. This allows the health monitoring system to analyze the user's biometric data in real time and monitor their health status.

[0069] The health monitoring system according to the embodiment comprises a data collection unit, an analysis unit, a detection unit, a notification unit, and a data provision unit. The data collection unit collects the user's biometric data. The data collection unit collects data such as heart rate, blood pressure, and body temperature. The data collection unit collects biometric data using, for example, a wearable device. The data collection unit can also collect biometric data using a smartphone sensor. Furthermore, the data collection unit can also collect biometric data using a medical device. For example, the data collection unit monitors heart rate in real time using a wearable device. The data collection unit can measure blood pressure using a smartphone sensor. The data collection unit can measure body temperature using a medical device. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using, for example, machine learning. The analysis unit can analyze the data using, for example, deep learning. Furthermore, the analysis unit can analyze the data using a support vector machine. Furthermore, the analysis unit can analyze the data using a random forest. For example, the analysis unit analyzes heart rate data using deep learning. The analysis unit can analyze blood pressure data using a support vector machine. The analysis unit can analyze body temperature data using a random forest. The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit can, for example, detect deviations from the normal range. The detection unit can, for example, detect specific patterns. The detection unit can also detect the frequency of anomaly occurrences. For example, the detection unit can detect deviations from the normal range in heart rate data. The detection unit can detect specific patterns in blood pressure data. The detection unit can detect the frequency of anomalies in body temperature data. The notification unit notifies the user of anomalies detected by the detection unit. The notification unit can, for example, send notifications via a smartphone app. The notification unit can, for example, send notifications via email. The notification unit can also send notifications via SMS. For example, the notification unit can notify of heart rate anomalies via a smartphone app. The notification unit can notify of blood pressure anomalies via email. The notification unit can notify of body temperature anomalies via SMS.The providing unit provides appropriate health management advice based on abnormalities notified by the notification unit. For example, the providing unit may recommend exercise. For example, the providing unit may suggest dietary improvements. The providing unit may also recommend seeing a doctor. For example, the providing unit may recommend exercise in response to an abnormal heart rate. For example, the providing unit may suggest dietary improvements in response to an abnormal blood pressure. For example, the providing unit may recommend seeing a doctor in response to an abnormal body temperature. In this way, the health monitoring system according to the embodiment can analyze the user's biometric data in real time and monitor their health status.

[0070] The data collection unit collects the user's biometric data. For example, the data collection unit collects data such as heart rate, blood pressure, and body temperature. The data collection unit collects biometric data using, for example, wearable devices. Specifically, wearable devices have built-in heart rate sensors, blood pressure monitors, and thermometers, and these sensors are in close contact with the user's body to acquire data. The heart rate sensor uses an optical sensor to detect changes in blood flow and measure heart rate. The blood pressure monitor measures blood pressure using a pressure sensor, and the thermometer measures the temperature of the skin surface using an infrared sensor. This data is transmitted to a smartphone or cloud server via Bluetooth or Wi-Fi. The data collection unit can also collect biometric data using the sensors of a smartphone. Smartphones are equipped with accelerometers, gyroscopes, GPS, etc., and these sensors are used to acquire the user's activity level and location information. For example, the accelerometer measures the user's steps and exercise intensity, the gyroscope detects the user's posture and movement, and GPS acquires the user's location information and measures distance traveled and speed. Furthermore, the data collection unit can also collect biometric data using medical devices. For example, it can obtain more accurate data using medical devices such as electrocardiographs, blood pressure monitors, and thermometers used in hospitals and clinics. This allows the data collection unit to collect biometric data from a variety of devices, including wearable devices, smartphones, and medical devices, and comprehensively monitor the user's health status.

[0071] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses machine learning to analyze the data. Specifically, it inputs collected data such as heart rate, blood pressure, and body temperature into a machine learning algorithm to detect anomalies and patterns. The analysis unit can also analyze data using deep learning. Deep learning uses multi-layered neural networks to analyze data and extract complex patterns and features. For example, by inputting heart rate data into a deep learning model and analyzing heart rhythm and variability, arrhythmias and heart rate abnormalities can be detected. The analysis unit can also analyze data using support vector machines. Support vector machines are used for data classification and regression analysis, and can analyze collected blood pressure data to detect deviations from the normal range. Furthermore, the analysis unit can also analyze data using random forests. Random forests are ensemble learning algorithms using numerous decision trees, and can analyze body temperature data to detect abnormal temperature fluctuations. This allows the analysis unit to comprehensively analyze collected biometric data and evaluate the user's health status in detail. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term health trend and risk assessments. For example, it can analyze a user's heart rate variability patterns based on past heart rate data and predict future heart disease risk. This allows the analysis unit to contribute not only to real-time data analysis but also to long-term health management.

