system

Abstract

The system according to this embodiment aims to prevent water-related accidents. [Solution] The system according to the embodiment comprises a monitoring unit, a detection unit, an alert unit, and an acquisition unit. The monitoring unit monitors the heart rate. The detection unit detects abnormalities based on the heart rate data monitored by the monitoring unit. The alert unit emits an alert with sound and light based on the abnormality detected by the detection unit. The acquisition unit acquires location information based on the alert emitted by the alert unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to prevent drowning accidents in advance.

[0005] The system according to the embodiment aims to prevent drowning accidents in advance.

Means for Solving the Problems

[0006] The system according to the embodiment includes a monitoring unit, a detection unit, an alert unit, and an acquisition unit. The monitoring unit monitors the heart rate. The detection unit detects an abnormality based on the heart rate data monitored by the monitoring unit. The alert unit issues an alert by sound and light based on the abnormality detected by the detection unit. The acquisition unit acquires position information based on the alert issued by the alert unit. [Effects of the Invention]

[0007] The system according to this embodiment can prevent water-related accidents. [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 water accident prevention system according to an embodiment of the present invention is a system that monitors heart rate, detects abnormalities, and issues an alert. This system uses a ring equipped with Bluetooth® and GPS to monitor heart rate and, when an abnormality is detected, emits an alert to the surroundings with sound and light. Furthermore, it can analyze heart rate data using a generating AI to immediately identify abnormalities. It can also acquire location information and respond quickly to emergencies. For example, the ring monitors the wearer's heart rate in real time. Heart rate data is transmitted to the administrator's Pad via Bluetooth for centralized management. The generating AI analyzes the heart rate data and immediately identifies abnormalities. For example, if the heart rate changes rapidly, the generating AI detects the abnormality and issues an alert. Next, when an abnormality is detected, the ring emits an alert to the surroundings with sound and light. This allows people in the vicinity to quickly confirm the abnormality and respond quickly. For example, safety managers at swimming pools or beaches can receive the alert and quickly begin rescue operations. Furthermore, the generating AI acquires location information and responds quickly to emergencies. For example, in the event of a drowning accident, the generating AI acquires location information and provides accurate location data to emergency services such as ambulances and police. This allows emergency services to arrive at the scene quickly and take appropriate action. This system enables the prevention and rapid intervention of water-related accidents. For example, it can improve safety management in school pools, commercial facility pools, beaches, and hot spring facilities, preventing water-related accidents from occurring. Furthermore, by utilizing the generating AI, heart rate data analysis and location information acquisition can be performed quickly and accurately, making emergency response more efficient. In this way, the water accident prevention system can achieve both the prevention and rapid intervention of water-related accidents.

[0029] The water accident prevention system according to this embodiment comprises a monitoring unit, a detection unit, an alert unit, and an acquisition unit. The monitoring unit monitors the heart rate. The monitoring unit can monitor the heart rate using techniques such as photoplethysmography (PPG) or electrocardiogram (ECG). The monitoring unit acquires heart rate data in real time and provides basic data for immediate detection of abnormalities. The detection unit detects abnormalities based on the heart rate data monitored by the monitoring unit. The detection unit can detect abnormalities based on, for example, a heart rate threshold or an abnormal rhythm pattern. The detection unit analyzes the heart rate data and immediately identifies the abnormality. For example, if the heart rate changes rapidly, the detection unit detects the abnormality and issues an alert. The alert unit issues an alert with sound and light based on the abnormality detected by the detection unit. The alert unit can, for example, set the type of alert sound and the color and pattern of the light. The alert unit prompts a quick response by notifying the surroundings of the abnormality with sound and light. The acquisition unit acquires location information based on alerts issued by the alert unit. The acquisition unit can acquire location information using technologies such as GPS, Wi-Fi location information, and cell tower location information. The acquisition unit acquires location information and provides information for a rapid response to emergencies. As a result, the water accident prevention system according to the embodiment can consistently perform everything from heart rate monitoring to anomaly detection, alert issuance, and location information acquisition.

