system

Abstract

The system according to this embodiment aims to detect abnormalities when elderly or disabled people are alone and to respond quickly. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a notification unit. The collection unit collects activity levels and health data. The analysis unit analyzes the data collected by the collection unit in real time and detects abnormalities. The notification unit makes an emergency notification to a designated contact based on the abnormality detected by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 prior art, there is a problem that it is difficult to respond quickly when an accident occurs when an elderly person or a person with a disability is alone.

[0005] The system according to the embodiment aims to detect an abnormality and respond quickly when an elderly person or a person with a disability is alone.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a notification unit. The collection unit collects activity amounts and health data. The analysis unit analyzes the data collected by the collection unit in real time and detects an abnormality. The notification unit makes an emergency notification to a designated contact based on the abnormality detected by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect abnormalities when elderly or disabled people are alone and respond quickly. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI-equipped wearable terminal system according to an embodiment of the present invention is a system that reduces anxiety for the elderly and disabled when they are alone and enables a rapid response in the event of an accident or illness. This system uses an AI-equipped wearable terminal to collect daily data, analyze the collected data in real time, and learn the user's health status and behavioral patterns. If an abnormality is detected, an emergency call is automatically made to a designated contact. This mechanism provides peace of mind to the user and their family and enables a rapid response. For example, the AI-equipped wearable terminal collects daily data. At this time, activity levels and health data (e.g., heart rate, blood pressure, body temperature, etc.) are collected. This allows the system to understand the user's health status and behavioral patterns. Next, the collected data is analyzed in real time. The AI ​​analyzes the collected data and detects abnormal values ​​and irregular behavior. For example, if an abnormality is detected, such as a sudden increase in heart rate or prolonged immobility, an immediate response can be taken. If an abnormality is detected, an emergency call is automatically made to a designated contact. For example, the system notifies the user's family or medical institution that an abnormality has occurred. This enables a rapid response, ensuring user safety. Furthermore, continuous data feedback allows the AI ​​to deepen its learning. This enables more accurate anomaly detection, improving user health management. This system reduces anxiety for the elderly and disabled when they are alone and allows for a quick response in the event of an accident or illness. It provides peace of mind to users and their families and improves their quality of life. It also offers a new approach to protecting the elderly as a whole society, as an important solution in an aging society. As a result, the AI-powered wearable terminal system can monitor the user's health status in real time and respond quickly when an anomaly occurs.

[0029] The AI-equipped wearable terminal system according to this embodiment comprises a data collection unit, an analysis unit, and a notification unit. The data collection unit collects activity levels and health data. For example, the data collection unit collects health data such as heart rate, blood pressure, and body temperature. The data collection unit can also collect activity level data such as steps taken, exercise time, and calories burned. For example, the data collection unit collects the user's health data in real time using sensors mounted on the wearable terminal. The analysis unit analyzes the data collected by the data collection unit in real time and detects abnormalities. For example, the analysis unit analyzes the collected data and detects abnormal values ​​and irregular behavior. For example, the analysis unit can respond immediately when an abnormality is detected, such as when the heart rate suddenly increases or when the user remains motionless for a long period of time. For example, the analysis unit can also use AI to analyze the collected data and detect abnormalities. The notification unit makes an emergency call to a designated contact based on the abnormality detected by the analysis unit. For example, when an abnormality is detected, the notification unit notifies the user's family or medical institution that an abnormality has occurred. The notification unit can make emergency calls using means such as telephone, email, or SMS. The notification unit can also automatically make emergency calls when an abnormality is detected, for example, using AI. As a result, the AI-equipped wearable terminal system according to this embodiment can reduce anxiety for the elderly and disabled when they are alone and enable a quick response in the event of an accident or illness.

[0030] The data collection unit collects activity levels and health data. For example, it collects health data such as heart rate, blood pressure, and body temperature. Specifically, a heart rate sensor mounted on the wearable device monitors the user's heart rate in real time and detects abnormal heart rate patterns. A blood pressure sensor periodically measures the user's blood pressure and detects abnormal blood pressure fluctuations. A body temperature sensor continuously measures the user's body temperature to detect early signs of fever or hypothermia. These sensors can collect accurate data by directly contacting the user's skin. Furthermore, the data collection unit can also collect activity data such as steps taken, exercise time, and calories burned. A pedometer detects the user's walking motion and counts the daily steps. An accelerometer measures the user's exercise intensity and duration and calculates calories burned. This allows the data collection unit to comprehensively understand the user's health status and activity level. The collected data is stored in the wearable device's memory and periodically uploaded to a cloud server. The data stored on the cloud server is made accessible to the analysis and notification units (described later), strengthening the overall system coordination. The data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to user needs and situations. For example, by increasing the data collection frequency during exercise and decreasing it during rest, it is possible to reliably collect necessary data while conserving battery power.

[0031] The analysis department analyzes data collected by the data collection department in real time to detect anomalies. For example, the analysis department analyzes collected data to detect abnormal values ​​and irregular behavior. Specifically, it uses AI to analyze data such as heart rate, blood pressure, and body temperature to detect abnormal values ​​that exceed the normal range. For example, it can respond immediately when an anomaly is detected, such as when heart rate suddenly increases, blood pressure is abnormally high or low, or body temperature fluctuates rapidly. Based on past data and statistical information, the AI ​​learns normal and abnormal patterns and builds an algorithm to detect anomalies in real time. This allows the analysis department to quickly and accurately analyze collected data and detect anomalies early. Furthermore, the analysis department can also learn the user's behavior patterns and lifestyle habits to detect abnormal behavior. For example, if a user remains motionless for a long period of time or exhibits behavior different from their normal activity pattern, it can detect an anomaly and take appropriate action. When an anomaly is detected, the analysis department immediately notifies the reporting department to prompt emergency response. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The notification unit makes emergency calls to designated contacts based on anomalies detected by the analysis unit. Specifically, when an anomaly is detected, it notifies the user's family or medical institution of the occurrence of the anomaly. The notification unit can make emergency calls using methods such as telephone, email, and SMS. In the case of telephone calls, an automated voice message is sent to pre-registered contacts, explaining the nature of the anomaly and the current situation. In the case of email calls, details of the anomaly and collected data are attached and sent to encourage a quick response. In the case of SMS calls, a short message notifies the occurrence of an anomaly and encourages emergency contact. Furthermore, the notification unit can also use AI to automatically make emergency calls when an anomaly is detected. The AI ​​selects the most appropriate notification method according to the type and urgency of the anomaly, and takes a quick and appropriate response. For example, if the heart rate suddenly increases and the user loses consciousness, the AI ​​will immediately make an emergency call and prompt the dispatch of an ambulance. The notification unit can also obtain the user's location information and notify the current location when making an emergency call. This allows paramedics and family members to quickly reach the scene. The notification unit also implements security measures to ensure that necessary information is reliably transmitted while protecting the user's privacy. This allows the reporting department to ensure user safety and support a swift and appropriate response in emergencies.