[0072] The detection unit detects abnormalities based on data analyzed by the analysis unit. For example, the detection unit detects deviations from the normal range. Specifically, based on the analysis results obtained by the analysis unit, it evaluates whether data such as heart rate, blood pressure, and body temperature are within the normal range. For example, if heart rate data exceeds the normal range, the detection unit detects an abnormality and notifies the user. The detection unit can also detect specific patterns. For example, it can detect arrhythmia patterns in heart rate data. The detection unit can also detect the frequency of abnormalities. For example, if abnormal temperature fluctuations occur frequently in body temperature data, the detection unit records the frequency and notifies the user. This allows the detection unit to quickly and accurately detect abnormalities in the user's health condition and respond early. Furthermore, the detection unit can issue different levels of warnings depending on the type and severity of the abnormality. For example, it can issue a cautionary notification for minor abnormalities and an emergency notification for severe abnormalities. This allows the detection unit to support appropriate responses according to the user's health condition.

[0073] The notification unit notifies the user of any abnormalities detected by the detection unit. The notification unit can, for example, send notifications via a smartphone app. Specifically, it sends push notifications to the smartphone app to inform the user of the abnormality. The notification unit can also send notifications via email. The email contains detailed information about the abnormality and recommended countermeasures, allowing the user to understand the situation by checking the email. The notification unit can also send notifications via SMS. SMS is an effective means of quickly notifying users of abnormalities in a short message format. For example, the notification unit can notify users of abnormal heart rates via a smartphone app. The app displays the abnormal heart rate and recommended countermeasures, allowing the user to understand the situation and take appropriate action by checking the app. The notification unit can also notify users of abnormal blood pressure via email. The email contains the abnormal blood pressure and recommended countermeasures, allowing the user to understand the situation by checking the email. The notification unit can also notify users of abnormal body temperature via SMS. SMS is an effective means of quickly notifying users of abnormalities in a short message format. This allows the notification unit to quickly and reliably notify users of anomalies and support them in taking appropriate action.

[0074] The service provider will provide appropriate health management advice based on the abnormalities notified by the notification unit. For example, the service provider may recommend exercise. Specifically, if an abnormality in heart rate is detected, the service provider will recommend appropriate exercise to the user. For example, light aerobic exercise or stretching can stabilize the heart rate. The service provider may also suggest dietary improvements. If an abnormality in blood pressure is detected, the service provider will suggest that the user reduce their salt intake. In addition, consuming more fruits and vegetables can stabilize blood pressure. Furthermore, the service provider may also recommend that the user see a doctor. If an abnormality in body temperature is detected, the service provider will recommend that the user see a doctor. This will allow the user to receive medical attention early and appropriate treatment. The service provider can provide individualized advice tailored to the user's health condition. For example, it may provide more specific advice considering the user's past data and lifestyle. This allows the service provider to support the user's health management and promote improvement in their health condition. Furthermore, the service provider can collect user feedback and continuously improve the content of the advice. This allows the service provider to offer users optimal health management advice and support them in maintaining and improving their health.

[0075] The data collection unit can collect the user's biometric data in real time. For example, the data collection unit can collect heart rate in real time using a wearable device. For example, the data collection unit can collect blood pressure in real time using a smartphone sensor. For example, the data collection unit can collect body temperature in real time using a medical device. This allows for the collection of the user's biometric data in real time, enabling an understanding of their latest health status. The specific definition of real time includes the frequency and delay time of data collection. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input heart rate data acquired from a wearable device into a generating AI and have the generating AI perform real-time data collection.

[0076] The analysis unit can analyze the collected data using machine learning. For example, the analysis unit can analyze heart rate data using deep learning. For example, the analysis unit can analyze blood pressure data using support vector machines. For example, the analysis unit can analyze body temperature data using random forests. This improves the accuracy of data analysis by using machine learning. Specific machine learning algorithms and techniques include deep learning, support vector machines, and random forests. Some or all of the above-mentioned processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform data analysis using machine learning.