[0030] The monitoring unit monitors heart rate. The monitoring unit can monitor heart rate using technologies such as photoplethysmography (PPG) and electrocardiogram (ECG). Specifically, PPG is a technology that measures blood flow through the skin to calculate heart rate, while ECG is a technology that measures heart rate by recording the electrical activity of the heart. Using these technologies, the monitoring unit acquires the user's heart rate data in real time, providing foundational data for immediate detection of abnormalities. The monitoring unit can be integrated into wearable devices and smartwatches, allowing users to constantly monitor their heart rate by wearing them daily. This enables continuous verification of whether the user's heart rate is within the normal range and allows for rapid response if an abnormality occurs. Furthermore, the monitoring unit can use multiple sensors in combination to improve data accuracy. For example, using a PPG sensor and an ECG sensor simultaneously can acquire more accurate heart rate data. Additionally, the monitoring unit can achieve flexible monitoring tailored to the user's activity level and environment by adjusting the data collection frequency and analysis algorithm. This allows the monitoring unit to monitor the user's heart rate with high accuracy, supporting early detection of abnormalities and prompt response.

[0031] The detection unit detects anomalies based on heart rate data monitored by the monitoring unit. The detection unit can detect anomalies based, for example, on heart rate thresholds or abnormal rhythm patterns. Specifically, the detection unit identifies anomalies when the heart rate rapidly increases or decreases, or when an abnormal rhythm is detected, by comparing it to a pre-set normal heart rate range. The detection unit uses AI to analyze heart rate data and immediately identify anomalies. The AI ​​learns from past data and statistical information, enabling it to detect anomaly patterns with high accuracy. For example, if the heart rate changes rapidly, the detection unit detects an anomaly and issues an alert. Furthermore, the detection unit can issue different levels of alerts depending on the type and severity of the anomaly. For example, it can issue a warning alert for minor anomalies and an emergency alert for serious anomalies. This allows the detection unit to quickly and accurately analyze the user's heart rate data and immediately identify anomalies. Additionally, the detection unit can use a combination of multiple analysis algorithms to improve the accuracy of anomaly detection. For example, combining machine learning algorithms with rule-based algorithms can improve the accuracy and reliability of anomaly detection. This allows the detection unit to analyze the user's heart rate data with high accuracy, supporting early detection of abnormalities and prompt response.

[0032] The alert unit emits alerts with sound and light based on anomalies detected by the detection unit. The alert unit can, for example, set the type of alert sound and the color and pattern of the light. Specifically, the alert unit emits different alert sounds and light patterns depending on the type and severity of the anomaly. For example, a short beep and green light are emitted for minor anomalies, while a continuous alarm sound and red light are emitted for serious anomalies. This allows the alert unit to quickly notify those around it of an anomaly. Furthermore, the alert unit can adjust the method of emitting alerts according to the user's situation and environment. For example, in a noisy environment, the volume can be increased or vibration alerts can be added to ensure that an anomaly is notified. The alert unit can also connect with the user's smartphone or other devices to notify family members or medical institutions in remote locations of an anomaly. This allows the alert unit to encourage a quick response and ensure the user's safety. In addition, the alert unit can record the history of alerts and analyze it later. This allows for understanding the frequency and patterns of anomalies and provides data for taking preventive measures. This allows the alert unit to ensure the user's safety and support a quick response.

[0033] The acquisition unit acquires location information based on alerts issued by the alert unit. The acquisition unit can acquire location information using technologies such as GPS, Wi-Fi location information, and cell tower location information. Specifically, the acquisition unit acquires location information from the user's device and provides information to respond quickly to emergencies. For example, the accuracy of location information can be improved by using GPS to pinpoint the user's exact location and supplementing it with Wi-Fi location information and cell tower location information. The acquisition unit can update location information in real time and respond quickly when an anomaly occurs. Furthermore, the acquisition unit can share location information with other systems and departments to support emergency response. For example, by providing acquired location information to emergency services and police, rapid rescue operations can be supported. The acquisition unit can also record the history of location information and analyze it later. This allows for understanding the location and frequency of anomalies and provides data for taking preventive measures. Furthermore, the acquisition unit can flexibly adjust settings regarding the acquisition and sharing of location information to protect user privacy. This allows the acquisition unit to acquire user location information with high accuracy and provide information to respond quickly to emergencies.

[0034] The monitoring unit can monitor heart rate in real time. For example, the monitoring unit can acquire heart rate data in real time and provide basic data for immediate detection of abnormalities. The monitoring unit can also transmit heart rate data to the administrator's Pad via Bluetooth. For example, the monitoring unit can transmit heart rate data using Bluetooth Low Energy (BLE), allowing the administrator to check the data in real time. This enables immediate detection of abnormalities by monitoring heart rate in real time.