[0033] The data collection unit can collect health data such as heart rate, blood pressure, and body temperature. For example, the data collection unit can collect the user's heart rate in real time using a heart rate measuring device. The data collection unit can also collect the user's blood pressure in real time using a blood pressure measuring device. The data collection unit can also collect the user's body temperature in real time using a body temperature measuring device. By collecting health data in this way, the user's health status can be understood. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect health data such as heart rate, blood pressure, and body temperature, input this data into AI, and the AI ​​can analyze the data to detect abnormalities.

[0034] The analysis unit can analyze the collected data and detect anomalies and irregular behavior. For example, the analysis unit can analyze the collected data and detect anomalies such as a sudden increase in heart rate or prolonged immobility. The analysis unit can also use AI to analyze the collected data and detect anomalies and irregular behavior. For example, the analysis unit can perform analysis using algorithms that detect anomalies and irregular behavior based on the collected data. This enables a rapid response by detecting anomalies and irregular behavior. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the collected data into an AI, which can analyze the data and detect anomalies.

[0035] The notification unit can notify the user's family or medical institution of an abnormality when an abnormality is detected. For example, the notification unit can notify the user's family or medical institution of an abnormality by telephone when an abnormality is detected. The notification unit can also notify the user's family or medical institution of an abnormality by email when an abnormality is detected. The notification unit can also notify the user's family or medical institution of an abnormality by SMS when an abnormality is detected. This ensures the user's safety by providing prompt notification when an abnormality occurs. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, when an abnormality is detected, the AI ​​can automatically make an emergency call.

[0036] AI-powered wearable terminal systems further include a feedback unit that allows the AI ​​to deepen its learning through continuous data feedback. The feedback unit allows the AI ​​to deepen its learning based on the collected data, enabling more accurate anomaly detection. For example, the feedback unit inputs collected data into the AI, which then analyzes the data to deepen its learning. The feedback unit can also, for example, allow the AI ​​to improve its anomaly detection algorithm based on the collected data. The feedback unit can also, for example, build a feedback loop to improve the accuracy of anomaly detection based on the collected data. This allows the AI ​​to deepen its learning, enabling more accurate anomaly detection. Some or all of the above-described processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input collected data into the AI, which then analyzes the data to deepen its learning.

[0037] The feedback unit allows the AI ​​to deepen its learning based on the collected data, enabling more accurate anomaly detection. For example, the feedback unit inputs the collected data into the AI, which then analyzes the data to deepen its learning. The feedback unit can also, for example, allow the AI ​​to improve its anomaly detection algorithm based on the collected data. The feedback unit can also, for example, build a feedback loop to improve the accuracy of anomaly detection based on the collected data. This improves the accuracy of anomaly detection as the AI ​​deepens its learning based on the collected data. Some or all of the above-described processes in the feedback unit may be performed using the AI, or not using the AI. For example, the feedback unit can input the collected data into the AI, which then analyzes the data to deepen its learning.

[0038] 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 detect from the user's past data that abnormalities are more likely to occur during specific time periods and focus data collection during those times. For example, the data collection unit can detect from the user's past data that abnormalities are more likely to occur during specific activities and collect detailed data during those activities. For example, the data collection unit can detect from the user's past data that abnormalities are more likely to occur under specific environmental conditions and collect data under those environmental conditions. By analyzing past health data, the optimal data collection method can be selected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into AI, which can then analyze the data and select the optimal data collection method.

[0039] The data collection unit can filter health data based on the user's current activity level and environment. For example, if the user is exercising, the data collection unit will prioritize collecting data related to exercise. For example, if the user is resting, the data collection unit can prioritize collecting data related to resting. For example, if the user is out, the data collection unit can prioritize collecting data related to going out. This allows for the collection of more relevant data by filtering the data based on the user's current activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activity level and environment data into the AI, which can then analyze and filter the data.

[0040] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information when collecting health data. For example, if the user is at home, the data collection unit can prioritize the collection of data related to activities at home. For example, if the user is out, the data collection unit can prioritize the collection of data related to activities at the location. For example, if the user is at a medical institution, the data collection unit can prioritize the collection of data related to activities at the medical institution. This enables more accurate data collection by collecting highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, and the AI ​​can analyze the data and prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user is experiencing stress on social media, the data collection unit can prioritize collecting stress-related data. For example, if a user is experiencing relaxation on social media, the data collection unit can also prioritize collecting relaxation-related data. For example, if a user is experiencing anxiety on social media, the data collection unit can also prioritize collecting anxiety-related data. In this way, relevant data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, which can then analyze the data and collect relevant data.

[0042] The analysis unit can improve the accuracy of anomaly detection based on the interrelationships of health data during analysis. For example, the analysis unit can detect anomalies by considering the correlation between heart rate and blood pressure. The analysis unit can also detect anomalies by considering the correlation between body temperature and activity level. The analysis unit can also detect anomalies by considering the correlation between respiratory rate and sleep data. By improving the accuracy of anomaly detection based on the interrelationships of health data, more accurate anomaly detection becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships of health data into AI, and the AI ​​can analyze the data to detect anomalies.