[0077] The detection unit can detect anomalies based on the analyzed data. For example, the detection unit can detect deviations from the normal range in heart rate data. For example, the detection unit can detect specific patterns in blood pressure data. For example, the detection unit can detect the frequency of anomalies in body temperature data. This allows for the early detection of health abnormalities by detecting anomalies based on the analyzed data. Specific criteria and definitions of anomalies include deviations from the normal range and detection of specific patterns. Some or all of the above-described processes in the detection unit may be performed using AI or not. For example, the detection unit can input the analyzed data into a generating AI and have the generating AI perform anomaly detection.

[0078] The notification unit can immediately notify the user if an abnormality is detected. For example, the notification unit can notify of an abnormal heart rate via a smartphone app. For example, the notification unit can notify of an abnormal blood pressure via email. For example, the notification unit can notify of an abnormal body temperature via SMS. This enables a quick response by notifying immediately when an abnormality is detected. The specific definition of "immediately" includes the delay time and timing of the notification. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can have an AI generate notifications of abnormalities.

[0079] The service provider can provide appropriate health management advice based on the notified abnormality. For example, the service provider may recommend exercise in response to an abnormal heart rate. For example, the service provider may suggest dietary improvements in response to an abnormal blood pressure. For example, the service provider may recommend seeing a doctor in response to an abnormal body temperature. In this way, the service provider supports the user's health management by providing appropriate health management advice based on the notified abnormality. Specific examples of appropriate health management advice include recommendations for exercise, dietary improvements, and consultations with a doctor. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider may have an AI generate health management advice based on the abnormality.

[0080] The data collection unit can estimate the user's emotions and adjust the frequency of biometric data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the collection frequency to obtain more detailed data. For example, if the user is relaxed, the data collection unit can decrease the collection frequency to reduce the burden of data collection. For example, if the user is exercising, the data collection unit can appropriately adjust the collection frequency to obtain real-time data. This allows for more appropriate data collection by adjusting the frequency of biometric data collection according to the user's emotions. Specific methods and criteria for estimating emotions include facial recognition, voice analysis, and self-reporting. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotional data into a generating AI and have the generating AI adjust the collection frequency based on the emotions.

[0081] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can analyze the user's past heart rate data and determine the optimal placement of heart rate sensors. For example, the data collection unit can analyze the user's past sleep data and select the optimal sleep tracking method. For example, the data collection unit can analyze the user's past exercise data and propose the optimal exercise data collection method. In this way, the optimal data collection method can be selected by analyzing the user's past health data. Specific criteria and methods for the optimal data collection method include sensor placement and data collection frequency. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past health data into a generating AI and have the generating AI select the optimal data collection method.

[0082] The data collection unit can filter biometric data based on the user's current activity level. For example, if the user is exercising, the data collection unit can collect only data related to exercise. For example, if the user is resting, the data collection unit can collect only data related to rest. For example, if the user is working, the data collection unit can collect only data related to work. This allows for the collection of highly relevant data by filtering the data based on the user's current activity level. Specific criteria and methods for filtering include the type of activity level and the filtering algorithm. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input current activity level data into a generating AI and have the generating AI perform the filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize collecting stress-related data. For example, if the user is relaxed, the data collection unit can prioritize collecting relaxation-related data. For example, if the user is exercising, the data collection unit can prioritize collecting exercise-related data. This allows for the priority collection of important data by determining the priority of data to collect according to the user's emotions. Specific methods and criteria for estimating emotions include facial recognition, voice analysis, and self-reporting. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform the determination of data priority based on emotions.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting biometric data. For example, if the user is at high altitude, the data collection unit can prioritize the collection of biometric data related to high altitude. For example, if the user is at the beach, the data collection unit can prioritize the collection of biometric data related to the beach. For example, if the user is in an urban area, the data collection unit can prioritize the collection of biometric data related to the urban area. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Specific methods for acquiring and using geographical location information include GPS data and location information services. Some or all of the above-described processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0085] The data collection unit can analyze a user's social media activity and collect relevant data when collecting biometric data. For example, if a user is experiencing stress on social media, the data collection unit can collect stress-related biometric data. For example, if a user is relaxing on social media, the data collection unit can collect relaxation-related biometric data. For example, if a user is posting about exercise on social media, the data collection unit can collect exercise-related biometric data. In this way, relevant data can be collected by analyzing a user's social media activity. Specific methods and criteria for analyzing social media activity include post content, activity frequency, and follower count. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI collect relevant data.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can apply an algorithm that emphasizes stress-related data. For example, if the user is relaxed, the analysis unit can apply an algorithm that emphasizes relaxation-related data. For example, if the user is exercising, the analysis unit can apply an algorithm that emphasizes exercise-related data. By adjusting the analysis algorithm according to the user's emotions, more appropriate analysis becomes possible. Specific methods and criteria for estimating emotions include facial recognition, voice analysis, and self-reporting. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's emotion data into a generating AI and have the generating AI perform adjustments to the algorithm based on the emotions.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Specific evaluation criteria and methods for data importance include data type, frequency of occurrence, and impact. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data importance into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0088] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a heart rate analysis algorithm to heart rate data. For example, the analysis unit can apply a sleep analysis algorithm to sleep data. For example, the analysis unit can apply an exercise analysis algorithm to exercise data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Specific classification methods and criteria for data categories include health data, activity data, environmental data, etc. Some or all of the above-described processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an analysis algorithm according to the category.