[0035] The detection unit can analyze heart rate data and immediately identify abnormalities. For example, the detection unit identifies abnormalities using time-series analysis of heart rate data or an anomaly detection algorithm. By analyzing heart rate data and immediately identifying abnormalities, the detection unit enables a rapid response. For example, the detection unit inputs heart rate data into a generating AI, which then identifies the abnormality. The generating AI analyzes the heart rate data and immediately identifies the abnormality. This allows for a rapid response by analyzing heart rate data and immediately identifying abnormalities.

[0036] The alert unit can emit alerts to the surroundings using sound and light. For example, the alert unit allows you to set the type of alert sound, the color and pattern of the light. The alert unit prompts a quick response by notifying those around it of an anomaly with sound and light. For example, if an anomaly is detected, the alert unit will notify those around it by flashing a red light. The alert unit can also notify those around it by emitting a warning sound when an anomaly is detected. Furthermore, the alert unit can change the light pattern to indicate the type of anomaly when an anomaly is detected. In this way, by emitting alerts with sound and light, it is possible to quickly notify people in the surroundings of an anomaly.

[0037] The acquisition unit can acquire location information and respond quickly to emergencies. The acquisition unit can acquire location information using technologies such as GPS, Wi-Fi location information, and cell tower location information. The acquisition unit acquires location information and provides information for a rapid response to emergencies. For example, when an anomaly is detected, the acquisition unit uses GPS to acquire accurate location information. The acquisition unit can also acquire indoor location information using Wi-Fi location information. Furthermore, the acquisition unit can acquire wide-area location information using cell tower location information. This allows for a rapid response to emergencies by acquiring location information.

[0038] The monitoring unit can transmit heart rate data to the administrator's Pad via Bluetooth. For example, the monitoring unit can transmit heart rate data using Bluetooth Low Energy (BLE), allowing the administrator to view the data in real time. By transmitting heart rate data via Bluetooth, the monitoring unit enables the administrator to view the data in real time. For instance, the monitoring unit can transmit heart rate data via Bluetooth, allowing the administrator to immediately detect any abnormalities. This allows the administrator to view the data in real time by transmitting heart rate data via Bluetooth.

[0039] The acquisition unit can acquire location information using a generating AI. For example, the acquisition unit analyzes location information using a generating AI to obtain accurate location information. By utilizing a generating AI, the acquisition unit can acquire location information quickly and accurately. For example, the acquisition unit inputs location information data into the generating AI, and the generating AI analyzes the location information. The generating AI analyzes the location information data and can obtain accurate location information. As a result, by utilizing a generating AI, location information can be acquired quickly and accurately.

[0040] The monitoring unit can dynamically change the reference heart rate value according to the user's activity level during monitoring. For example, when the user is exercising, the monitoring unit sets the reference heart rate value higher to improve the accuracy of anomaly detection. When the user is resting, the monitoring unit sets the reference heart rate value lower to enable early detection of anomalies. When the user is sleeping, the monitoring unit sets the reference heart rate value lower than usual to make it easier to detect abnormal fluctuations. In this way, the accuracy of anomaly detection is improved by changing the reference heart rate value according to the user's activity level. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's activity level data into a generating AI and have the generating AI perform the dynamic change of the reference heart rate value.

[0041] The monitoring unit can detect abnormalities early by referring to the user's past health data during monitoring. For example, the monitoring unit can refer to the user's past heart rate data to detect abnormal patterns early. The monitoring unit can refer to the user's past health checkup results to detect signs of abnormalities early. The monitoring unit can refer to the user's past exercise history to detect abnormal heart rate fluctuations early. This makes it possible to detect abnormalities early by referring to past health data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past health data into a generating AI and have the generating AI perform early detection of abnormalities.

[0042] The monitoring unit can correct for environmental factors by considering the user's geographical location information during monitoring. For example, if the user is at high altitude, the monitoring unit can adjust the baseline heart rate to improve the accuracy of anomaly detection. If the user is in a hot environment, the monitoring unit can adjust the baseline heart rate to detect anomalies earlier. If the user is in a cold region, the monitoring unit can adjust the baseline heart rate to make it easier to detect abnormal fluctuations. In this way, by considering geographical location information, environmental factors are corrected and the accuracy of anomaly detection is improved. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information into a generating AI and have the generating AI perform the correction of environmental factors.