[0043] The analysis unit can perform anomaly detection based on user attribute information during analysis. For example, the analysis unit can detect anomalies by considering the user's age. The analysis unit can also detect anomalies by considering the user's gender. The analysis unit can also detect anomalies by considering the user's medical history. This allows for more personalized anomaly detection by performing anomaly detection based on user attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information into AI, and the AI ​​can analyze the data to detect anomalies.

[0044] The analysis unit can perform anomaly detection based on the geographical distribution of health data during analysis. For example, if a user is in a specific region, the analysis unit can detect anomalies by considering the health risks of that region. For example, if a user is traveling, the analysis unit can also detect anomalies by considering the health risks of the travel destination. For example, if a user is at home, the analysis unit can also detect anomalies by considering the environmental risks of the home. This allows for more accurate anomaly detection by performing anomaly detection based on the geographical distribution of health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution of health data into AI, and the AI ​​can analyze the data and detect anomalies.

[0045] The analysis unit can improve the accuracy of anomaly detection based on relevant literature during analysis. For example, the analysis unit can update the criteria for anomaly detection by referring to the latest medical literature. The analysis unit can also improve the anomaly detection algorithm by referring to past research data. The analysis unit can also improve the accuracy of anomaly detection by referring to relevant academic papers. By improving the accuracy of anomaly detection based on relevant literature, more accurate anomaly detection becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input relevant literature into AI, and the AI ​​can analyze the data to improve the accuracy of anomaly detection.

[0046] The reporting unit can adjust the level of detail in a report based on the severity of the anomaly. For example, if a serious anomaly is detected, the reporting unit will issue a report containing detailed information. For example, if a minor anomaly is detected, the reporting unit may issue a concise report. The reporting unit can also adjust the level of detail in a report in stages according to the severity of the anomaly. This allows for the provision of more appropriate information by adjusting the level of detail in the report according to the severity of the anomaly. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input anomaly severity data into AI, and the AI ​​can analyze the data to adjust the level of detail in the report.

[0047] The notification unit can apply different notification algorithms depending on the category of the anomaly when a notification is made. For example, if an abnormality in heart rate is detected, the notification unit can apply a notification algorithm specifically for heart rate. For example, if an abnormality in blood pressure is detected, the notification unit can also apply a notification algorithm specifically for blood pressure. For example, if an abnormality in body temperature is detected, the notification unit can also apply a notification algorithm specifically for body temperature. This allows for more appropriate notifications by applying a notification algorithm according to the category of the anomaly. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly category data into AI, and the AI ​​can analyze the data and apply a different notification algorithm.

[0048] The reporting unit can determine the priority of reports based on when the anomaly occurred. For example, the reporting unit may report immediately after the anomaly occurs. For example, the reporting unit may also prioritize reporting if the anomaly is ongoing. For example, the reporting unit may also adjust the priority of reports in stages according to when the anomaly occurred. This allows for a faster response by determining the priority of reports based on when the anomaly occurred. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input data on when the anomaly occurred into the AI, and the AI ​​can analyze the data to determine the priority of reports.

[0049] The notification unit can adjust the order of notifications based on the relevance of the anomalies when a notification is made. For example, if multiple anomalies are detected, the notification unit will prioritize notifying the most important anomaly. The notification unit can also adjust the order of notifications in stages according to the relevance of the anomalies. The notification unit can also optimize the order of notifications by considering the relevance of the anomalies. This allows for the provision of more appropriate information by adjusting the order of notifications based on the relevance of the anomalies. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly relevance data into AI, and the AI ​​can analyze the data to adjust the order of notifications.

[0050] The feedback unit can optimize the learning algorithm based on past learning data during the learning process. For example, the feedback unit can select the most effective learning algorithm from past learning data. The feedback unit can also adjust the parameters of the learning algorithm based on past learning data. For example, the feedback unit can improve the accuracy of the learning algorithm by referring to past learning data. As a result, the accuracy of learning is improved by optimizing the learning algorithm based on past learning data. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input past learning data into AI, and the AI ​​can analyze the data to optimize the learning algorithm.

[0051] The feedback unit can weight the training data during learning based on when the health data was collected. For example, the feedback unit may assign a higher weight to data collected recently, or a lower weight to data collected older. The feedback unit can also adjust the weighting of the training data in stages according to the timing of the health data collection. This allows for more accurate learning by weighting the training data based on the timing of the health data collection. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input health data collection timing data into the AI, which can then analyze the data and weight the training data.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] 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 detect from the user's past data that abnormalities are more likely to occur during specific time periods and focus data collection during those times. For example, the data collection unit can detect from the user's past data that abnormalities are more likely to occur during specific activities and collect detailed data during those activities. For example, the data collection unit can detect from the user's past data that abnormalities are more likely to occur under specific environmental conditions and collect data under those environmental conditions. By analyzing past health data, the optimal data collection method can be selected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into AI, which can then analyze the data and select the optimal data collection method.

[0054] The data collection unit can filter health data based on the user's current activity level and environment. For example, if the user is exercising, the data collection unit will prioritize collecting data related to exercise. For example, if the user is resting, the data collection unit can prioritize collecting data related to resting. For example, if the user is out, the data collection unit can prioritize collecting data related to going out. This allows for the collection of more relevant data by filtering the data based on the user's current activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activity level and environment data into the AI, which can then analyze and filter the data.

[0055] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information when collecting health data. For example, if the user is at home, the data collection unit can prioritize the collection of data related to activities at home. For example, if the user is out, the data collection unit can prioritize the collection of data related to activities at the location. For example, if the user is at a medical institution, the data collection unit can prioritize the collection of data related to activities at the medical institution. This enables more accurate data collection by collecting highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, and the AI ​​can analyze the data and prioritize the collection of highly relevant data.

[0056] The analysis unit can improve the accuracy of anomaly detection based on the interrelationships of health data during analysis. For example, the analysis unit can detect anomalies by considering the correlation between heart rate and blood pressure. The analysis unit can also detect anomalies by considering the correlation between body temperature and activity level. The analysis unit can also detect anomalies by considering the correlation between respiratory rate and sleep data. By improving the accuracy of anomaly detection based on the interrelationships of health data, more accurate anomaly detection becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships of health data into AI, and the AI ​​can analyze the data to detect anomalies.