[0089] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit may prioritize the analysis of stress-related data. For example, if the user is relaxed, the analysis unit may prioritize the analysis of relaxation-related data. For example, if the user is exercising, the analysis unit may prioritize the analysis of exercise-related data. This allows for the prioritization of important data by determining the priority of analysis according to the user's emotions. Specific methods and criteria for estimating emotions include facial recognition, voice analysis, and self-reporting. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform the determination of priority for analysis based on emotions.

[0090] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of recently collected data. For example, the analysis unit may postpone the analysis of previously collected data. For example, the analysis unit may prioritize the analysis of data collected during a specific period. This allows for the prioritization of the latest data by determining the priority of analysis based on the data collection period. Specific evaluation criteria and methods for the data collection period include the collection date and time, and the collection frequency. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the priority of analysis based on the collection period.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Specific evaluation criteria and methods for data relevance include correlation and causation. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order based on the relevance.

[0092] The detection unit can estimate the user's emotions and adjust the anomaly detection criteria based on the estimated user emotions. For example, if the user is stressed, the detection unit can tighten the stress-related anomaly detection criteria. For example, if the user is relaxed, the detection unit can loosen the relaxation-related anomaly detection criteria. For example, if the user is exercising, the detection unit can appropriately adjust the exercise-related anomaly detection criteria. This allows for more accurate anomaly detection by adjusting the anomaly detection criteria according to the user's emotions. Specific methods and criteria for estimating emotions include facial recognition, voice analysis, and self-reporting. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input user emotion data into a generating AI and have the generating AI perform the adjustment of the emotion-based anomaly detection criteria.

[0093] The detection unit can improve the accuracy of anomaly detection by considering the interrelationships between data during detection. For example, the detection unit can detect anomalies by considering the interrelationships between heart rate and blood pressure data. For example, the detection unit can detect anomalies by considering the interrelationships between sleep data and exercise data. For example, the detection unit can detect anomalies by considering the interrelationships between meal data and blood glucose level data. This improves the accuracy of anomaly detection by considering the interrelationships between data. Specific evaluation criteria and methods for data interrelationships include correlation and causation. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the interrelationships between data into a generating AI and have the generating AI perform an improvement in the accuracy of anomaly detection based on the interrelationships.

[0094] The detection unit can perform anomaly detection by considering the user's attribute information during detection. For example, the detection unit can detect anomalies by considering the user's age. For example, the detection unit can detect anomalies by considering the user's gender. For example, the detection unit can detect anomalies by considering the user's medical history. This makes it possible to perform more individualized anomaly detection by considering the user's attribute information. Specific methods for acquiring and using the user's attribute information include age, gender, occupation, etc. Some or all of the above processing in the detection unit may be performed using AI, or it may be performed without using AI. For example, the detection unit can input the user's attribute information into a generating AI and have the generating AI perform anomaly detection based on the attribute information.

[0095] The detection unit can estimate the user's emotions and adjust the order in which anomaly detection results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the detection unit can prioritize displaying stress-related anomalies. For example, if the user is relaxed, the detection unit can postpone displaying relaxation-related anomalies. For example, if the user is exercising, the detection unit can prioritize displaying exercise-related anomalies. By adjusting the order in which anomaly detection results are displayed according to the user's emotions, a display that is easy for the user to understand becomes possible. Specific methods and criteria for estimating emotions include facial recognition, voice analysis, and self-reporting. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input the user's emotion data into a generating AI and have the generating AI perform the adjustment of the display order based on emotions.