[0043] The monitoring unit can analyze the user's social media activity during monitoring, estimate stress levels, and detect abnormal heart rates. For example, the monitoring unit can analyze the content of the user's social media posts, estimate stress levels, and detect abnormal heart rates. The monitoring unit can analyze the frequency of the user's social media use, estimate stress levels, and detect abnormal heart rates. The monitoring unit can analyze the user's emotional expressions on social media, estimate stress levels, and detect abnormal heart rates. In this way, stress levels can be estimated and abnormal heart rates can be detected by analyzing social media activity. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's social media data into a generating AI and have the generating AI perform stress level estimation and heart rate abnormality detection.

[0044] The detection unit can analyze the heart rate fluctuation pattern upon detection to identify the type of abnormality. For example, the detection unit can analyze a sudden increase in heart rate to determine whether it is due to exercise or stress. The detection unit can analyze a sudden decrease in heart rate to determine whether it is due to rest or an abnormality. The detection unit can analyze irregular fluctuations in heart rate to determine whether they are due to health problems or environmental factors. In this way, the type of abnormality can be identified by analyzing the heart rate fluctuation pattern. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input heart rate data into a generating AI and have the generating AI perform the identification of the type of abnormality.

[0045] The detection unit can predict the recurrence of an anomaly by referring to the user's past anomaly data when an anomaly is detected. For example, the detection unit can refer to the user's past anomaly data and predict the likelihood of a similar anomaly recurring. The detection unit can refer to the user's past anomaly data and propose measures to prevent the recurrence of an anomaly. The detection unit can refer to the user's past anomaly data and set a threshold for early detection of an anomaly recurrence. This allows the recurrence of an anomaly to be predicted by referring to past anomaly data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the user's past anomaly data into a generating AI and have the generating AI perform anomaly recurrence prediction.

[0046] The detection unit can evaluate the extent of the anomaly's impact by considering the user's geographical location information when an anomaly is detected. For example, if the user is at high altitude, the detection unit can evaluate the extent of the anomaly's impact and propose an appropriate response. If the user is in a hot environment, the detection unit can evaluate the extent of the anomaly's impact and propose a rapid response. If the user is in a cold region, the detection unit can evaluate the extent of the anomaly's impact and propose an appropriate response. In this way, by considering geographical location information, it becomes possible to evaluate the extent of the anomaly's impact and take an appropriate response. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the user's geographical location information into a generating AI and have the generating AI perform an evaluation of the extent of the anomaly's impact.

[0047] The detection unit can analyze the user's social media activity and identify the cause of the anomaly upon detection. For example, the detection unit can analyze the content of the user's social media posts to identify the cause of the anomaly. The detection unit can analyze the frequency of the user's social media use to identify the cause of the anomaly. The detection unit can analyze the emotional expressions of the user on social media to identify the cause of the anomaly. In this way, the cause of the anomaly can be identified by analyzing social media activity. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's social media data into a generating AI and have the generating AI perform the task of identifying the cause of the anomaly.

[0048] The alert unit can analyze ambient noise and set the optimal volume when an alert is issued. For example, if the surroundings are noisy, the alert unit increases the volume to notify those nearby. If the surroundings are quiet, the alert unit can return the volume to normal, avoiding excessive volume. The alert unit can analyze ambient noise in real time and set the optimal volume. This maximizes the effectiveness of the alert by setting the optimal volume according to the ambient noise. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input ambient noise data into a generating AI and have the generating AI set the optimal volume.

[0049] The alert unit can indicate the type of anomaly by changing the color or pattern of light when an alert is issued. For example, the alert unit can notify the surroundings by flashing a red light depending on the type of anomaly. The alert unit can notify the surroundings by flashing a blue light depending on the type of anomaly. The alert unit can notify the surroundings by flashing a green light depending on the type of anomaly. In this way, the type of anomaly can be visually indicated by changing the color or pattern of light. Some or all of the above processing in the alert unit may be performed using AI, for example, or without using AI. For example, the alert unit can have a generating AI execute the setting of the color or pattern of light according to the type of anomaly.

[0050] The alert unit can select the optimal alert method by considering the user's geographical location when issuing an alert. For example, if the user is at high altitude, the alert unit can increase the volume of the alert to notify those around them. If the user is in a hot environment, the alert unit can return the volume to normal to avoid excessive volume. If the user is in a cold region, the alert unit can adjust the volume of the alert to provide appropriate notification. In this way, by considering geographical location, the optimal alert method can be selected, enabling effective notification. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal alert method.