[0057] The analysis unit can perform anomaly detection based on user attribute information during analysis. For example, the analysis unit can detect anomalies by considering the user's age. The analysis unit can also detect anomalies by considering the user's gender. The analysis unit can also detect anomalies by considering the user's medical history. This allows for more personalized anomaly detection by performing anomaly detection based on user attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information into AI, and the AI ​​can analyze the data to detect anomalies.

[0058] The analysis unit can perform anomaly detection based on the geographical distribution of health data during analysis. For example, if a user is in a specific region, the analysis unit can detect anomalies by considering the health risks of that region. For example, if a user is traveling, the analysis unit can also detect anomalies by considering the health risks of the travel destination. For example, if a user is at home, the analysis unit can also detect anomalies by considering the environmental risks of the home. This allows for more accurate anomaly detection by performing anomaly detection based on the geographical distribution of health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution of health data into AI, and the AI ​​can analyze the data and detect anomalies.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects activity levels and health data. For example, it can collect health data such as heart rate, blood pressure, and body temperature, as well as activity data such as steps taken, exercise time, and calories burned. The data collection unit uses sensors mounted on the wearable device to collect the user's health data in real time. Step 2: The analysis unit analyzes the data collected by the collection unit in real time and detects anomalies. For example, it analyzes the collected data to detect abnormal values ​​or irregular behavior. It can respond immediately if an anomaly is detected, such as a sudden increase in heart rate or prolonged immobility. AI can also be used to analyze the collected data and detect anomalies. Step 3: The notification unit makes an emergency call to the designated contacts based on the anomaly detected by the analysis unit. For example, if an anomaly is detected, it will notify the user's family or medical institution that an anomaly has occurred. Emergency calls can be made using methods such as phone, email, and SMS. It is also possible to use AI to automatically make an emergency call when an anomaly is detected.

[0061] (Example of form 2) The AI-equipped wearable terminal system according to an embodiment of the present invention is a system that reduces anxiety for the elderly and disabled when they are alone and enables a rapid response in the event of an accident or illness. This system uses an AI-equipped wearable terminal to collect daily data, analyze the collected data in real time, and learn the user's health status and behavioral patterns. If an abnormality is detected, an emergency call is automatically made to a designated contact. This mechanism provides peace of mind to the user and their family and enables a rapid response. For example, the AI-equipped wearable terminal collects daily data. At this time, activity levels and health data (e.g., heart rate, blood pressure, body temperature, etc.) are collected. This allows the system to understand the user's health status and behavioral patterns. Next, the collected data is analyzed in real time. The AI ​​analyzes the collected data and detects abnormal values ​​and irregular behavior. For example, if an abnormality is detected, such as a sudden increase in heart rate or prolonged immobility, an immediate response can be taken. If an abnormality is detected, an emergency call is automatically made to a designated contact. For example, the system notifies the user's family or medical institution that an abnormality has occurred. This enables a rapid response, ensuring user safety. Furthermore, continuous data feedback allows the AI ​​to deepen its learning. This enables more accurate anomaly detection, improving user health management. This system reduces anxiety for the elderly and disabled when they are alone and allows for a quick response in the event of an accident or illness. It provides peace of mind to users and their families and improves their quality of life. It also offers a new approach to protecting the elderly as a whole society, as an important solution in an aging society. As a result, the AI-powered wearable terminal system can monitor the user's health status in real time and respond quickly when an anomaly occurs.

[0062] The AI-equipped wearable terminal system according to this embodiment comprises a data collection unit, an analysis unit, and a notification unit. The data collection unit collects activity levels and health data. For example, the data collection unit collects health data such as heart rate, blood pressure, and body temperature. The data collection unit can also collect activity level data such as steps taken, exercise time, and calories burned. For example, the data collection unit collects the user's health data in real time using sensors mounted on the wearable terminal. The analysis unit analyzes the data collected by the data collection unit in real time and detects abnormalities. For example, the analysis unit analyzes the collected data and detects abnormal values ​​and irregular behavior. For example, the analysis unit can respond immediately when an abnormality is detected, such as when the heart rate suddenly increases or when the user remains motionless for a long period of time. For example, the analysis unit can also use AI to analyze the collected data and detect abnormalities. The notification unit makes an emergency call to a designated contact based on the abnormality detected by the analysis unit. For example, when an abnormality is detected, the notification unit notifies the user's family or medical institution that an abnormality has occurred. The notification unit can make emergency calls using means such as telephone, email, or SMS. The notification unit can also automatically make emergency calls when an abnormality is detected, for example, using AI. As a result, the AI-equipped wearable terminal system according to this embodiment can reduce anxiety for the elderly and disabled when they are alone and enable a quick response in the event of an accident or illness.

[0063] The data collection unit collects activity levels and health data. For example, it collects health data such as heart rate, blood pressure, and body temperature. Specifically, a heart rate sensor mounted on the wearable device monitors the user's heart rate in real time and detects abnormal heart rate patterns. A blood pressure sensor periodically measures the user's blood pressure and detects abnormal blood pressure fluctuations. A body temperature sensor continuously measures the user's body temperature to detect early signs of fever or hypothermia. These sensors can collect accurate data by directly contacting the user's skin. Furthermore, the data collection unit can also collect activity data such as steps taken, exercise time, and calories burned. A pedometer detects the user's walking motion and counts the daily steps. An accelerometer measures the user's exercise intensity and duration and calculates calories burned. This allows the data collection unit to comprehensively understand the user's health status and activity level. The collected data is stored in the wearable device's memory and periodically uploaded to a cloud server. The data stored on the cloud server is made accessible to the analysis and notification units (described later), strengthening the overall system coordination. The data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to user needs and situations. For example, by increasing the data collection frequency during exercise and decreasing it during rest, it is possible to reliably collect necessary data while conserving battery power.