[0096] The detection unit can perform anomaly detection while considering the geographical distribution of the data. For example, if the user is in a high-altitude area, the detection unit can detect anomalies related to high altitude. For example, if the user is on a beach, the detection unit can detect anomalies related to the beach. For example, if the user is in an urban area, the detection unit can detect anomalies related to urban areas. In this way, by considering the geographical distribution of the data, region-specific anomalies can be detected. Specific evaluation criteria and methods for geographical distribution include the data distribution for each region and geographical characteristics. Some or all of the above processing in the detection unit may be performed using AI, or it may be performed without AI. For example, the detection unit can input geographical distribution data into a generating AI and have the generating AI perform anomaly detection based on geographical distribution.

[0097] The detection unit can improve the accuracy of anomaly detection by referring to relevant literature during detection. For example, the detection unit can update the criteria for anomaly detection by referring to the latest medical literature. For example, the detection unit can improve the anomaly detection algorithm by referring to relevant research papers. For example, the detection unit can improve the accuracy of anomaly detection by referring to medical guidelines. As a result, the accuracy of anomaly detection is improved by referring to relevant literature. Specific methods and criteria for referring to relevant literature include literature databases and citation standards. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input relevant literature data into a generating AI and have the generating AI perform literature-based anomaly detection accuracy improvement.

[0098] The notification unit can estimate the user's emotions and adjust the way notifications are expressed based on those emotions. For example, if the user is stressed, the notification unit can use a calm expression. If the user is relaxed, the notification unit can use a cheerful expression. If the user is in a hurry, the notification unit can use a concise expression. By adjusting the way notifications are expressed according to the user's emotions, it becomes possible to provide notifications that are more acceptable to the user. Specific methods and criteria for estimating emotions include facial recognition, voice analysis, and self-reporting. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generating AI and have the generating AI adjust the way notifications are expressed based on those emotions.

[0099] The notification unit can adjust the level of detail of the notification based on the severity of the anomaly. For example, the notification unit can provide detailed notifications for highly severe anomalies. For example, it can provide simplified notifications for less severe anomalies. For example, it can provide notifications with an appropriate level of detail for moderately severe anomalies. By adjusting the level of detail of the notification based on the severity of the anomaly, appropriate notifications can be provided for important anomalies. Specific evaluation criteria and methods for the severity of anomalies include the degree of impact and frequency of occurrence of the anomaly. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the severity of the anomaly into a generating AI and have the generating AI perform the adjustment of the level of detail of the notification based on the severity.

[0100] The notification unit can apply different notification methods depending on the category of the anomaly when it issues a notification. For example, the notification unit can provide an audio notification for an abnormal heart rate. For example, it can provide a text notification for an abnormal blood pressure. For example, it can provide a visual notification for an abnormal sleep. By applying different notification methods depending on the category of the anomaly, appropriate notifications can be provided according to the content of the anomaly. Specific classification methods and criteria for the categories of anomalies include health anomalies, environmental anomalies, etc. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the category of the anomaly into a generating AI and have the generating AI execute the application of a notification method according to the category.

[0101] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is stressed, the notification unit can delay the notification. For example, if the user is relaxed, the notification unit can speed up the notification. For example, if the user is in a hurry, the notification unit can deliver the notification immediately. By adjusting the timing of notifications according to the user's emotions, notifications can be delivered at the optimal time for the user. Specific methods and criteria for estimating emotions include facial recognition, voice analysis, and self-reporting. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generating AI and have the generating AI adjust the timing of notifications based on those emotions.

[0102] The notification unit can determine the priority of notifications based on when the anomaly occurred. For example, the notification unit may prioritize notifications for recently occurring anomalies. For example, the notification unit may postpone notifications for anomalies that occurred in the past. For example, the notification unit may prioritize notifications for anomalies that occurred during a specific period. This allows for priority notification of the latest anomalies by determining the priority of notifications based on when the anomaly occurred. Specific evaluation criteria and methods for determining the timing of an anomaly include the date and time of occurrence and frequency of occurrence. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the timing of the anomaly into a generating AI and have the generating AI determine the priority of notifications based on the timing of occurrence.

[0103] The notification unit can adjust the order of notifications based on the relevance of the anomalies. For example, the notification unit may prioritize notifying of highly relevant anomalies. For example, the notification unit may postpone notifying of less relevant anomalies. For example, the notification unit can dynamically adjust the order of notifications according to the relevance of the anomalies. This allows important anomalies to be notified preferentially by adjusting the order of notifications based on the relevance of the anomalies. Specific evaluation criteria and methods for the relevance of anomalies include correlation and causation. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the relevance of anomalies into a generating AI and have the generating AI perform the adjustment of the notification order based on the relevance.