[0051] The alert unit can analyze the user's social media activity and generate an appropriate alert message when an alert is issued. For example, the alert unit can analyze the content of the user's social media posts and generate an appropriate alert message. The alert unit can analyze the frequency of the user's social media use and generate an appropriate alert message. The alert unit can analyze the user's emotional expression on social media and generate an appropriate alert message. This enables effective notification by generating appropriate alert messages through analysis of social media activity. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the user's social media data into a generating AI and have the generating AI generate an appropriate alert message.

[0052] The acquisition unit can analyze the user's movement patterns when acquiring location information to predict emergencies. For example, the acquisition unit can analyze the user's movement patterns, detect abnormal movements, and predict emergencies. The acquisition unit can analyze the user's movement speed, detect sudden changes, and predict emergencies. The acquisition unit can analyze the user's direction of movement, detect abnormal changes in direction, and predict emergencies. In this way, it becomes possible to predict emergencies by analyzing movement patterns. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the user's movement pattern data into a generating AI and have the generating AI perform emergency predictions.

[0053] The acquisition unit can identify the location of an anomaly by referring to the user's past location data when acquiring location information. The acquisition unit can identify the location of an anomaly by referring to the user's past location data. The acquisition unit can identify the location of an anomaly by referring to the user's past movement history. The acquisition unit can identify the location of an anomaly by analyzing the user's past location data. In this way, the location of an anomaly can be identified by referring to past location data. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the user's past location data into a generating AI and have the generating AI perform the task of identifying the location of the anomaly.

[0054] The acquisition unit can select the optimal acquisition method by considering the user's geographical location when acquiring location information. For example, if the user is at high altitude, the acquisition unit can acquire location information using GPS. If the user is indoors, the acquisition unit can acquire location information using Wi-Fi. If the user is underground, the acquisition unit can acquire location information using Bluetooth beacons. By considering geographical location information, the acquisition unit can select the optimal acquisition method and acquire accurate location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal acquisition method.

[0055] The acquisition unit can analyze the user's social media activity when acquiring location information and identify the location of an emergency. For example, the acquisition unit can analyze the content of the user's social media posts to identify the location of an emergency. The acquisition unit can analyze the frequency of the user's social media use to identify the location of an emergency. The acquisition unit can analyze the emotional expressions of the user on social media to identify the location of an emergency. In this way, the location of an emergency can be identified by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the user's social media data into a generating AI and have the generating AI perform the identification of the emergency location.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The monitoring unit can monitor the user's body temperature and detect abnormalities. For example, the monitoring unit uses a body temperature sensor to acquire the user's body temperature in real time and detect abnormal temperature fluctuations. The monitoring unit can transmit the body temperature data to the administrator's iPad via Bluetooth and issue an alert if an abnormality is detected. This enables more comprehensive health management by monitoring not only heart rate but also body temperature.

[0058] The detection unit can monitor the user's respiratory rate and detect abnormalities. For example, the detection unit uses a respiratory sensor to acquire the user's respiratory rate in real time and detect abnormal breathing patterns. The detection unit can analyze the respiratory rate data and issue an alert if an abnormality is detected. This allows for early detection of abnormalities by monitoring both heart rate and respiratory rate.

[0059] The alert unit can send notifications to the user's smartphone when an anomaly is detected. For example, the alert unit connects to the user's smartphone via Bluetooth and sends push notifications when an anomaly is detected. The alert unit can customize the notification content and display messages according to the type and urgency of the anomaly. This allows the user to quickly understand the anomaly and take appropriate action.

[0060] The data acquisition unit can monitor the user's activity level and detect anomalies. For example, the unit uses an accelerometer to acquire the user's activity level in real time and detect abnormal activity patterns. The unit can analyze the activity level data and issue an alert if an anomaly is detected. This allows for early detection of anomalies by monitoring not only heart rate and body temperature, but also activity levels.