[0064] The analysis department analyzes data collected by the data collection department in real time to detect anomalies. For example, the analysis department analyzes collected data to detect abnormal values ​​and irregular behavior. Specifically, it uses AI to analyze data such as heart rate, blood pressure, and body temperature to detect abnormal values ​​that exceed the normal range. For example, it can respond immediately when an anomaly is detected, such as when heart rate suddenly increases, blood pressure is abnormally high or low, or body temperature fluctuates rapidly. Based on past data and statistical information, the AI ​​learns normal and abnormal patterns and builds an algorithm to detect anomalies in real time. This allows the analysis department to quickly and accurately analyze collected data and detect anomalies early. Furthermore, the analysis department can also learn the user's behavior patterns and lifestyle habits to detect abnormal behavior. For example, if a user remains motionless for a long period of time or exhibits behavior different from their normal activity pattern, it can detect an anomaly and take appropriate action. When an anomaly is detected, the analysis department immediately notifies the reporting department to prompt emergency response. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0065] The notification unit makes emergency calls to designated contacts based on anomalies detected by the analysis unit. Specifically, when an anomaly is detected, it notifies the user's family or medical institution of the occurrence of the anomaly. The notification unit can make emergency calls using methods such as telephone, email, and SMS. In the case of telephone calls, an automated voice message is sent to pre-registered contacts, explaining the nature of the anomaly and the current situation. In the case of email calls, details of the anomaly and collected data are attached and sent to encourage a quick response. In the case of SMS calls, a short message notifies the occurrence of an anomaly and encourages emergency contact. Furthermore, the notification unit can also use AI to automatically make emergency calls when an anomaly is detected. The AI ​​selects the most appropriate notification method according to the type and urgency of the anomaly, and takes a quick and appropriate response. For example, if the heart rate suddenly increases and the user loses consciousness, the AI ​​will immediately make an emergency call and prompt the dispatch of an ambulance. The notification unit can also obtain the user's location information and notify the current location when making an emergency call. This allows paramedics and family members to quickly reach the scene. The notification unit also implements security measures to ensure that necessary information is reliably transmitted while protecting the user's privacy. This allows the reporting department to ensure user safety and support a swift and appropriate response in emergencies.

[0066] The data collection unit can collect health data such as heart rate, blood pressure, and body temperature. For example, the data collection unit can collect the user's heart rate in real time using a heart rate measuring device. The data collection unit can also collect the user's blood pressure in real time using a blood pressure measuring device. The data collection unit can also collect the user's body temperature in real time using a body temperature measuring device. By collecting health data in this way, the user's health status can be understood. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect health data such as heart rate, blood pressure, and body temperature, input this data into AI, and the AI ​​can analyze the data to detect abnormalities.

[0067] The analysis unit can analyze the collected data and detect anomalies and irregular behavior. For example, the analysis unit can analyze the collected data and detect anomalies such as a sudden increase in heart rate or prolonged immobility. The analysis unit can also use AI to analyze the collected data and detect anomalies and irregular behavior. For example, the analysis unit can perform analysis using algorithms that detect anomalies and irregular behavior based on the collected data. This enables a rapid response by detecting anomalies and irregular behavior. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the collected data into an AI, which can analyze the data and detect anomalies.

[0068] The notification unit can notify the user's family or medical institution of an abnormality when an abnormality is detected. For example, the notification unit can notify the user's family or medical institution of an abnormality by telephone when an abnormality is detected. The notification unit can also notify the user's family or medical institution of an abnormality by email when an abnormality is detected. The notification unit can also notify the user's family or medical institution of an abnormality by SMS when an abnormality is detected. This ensures the user's safety by providing prompt notification when an abnormality occurs. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, when an abnormality is detected, the AI ​​can automatically make an emergency call.

[0069] AI-powered wearable terminal systems further include a feedback unit that allows the AI ​​to deepen its learning through continuous data feedback. The feedback unit allows the AI ​​to deepen its learning based on the collected data, enabling more accurate anomaly detection. For example, the feedback unit inputs collected data into the AI, which then analyzes the data to deepen its learning. The feedback unit can also, for example, allow the AI ​​to improve its anomaly detection algorithm based on the collected data. The feedback unit can also, for example, build a feedback loop to improve the accuracy of anomaly detection based on the collected data. This allows the AI ​​to deepen its learning, enabling more accurate anomaly detection. Some or all of the above-described processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input collected data into the AI, which then analyzes the data to deepen its learning.

[0070] The feedback unit allows the AI ​​to deepen its learning based on the collected data, enabling more accurate anomaly detection. For example, the feedback unit inputs the collected data into the AI, which then analyzes the data to deepen its learning. The feedback unit can also, for example, allow the AI ​​to improve its anomaly detection algorithm based on the collected data. The feedback unit can also, for example, build a feedback loop to improve the accuracy of anomaly detection based on the collected data. This improves the accuracy of anomaly detection as the AI ​​deepens its learning based on the collected data. Some or all of the above-described processes in the feedback unit may be performed using the AI, or not using the AI. For example, the feedback unit can input the collected data into the AI, which then analyzes the data to deepen its learning.

[0071] The data collection unit can estimate the user's emotions and adjust the frequency of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the collection frequency to collect 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 anxious, the data collection unit can appropriately adjust the collection frequency to provide a sense of security. By adjusting the frequency of health data collection according to the user's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's emotion data into an AI, which can analyze the data and adjust the collection frequency.

[0072] 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 detect from the user's past data that abnormalities are more likely to occur during specific time periods and focus data collection during those times. For example, the data collection unit can detect from the user's past data that abnormalities are more likely to occur during specific activities and collect detailed data during those activities. For example, the data collection unit can detect from the user's past data that abnormalities are more likely to occur under specific environmental conditions and collect data under those environmental conditions. By analyzing past health data, the optimal data collection method can be selected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into AI, which can then analyze the data and select the optimal data collection method.

[0073] The data collection unit can filter health data based on the user's current activity level and environment. For example, if the user is exercising, the data collection unit will prioritize collecting data related to exercise. For example, if the user is resting, the data collection unit can prioritize collecting data related to resting. For example, if the user is out, the data collection unit can prioritize collecting data related to going out. This allows for the collection of more relevant data by filtering the data based on the user's current activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activity level and environment data into the AI, which can then analyze and filter the data.