[0104] The service provider can estimate the user's emotions and adjust the content of health management advice based on the estimated emotions. For example, if the user is feeling stressed, the service provider can provide stress reduction advice. For example, if the user is relaxed, the service provider can provide advice to maintain that relaxation. For example, if the user is exercising, the service provider can provide advice to enhance the effects of the exercise. By adjusting the content of health management advice according to the user's emotions, more appropriate advice can be provided. Specific methods and criteria for estimating emotions include facial recognition, voice analysis, and self-reporting. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's emotion data into a generating AI and have the generating AI adjust the content of the advice based on the emotions.

[0105] The service provider can provide optimal advice by referring to the user's past health data when providing advice. For example, the service provider can provide advice on heart rate management by referring to the user's past heart rate data. For example, the service provider can provide advice on improving sleep by referring to the user's past sleep data. For example, the service provider can provide advice on enhancing the effects of exercise by referring to the user's past exercise data. In this way, optimal advice can be provided by referring to the user's past health data. Specific criteria and methods for optimal advice include personalized advice and advice based on past data. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input past health data into a generating AI and have the generating AI perform the task of providing optimal advice based on the data.

[0106] The service provider can customize the means of providing advice based on the user's current lifestyle. For example, if the user is busy, the service provider can provide advice that can be implemented in a short amount of time. If the user is relaxed, the service provider can provide advice to help them maintain that relaxation. If the user is exercising, the service provider can provide advice to enhance the effects of the exercise. By customizing the means of advice based on the user's current lifestyle, more actionable advice can be provided. Specific evaluation criteria and methods for the current lifestyle include lifestyle habits and activity levels. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input current lifestyle data into a generating AI and have the generating AI customize the means of advice based on the situation.

[0107] The service provider can estimate the user's emotions and determine the priority of advice based on those emotions. For example, if the user is feeling stressed, the service provider may prioritize stress-reducing advice. If the user is relaxed, the service provider may prioritize advice to maintain that relaxation. If the user is exercising, the service provider may prioritize advice to enhance the effects of the exercise. This allows for the priority of important advice by determining the priority of advice according to the user's emotions. Specific methods and criteria for estimating emotions include facial recognition, voice analysis, and self-reporting. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generating AI and have the generating AI determine the priority of emotion-based advice.

[0108] The service provider can provide optimal advice by considering the user's geographical location when providing advice. For example, if the user is at high altitude, the service provider can provide health management advice suitable for high altitude. For example, if the user is at the beach, the service provider can provide health management advice suitable for the beach. For example, if the user is in an urban area, the service provider can provide health management advice suitable for the urban area. In this way, optimal advice can be provided by considering the user's geographical location. Specific methods for acquiring and using geographical location information include GPS data and location information services. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input geographical location data into a generating AI and have the generating AI perform the task of providing optimal advice based on location information.

[0109] The service provider can analyze a user's social media activity and suggest methods for providing advice. For example, if a user is experiencing stress on social media, the service provider can offer advice on stress reduction. If a user is relaxing on social media, the service provider can offer advice on maintaining that relaxation. If a user is posting about exercise on social media, the service provider can offer advice on enhancing the effectiveness of their exercise. By analyzing a user's social media activity, the service provider can suggest more appropriate methods for providing advice. Specific methods and criteria for analyzing social media activity include post content, activity frequency, and follower count. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input social media activity data into a generating AI and have the generating AI suggest methods for providing advice based on that activity.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is stressed, the analysis of stress-related data can be prioritized. If the user is relaxed, the analysis of relaxation-related data can be prioritized. If the user is exercising, the analysis of exercise-related data can be prioritized. This allows important data to be analyzed preferentially by determining the priority of analysis according to the user's emotions. Specific methods and criteria for estimating emotions include facial recognition, voice analysis, and self-reporting. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's emotion data into a generating AI and have the generating AI perform the determination of analysis priorities based on emotions.