[0074] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting stress-related data such as heart rate and blood pressure. For example, if the user is relaxed, the data collection unit may prioritize collecting relaxation-related data such as body temperature and respiratory rate. For example, if the user is anxious, the data collection unit may prioritize collecting anxiety-related data such as activity level and sleep data. This allows for the priority collection of more important data by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's emotion data into an AI, which can analyze the data and determine the priority of data to collect.

[0075] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information when collecting health data. For example, if the user is at home, the data collection unit can prioritize the collection of data related to activities at home. For example, if the user is out, the data collection unit can prioritize the collection of data related to activities at the location. For example, if the user is at a medical institution, the data collection unit can prioritize the collection of data related to activities at the medical institution. This enables more accurate data collection by collecting highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, and the AI ​​can analyze the data and prioritize the collection of highly relevant data.

[0076] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user is experiencing stress on social media, the data collection unit can prioritize collecting stress-related data. For example, if a user is experiencing relaxation on social media, the data collection unit can also prioritize collecting relaxation-related data. For example, if a user is experiencing anxiety on social media, the data collection unit can also prioritize collecting anxiety-related data. In this way, relevant data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, which can then analyze the data and collect relevant data.

[0077] The analysis 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 analysis unit can tighten the stress-related anomaly detection criteria. For example, if the user is relaxed, the analysis unit can loosen the relaxation-related anomaly detection criteria. For example, if the user is anxious, the analysis unit can moderately adjust the anxiety-related anomaly detection criteria. By adjusting the anomaly detection criteria according to the user's emotions, more appropriate anomaly detection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can analyze the data and adjust the anomaly detection criteria.

[0078] The analysis unit can improve the accuracy of anomaly detection based on the interrelationships of health data during analysis. For example, the analysis unit can detect anomalies by considering the correlation between heart rate and blood pressure. The analysis unit can also detect anomalies by considering the correlation between body temperature and activity level. The analysis unit can also detect anomalies by considering the correlation between respiratory rate and sleep data. By improving the accuracy of anomaly detection based on the interrelationships of health data, more accurate anomaly detection becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships of health data into AI, and the AI ​​can analyze the data to detect anomalies.

[0079] The analysis unit can perform anomaly detection based on user attribute information during analysis. For example, the analysis unit can detect anomalies by considering the user's age. The analysis unit can also detect anomalies by considering the user's gender. The analysis unit can also detect anomalies by considering the user's medical history. This allows for more personalized anomaly detection by performing anomaly detection based on user attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information into AI, and the AI ​​can analyze the data to detect anomalies.

[0080] The analysis 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 analysis unit will prioritize displaying important anomalies. For example, if the user is relaxed, the analysis unit may also sequentially display detailed anomaly information. For example, if the user is feeling anxious, the analysis unit may also prioritize displaying information that provides a sense of security. This allows for the provision of more appropriate information by adjusting the order in which anomaly detection results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can analyze the data and adjust the order in which anomaly detection results are displayed.

[0081] The analysis unit can perform anomaly detection based on the geographical distribution of health data during analysis. For example, if a user is in a specific region, the analysis unit can detect anomalies by considering the health risks of that region. For example, if a user is traveling, the analysis unit can also detect anomalies by considering the health risks of the travel destination. For example, if a user is at home, the analysis unit can also detect anomalies by considering the environmental risks of the home. This allows for more accurate anomaly detection by performing anomaly detection based on the geographical distribution of health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution of health data into AI, and the AI ​​can analyze the data and detect anomalies.

[0082] The analysis unit can improve the accuracy of anomaly detection based on relevant literature during analysis. For example, the analysis unit can update the criteria for anomaly detection by referring to the latest medical literature. The analysis unit can also improve the anomaly detection algorithm by referring to past research data. The analysis unit can also improve the accuracy of anomaly detection by referring to relevant academic papers. By improving the accuracy of anomaly detection based on relevant literature, more accurate anomaly detection becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input relevant literature into AI, and the AI ​​can analyze the data to improve the accuracy of anomaly detection.

[0083] The reporting unit can estimate the user's emotions and adjust the way the report is expressed based on the estimated emotions. For example, if the user is stressed, the reporting unit will make a concise and clear report. For example, if the user is relaxed, the reporting unit may also make a report that includes detailed information. For example, if the user is anxious, the reporting unit may also make a report using reassuring language. This allows for more appropriate reporting by adjusting the way the report is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input user emotion data into an AI, which can analyze the data and adjust the way the report is expressed.

[0084] The reporting unit can adjust the level of detail in a report based on the severity of the anomaly. For example, if a serious anomaly is detected, the reporting unit will issue a report containing detailed information. For example, if a minor anomaly is detected, the reporting unit may issue a concise report. The reporting unit can also adjust the level of detail in a report in stages according to the severity of the anomaly. This allows for the provision of more appropriate information by adjusting the level of detail in the report according to the severity of the anomaly. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input anomaly severity data into AI, and the AI ​​can analyze the data to adjust the level of detail in the report.

[0085] The notification unit can apply different notification algorithms depending on the category of the anomaly when a notification is made. For example, if an abnormality in heart rate is detected, the notification unit can apply a notification algorithm specifically for heart rate. For example, if an abnormality in blood pressure is detected, the notification unit can also apply a notification algorithm specifically for blood pressure. For example, if an abnormality in body temperature is detected, the notification unit can also apply a notification algorithm specifically for body temperature. This allows for more appropriate notifications by applying a notification algorithm according to the category of the anomaly. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly category data into AI, and the AI ​​can analyze the data and apply a different notification algorithm.

[0086] The notification unit can estimate the user's emotions and adjust the length of the notification based on the estimated emotions. For example, if the user is stressed, the notification unit will send a short, to-the-point notification. If the user is relaxed, the notification unit may send a longer notification containing detailed information. If the user is anxious, the notification unit may send a notification containing reassuring content. By adjusting the length of the notification according to the user's emotions, more appropriate notifications can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into an AI, which can analyze the data and adjust the length of the notification.