[0112] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user is at high altitude, biometric data related to high altitude can be prioritized. If the user is at the beach, biometric data related to the beach can be prioritized. If the user is in an urban area, biometric data related to the urban area can be prioritized. In this way, by considering the user's geographical location, highly relevant data can be prioritized for collection. Specific methods for acquiring and using geographical location information include GPS data and location information services. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0113] The notification unit can estimate the user's emotions and adjust the way notifications are presented based on those emotions. For example, if the user is stressed, the notification can be presented in a calm manner. If the user is relaxed, the notification can be presented in a cheerful manner. If the user is in a hurry, the notification can be presented in a concise manner. By adjusting the way notifications are presented according to the user's emotions, it becomes possible to provide notifications that are more acceptable to the user. Specific methods and criteria for estimating emotions include facial recognition, voice analysis, and self-reporting. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generating AI and have the generating AI adjust the way notifications are presented based on those emotions.

[0114] The service provider can provide optimal advice by referring to the user's past health data when providing advice. For example, it can provide advice on heart rate management by referring to the user's past heart rate data. It can provide advice on improving sleep by referring to the user's past sleep data. It can provide advice on enhancing the effects of exercise by referring to the user's past exercise data. In this way, optimal advice can be provided by referring to the user's past health data. Specific criteria and methods for optimal advice include personalized advice and advice based on past data. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input past health data into a generating AI and have the generating AI perform the task of providing optimal advice based on the data.

[0115] The detection unit can estimate the user's emotions and adjust the anomaly detection criteria based on the estimated user emotions. For example, if the user is stressed, the criteria for detecting stress-related anomalies can be made stricter. If the user is relaxed, the criteria for detecting relaxation-related anomalies can be made looser. If the user is exercising, the criteria for detecting exercise-related anomalies can be appropriately adjusted. This allows for more accurate anomaly detection by adjusting the criteria according to the user's emotions. Specific methods and criteria for estimating emotions include facial recognition, voice analysis, and self-reporting. Some or all of the above-described processes in the detection unit may be performed using AI or not. For example, the detection unit can input user emotion data into a generating AI and have the generating AI perform the adjustment of the anomaly detection criteria based on emotions.

[0116] The data collection unit can analyze a user's social media activity and collect relevant data. For example, if a user is experiencing stress on social media, it can collect biometric data related to stress. If a user is relaxing on social media, it can collect biometric data related to relaxation. If a user is posting about exercise on social media, it can collect biometric data related to exercise. In this way, relevant data can be collected by analyzing a user's social media activity. Specific methods and criteria for analyzing social media activity include post content, activity frequency, and follower count. Some or all of the above-described processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI collect relevant data.

[0117] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on highly important data, a simplified analysis on less important data, and an analysis with an appropriate level of detail on moderately important data. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Specific evaluation criteria and methods for data importance include data type, frequency of occurrence, and impact. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the data importance into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0118] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is stressed, the notification timing can be delayed. If the user is relaxed, the notification timing can be advanced. If the user is in a hurry, the notification timing can be immediate. By adjusting the notification timing according to the user's emotions, notifications can be delivered at the optimal time for the user. Specific methods and criteria for estimating emotions include facial recognition, voice analysis, and self-reporting. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generating AI and have the generating AI perform the emotion-based adjustment of notification timing.

[0119] The service provider can customize the means of providing advice based on the user's current lifestyle. For example, if the user is busy, it can provide advice that can be implemented in a short amount of time. If the user is relaxed, it can provide advice to help them maintain that relaxation. If the user is exercising, it can provide advice to enhance the effects of their exercise. By customizing the means of advice based on the user's current lifestyle, more actionable advice can be provided. Specific evaluation criteria and methods for the current lifestyle include lifestyle habits and activity levels. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input current lifestyle data into a generating AI and have the generating AI customize the means of advice based on the situation.