[0087] The reporting unit can determine the priority of reports based on when the anomaly occurred. For example, the reporting unit may report immediately after the anomaly occurs. For example, the reporting unit may also prioritize reporting if the anomaly is ongoing. For example, the reporting unit may also adjust the priority of reports in stages according to when the anomaly occurred. This allows for a faster response by determining the priority of reports based on when the anomaly occurred. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input data on when the anomaly occurred into the AI, and the AI ​​can analyze the data to determine the priority of reports.

[0088] The notification unit can adjust the order of notifications based on the relevance of the anomalies when a notification is made. For example, if multiple anomalies are detected, the notification unit will prioritize notifying the most important anomaly. The notification unit can also adjust the order of notifications in stages according to the relevance of the anomalies. The notification unit can also optimize the order of notifications by considering the relevance of the anomalies. This allows for the provision of more appropriate information by adjusting the order of notifications based on the relevance of the anomalies. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly relevance data into AI, and the AI ​​can analyze the data to adjust the order of notifications.

[0089] The feedback unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the feedback unit will prioritize learning stress-related data. For example, if the user is relaxed, the feedback unit can also prioritize learning relaxation-related data. For example, if the user is anxious, the feedback unit can also prioritize learning anxiety-related data. This allows for more accurate learning by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input user emotion data into an AI, which can analyze the data and select training data.

[0090] The feedback unit can optimize the learning algorithm based on past learning data during the learning process. For example, the feedback unit can select the most effective learning algorithm from past learning data. The feedback unit can also adjust the parameters of the learning algorithm based on past learning data. For example, the feedback unit can improve the accuracy of the learning algorithm by referring to past learning data. As a result, the accuracy of learning is improved by optimizing the learning algorithm based on past learning data. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input past learning data into AI, and the AI ​​can analyze the data to optimize the learning algorithm.

[0091] The feedback unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the feedback unit can increase the learning frequency to collect more detailed data. For example, if the user is relaxed, the feedback unit can decrease the learning frequency to reduce the burden of data collection. For example, if the user is anxious, the feedback unit can appropriately adjust the learning frequency to provide a sense of security. This allows for more appropriate learning by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user emotion data into an AI, which can analyze the data and adjust the learning frequency.

[0092] The feedback unit can weight the training data during learning based on when the health data was collected. For example, the feedback unit may assign a higher weight to data collected recently, or a lower weight to data collected older. The feedback unit can also adjust the weighting of the training data in stages according to the timing of the health data collection. This allows for more accurate learning by weighting the training data based on the timing of the health data collection. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input health data collection timing data into the AI, which can then analyze the data and weight the training data.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The data collection unit can estimate the user's emotions and adjust the frequency of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the collection frequency to collect 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 anxious, the data collection unit can appropriately adjust the collection frequency to provide a sense of security. By adjusting the frequency of health data collection according to the user's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's emotion data into an AI, which can analyze the data and adjust the collection frequency.

[0095] 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 detect from the user's past data that abnormalities are more likely to occur during specific time periods and focus data collection during those times. For example, the data collection unit can detect from the user's past data that abnormalities are more likely to occur during specific activities and collect detailed data during those activities. For example, the data collection unit can detect from the user's past data that abnormalities are more likely to occur under specific environmental conditions and collect data under those environmental conditions. By analyzing past health data, the optimal data collection method can be selected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into AI, which can then analyze the data and select the optimal data collection method.

[0096] The data collection unit can filter health data based on the user's current activity level and environment. For example, if the user is exercising, the data collection unit will prioritize collecting data related to exercise. For example, if the user is resting, the data collection unit can prioritize collecting data related to resting. For example, if the user is out, the data collection unit can prioritize collecting data related to going out. This allows for the collection of more relevant data by filtering the data based on the user's current activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activity level and environment data into the AI, which can then analyze and filter the data.

[0097] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting stress-related data such as heart rate and blood pressure. For example, if the user is relaxed, the data collection unit may prioritize collecting relaxation-related data such as body temperature and respiratory rate. For example, if the user is anxious, the data collection unit may prioritize collecting anxiety-related data such as activity level and sleep data. This allows for the priority collection of more important data by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's emotion data into an AI, which can analyze the data and determine the priority of data to collect.

[0098] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information when collecting health data. For example, if the user is at home, the data collection unit can prioritize the collection of data related to activities at home. For example, if the user is out, the data collection unit can prioritize the collection of data related to activities at the location. For example, if the user is at a medical institution, the data collection unit can prioritize the collection of data related to activities at the medical institution. This enables more accurate data collection by collecting highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, and the AI ​​can analyze the data and prioritize the collection of highly relevant data.

[0099] The analysis 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 analysis unit can tighten the stress-related anomaly detection criteria. For example, if the user is relaxed, the analysis unit can loosen the relaxation-related anomaly detection criteria. For example, if the user is anxious, the analysis unit can moderately adjust the anxiety-related anomaly detection criteria. By adjusting the anomaly detection criteria according to the user's emotions, more appropriate anomaly detection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can analyze the data and adjust the anomaly detection criteria.

[0100] The analysis unit can improve the accuracy of anomaly detection based on the interrelationships of health data during analysis. For example, the analysis unit can detect anomalies by considering the correlation between heart rate and blood pressure. The analysis unit can also detect anomalies by considering the correlation between body temperature and activity level. The analysis unit can also detect anomalies by considering the correlation between respiratory rate and sleep data. By improving the accuracy of anomaly detection based on the interrelationships of health data, more accurate anomaly detection becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships of health data into AI, and the AI ​​can analyze the data to detect anomalies.

[0101] The analysis unit can perform anomaly detection based on user attribute information during analysis. For example, the analysis unit can detect anomalies by considering the user's age. The analysis unit can also detect anomalies by considering the user's gender. The analysis unit can also detect anomalies by considering the user's medical history. This allows for more personalized anomaly detection by performing anomaly detection based on user attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information into AI, and the AI ​​can analyze the data to detect anomalies.

[0102] The analysis 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 analysis unit will prioritize displaying important anomalies. For example, if the user is relaxed, the analysis unit may also sequentially display detailed anomaly information. For example, if the user is feeling anxious, the analysis unit may also prioritize displaying information that provides a sense of security. This allows for the provision of more appropriate information by adjusting the order in which anomaly detection results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can analyze the data and adjust the order in which anomaly detection results are displayed.

[0103] The analysis unit can perform anomaly detection based on the geographical distribution of health data during analysis. For example, if a user is in a specific region, the analysis unit can detect anomalies by considering the health risks of that region. For example, if a user is traveling, the analysis unit can also detect anomalies by considering the health risks of the travel destination. For example, if a user is at home, the analysis unit can also detect anomalies by considering the environmental risks of the home. This allows for more accurate anomaly detection by performing anomaly detection based on the geographical distribution of health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution of health data into AI, and the AI ​​can analyze the data and detect anomalies.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The data collection unit collects activity levels and health data. For example, it can collect health data such as heart rate, blood pressure, and body temperature, as well as activity data such as steps taken, exercise time, and calories burned. The data collection unit uses sensors mounted on the wearable device to collect the user's health data in real time. Step 2: The analysis unit analyzes the data collected by the collection unit in real time and detects anomalies. For example, it analyzes the collected data to detect abnormal values ​​or irregular behavior. It can respond immediately if an anomaly is detected, such as a sudden increase in heart rate or prolonged immobility. AI can also be used to analyze the collected data and detect anomalies. Step 3: The notification unit makes an emergency call to the designated contacts based on the anomaly detected by the analysis unit. For example, if an anomaly is detected, it will notify the user's family or medical institution that an anomaly has occurred. Emergency calls can be made using methods such as phone, email, and SMS. It is also possible to use AI to automatically make an emergency call when an anomaly is detected.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the data collection unit, analysis unit, notification unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects activity levels and health data using the sensors of the smart device 14. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 in real time and detects anomalies. The notification unit makes an emergency notification to a designated contact when an anomaly is detected by the specific processing unit 290 of the data processing unit 12. The feedback unit uses the data collected by the specific processing unit 290 of the data processing unit 12 to deepen the AI's learning and improve the accuracy of anomaly detection. 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.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the data collection unit, analysis unit, notification unit, and feedback unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects activity levels and health data using the sensors of the smart glasses 214. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 in real time and detects anomalies. The notification unit makes an emergency call to a designated contact when an anomaly is detected by the specific processing unit 290 of the data processing unit 12. The feedback unit uses the data collected by the specific processing unit 290 of the data processing unit 12 to deepen the AI's learning and improve the accuracy of anomaly detection. 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.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the data collection unit, analysis unit, notification unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects activity levels and health data using the sensors of the headset terminal 314. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 in real time and detects anomalies. The notification unit makes an emergency call to a designated contact when an anomaly is detected by the specific processing unit 290 of the data processing unit 12. The feedback unit uses the data collected by the specific processing unit 290 of the data processing unit 12 to deepen the AI's learning and improve the accuracy of anomaly detection. 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.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[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 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.

[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 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.

[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 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.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, notification unit, and feedback unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects activity levels and health data using the sensors of the robot 414. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 in real time and detects anomalies. The notification unit makes an emergency notification to a designated contact when an anomaly is detected by the specific processing unit 290 of the data processing unit 12. The feedback unit deepens the AI's learning based on the data collected by the specific processing unit 290 of the data processing unit 12 and improves the accuracy of anomaly detection. 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] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (Note 1) The data collection unit collects activity levels and health data, An analysis unit analyzes the data collected by the aforementioned collection unit in real time and detects anomalies, The system includes a notification unit that makes an emergency call to a designated contact based on an anomaly detected by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect health data such as heart rate, blood pressure, and body temperature. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed to detect anomalies and irregular behavior. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reporting unit, If an anomaly is detected, the system will notify the user's family and medical institutions that an anomaly has occurred. The system described in Appendix 1, characterized by the features described herein. (Note 5) Through continuous data feedback, The AI ​​will be further equipped with a feedback system to deepen its learning. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is Based on the collected data, the AI ​​deepens its learning, enabling more accurate anomaly detection. The system described in Appendix 5, 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 health data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past 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 health data, filtering is performed based on the user's current activity level and environment. 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 health data, the system prioritizes collecting highly relevant data based on 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 health data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis 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 14) The aforementioned analysis unit is During analysis, improve the accuracy of anomaly detection based on the interrelationships of health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, anomaly detection is performed based on user attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis 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 17) The aforementioned analysis unit is During analysis, anomaly detection is performed based on the geographical distribution of health data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, improve the accuracy of anomaly detection based on relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reporting unit, The system estimates the user's emotions and adjusts the way reports are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reporting unit, When reporting an anomaly, adjust the level of detail in the report based on its severity. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reporting unit, When reporting an issue, different reporting algorithms are applied depending on the category of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reporting unit, The system estimates the user's emotions and adjusts the length of the report based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reporting unit, When reporting an issue, the priority of the report is determined based on when the anomaly occurred. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting unit, When reporting, the order of reports will be adjusted based on the relevance of the anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 26) The aforementioned feedback unit is During training, the learning algorithm is optimized based on past training data. The system described in Appendix 5, characterized by the features described herein. (Note 27) The aforementioned feedback unit is It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 28) The aforementioned feedback unit is During training, the training data is weighted based on when the health data was collected. The system described in Appendix 5, characterized by the features described herein. [Explanation of symbols]

[0178] 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. The data collection unit collects activity levels and health data, An analysis unit analyzes the data collected by the aforementioned collection unit in real time and detects anomalies, The system includes a notification unit that makes an emergency call to a designated contact based on an anomaly detected by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect health data such as heart rate, blood pressure, and body temperature. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed to detect anomalies and irregular behavior. The system according to feature 1.

4. The aforementioned reporting unit, If an anomaly is detected, the system will notify the user's family and medical institutions that an anomaly has occurred. The system according to feature 1.

5. Through continuous data feedback, The AI ​​will be further equipped with a feedback system to deepen its learning. The system according to feature 1.

6. The aforementioned feedback unit is Based on the collected data, the AI ​​deepens its learning, enabling more accurate anomaly detection. The system according to claim 5, characterized in that it is the same as described in claim 5.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of health data collection based on those 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 health data, filtering is performed based on the user's current activity level and environment. 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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