[0120] The service provider can estimate the user's emotions and adjust the content of health management advice based on the estimated emotions. For example, if the user is feeling stressed, it can provide advice on stress reduction. If the user is relaxed, it can provide advice on maintaining that relaxation. If the user is exercising, it can provide advice on enhancing the effects of the exercise. By adjusting the content of health management advice according to the user's emotions, more appropriate advice can be provided. Specific methods and criteria for estimating emotions include facial recognition, voice analysis, and self-reporting. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generating AI and have the generating AI adjust the content of the advice based on the emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects the user's biometric data. The data collection unit collects data such as heart rate, blood pressure, and body temperature. The data collection unit can collect biometric data using wearable devices, smartphone sensors, and medical devices. For example, the data collection unit can monitor heart rate in real time using a wearable device, measure blood pressure using a smartphone sensor, and measure body temperature using a medical device. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using methods such as machine learning, deep learning, support vector machines, and random forests. For example, the analysis unit can analyze heart rate data using deep learning, blood pressure data using support vector machines, and body temperature data using random forests. Step 3: The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit can detect deviations from the normal range, specific patterns, and the frequency of anomalies. For example, the detection unit can detect deviations from the normal range in heart rate data, specific patterns in blood pressure data, and the frequency of anomalies in body temperature data. Step 4: The notification unit notifies the user of any abnormalities detected by the detection unit. The notification unit can send notifications via smartphone apps, email, SMS, etc. For example, the notification unit can notify of abnormal heart rate via a smartphone app, abnormal blood pressure via email, and abnormal body temperature via SMS. Step 5: The service provider provides appropriate health management advice based on the abnormalities notified by the notification provider. The service provider may recommend exercise, suggest dietary improvements, or advise the user to see a doctor. For example, the service provider may recommend exercise for abnormal heart rate, suggest dietary improvements for abnormal blood pressure, or advise the user to see a doctor for abnormal body temperature.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, notification unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects biometric data using sensors or wearable devices of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and detects abnormalities based on the analyzed data. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies the user of the abnormality. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides appropriate health management advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, notification unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects biometric data using the sensors of the smart glasses 214 or a wearable device. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data using machine learning. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects abnormalities based on the analyzed data. The notification unit is implemented by the control unit 46A of the smart glasses 214 and notifies the user of the abnormality. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides appropriate health management advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, notification unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects biometric data using sensors or wearable devices of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and detects abnormalities based on the analyzed data. The notification unit is implemented by the control unit 46A of the headset terminal 314 and notifies the user of the abnormality. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides appropriate health management advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, notification unit, and provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects biometric data using sensors and wearable devices of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and detects abnormalities based on the analyzed data. The notification unit is implemented by the control unit 46A of the robot 414 and notifies the user of the abnormality. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides appropriate health management advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A collection unit that collects the user's biometric data, An analysis unit analyzes the data collected by the aforementioned collection unit, A detection unit that detects anomalies based on the data analyzed by the analysis unit, A notification unit that notifies the user of any abnormalities detected by the detection unit, The system includes a provisioning unit that provides appropriate health management advice based on the abnormality notified by the notification unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect user biometric data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed using machine learning. The system described in Appendix 1, characterized by the features described herein. (Note 4) The detection unit is Detect anomalies based on analyzed data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, If an anomaly is detected, the user will be notified immediately. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide appropriate health management advice based on the reported abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of biometric data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting biometric data, filtering is performed based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting biometric data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting biometric data, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit is The system estimates the user's emotions and adjusts the anomaly detection criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit is When detecting anomalies, the accuracy of the detection is improved by considering the interrelationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit is When detecting anomalies, the system takes user attribute information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit is It estimates the user's emotions and adjusts the order in which anomaly detection results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The detection unit is Anomaly detection is performed considering the geographical distribution of the data during detection. The system described in Appendix 1, characterized by the features described herein. (Note 24) The detection unit is During detection, we improve the accuracy of anomaly detection by referring to relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, When a notification is sent, adjust the level of detail in the notification based on the severity of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When a notification is sent, different notification methods will be applied depending on the category of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When a notification is sent, the notification priority is determined based on when the anomaly occurred. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending notifications, adjust the order of notifications based on the relevance of the anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the content of health management advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing advice, we refer to the user's past health data to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing advice, customize the method of advice based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing advice, we analyze the user's social media activity and suggest methods for providing advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects the user's biometric data, An analysis unit analyzes the data collected by the aforementioned collection unit, A detection unit that detects anomalies based on the data analyzed by the analysis unit, A notification unit that notifies the user of any abnormalities detected by the detection unit, The system includes a provisioning unit that provides appropriate health management advice based on the abnormality notified by the notification unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect user biometric data in real time. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed using machine learning. The system according to feature 1.

4. The detection unit is Detect anomalies based on analyzed data. The system according to feature 1.

5. The aforementioned notification unit, If an anomaly is detected, the user will be notified immediately. The system according to feature 1.

6. The aforementioned supply unit is, Provide appropriate health management advice based on the reported abnormalities. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of biometric data collection based on the estimated emotions. The system according to feature 1.

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

9. The aforementioned collection unit is When collecting biometric data, filtering is performed based on the user's current activity status. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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