[0061] The monitoring unit can monitor the user's fluid intake and detect abnormalities. For example, the monitoring unit uses a fluid intake sensor to acquire the user's fluid intake in real time and detect abnormal intake patterns. The monitoring unit can transmit the fluid intake data to the administrator's iPad via Bluetooth and issue an alert if an abnormality is detected. This allows for early detection of abnormalities by monitoring not only heart rate and body temperature, but also fluid intake.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The monitoring unit monitors the heart rate. The monitoring unit can monitor the heart rate using techniques such as photoplethysmography (PPG) or electrocardiogram (ECG). The monitoring unit acquires heart rate data in real time and provides basic data for immediate detection of abnormalities. Step 2: The detection unit detects abnormalities based on heart rate data monitored by the monitoring unit. The detection unit can detect abnormalities based, for example, on a heart rate threshold or an abnormal rhythm pattern. The detection unit analyzes the heart rate data and immediately identifies the abnormality. For example, if the heart rate changes rapidly, the detection unit detects the abnormality and issues an alert. Step 3: The alert unit emits an alert with sound and light based on the anomaly detected by the detection unit. The alert unit can, for example, set the type of alert sound, the color and pattern of the light. The alert unit prompts a quick response by notifying those around it of the anomaly with sound and light. Step 4: The acquisition unit acquires location information based on alerts issued by the alert unit. The acquisition unit can acquire location information using technologies such as GPS, Wi-Fi location information, and cell tower location information. The acquisition unit acquires location information and provides information to respond quickly to emergencies.

[0064] (Example of form 2) The water accident prevention system according to an embodiment of the present invention is a system that monitors heart rate, detects abnormalities, and issues an alert. This system uses a ring equipped with Bluetooth and GPS to monitor heart rate and, when an abnormality is detected, emits an alert to the surroundings with sound and light. Furthermore, it can analyze heart rate data using a generation AI to immediately identify abnormalities. It can also acquire location information and respond quickly to emergencies. For example, the ring monitors the wearer's heart rate in real time. Heart rate data is transmitted to the administrator's Pad via Bluetooth for centralized management. The generation AI analyzes the heart rate data and immediately identifies abnormalities. For example, if the heart rate changes rapidly, the generation AI detects the abnormality and issues an alert. Next, when an abnormality is detected, the ring emits an alert to the surroundings with sound and light. This allows people in the vicinity to quickly confirm the abnormality and respond quickly. For example, safety managers at swimming pools or beaches can receive the alert and quickly begin rescue operations. Furthermore, the generation AI acquires location information and responds quickly to emergencies. For example, in the event of a drowning accident, the generating AI acquires location information and provides accurate location data to emergency services such as ambulances and police. This allows emergency services to arrive at the scene quickly and take appropriate action. This system enables the prevention and rapid intervention of water-related accidents. For example, it can improve safety management in school pools, commercial facility pools, beaches, and hot spring facilities, preventing water-related accidents from occurring. Furthermore, by utilizing the generating AI, heart rate data analysis and location information acquisition can be performed quickly and accurately, making emergency response more efficient. In this way, the water accident prevention system can achieve both the prevention and rapid intervention of water-related accidents.

[0065] The water accident prevention system according to this embodiment comprises a monitoring unit, a detection unit, an alert unit, and an acquisition unit. The monitoring unit monitors the heart rate. The monitoring unit can monitor the heart rate using techniques such as photoplethysmography (PPG) or electrocardiogram (ECG). The monitoring unit acquires heart rate data in real time and provides basic data for immediate detection of abnormalities. The detection unit detects abnormalities based on the heart rate data monitored by the monitoring unit. The detection unit can detect abnormalities based on, for example, a heart rate threshold or an abnormal rhythm pattern. The detection unit analyzes the heart rate data and immediately identifies the abnormality. For example, if the heart rate changes rapidly, the detection unit detects the abnormality and issues an alert. The alert unit issues an alert with sound and light based on the abnormality detected by the detection unit. The alert unit can, for example, set the type of alert sound and the color and pattern of the light. The alert unit prompts a quick response by notifying the surroundings of the abnormality with sound and light. The acquisition unit acquires location information based on alerts issued by the alert unit. The acquisition unit can acquire location information using technologies such as GPS, Wi-Fi location information, and cell tower location information. The acquisition unit acquires location information and provides information for a rapid response to emergencies. As a result, the water accident prevention system according to the embodiment can consistently perform everything from heart rate monitoring to anomaly detection, alert issuance, and location information acquisition.

[0066] The monitoring unit monitors heart rate. The monitoring unit can monitor heart rate using technologies such as photoplethysmography (PPG) and electrocardiogram (ECG). Specifically, PPG is a technology that measures blood flow through the skin to calculate heart rate, while ECG is a technology that measures heart rate by recording the electrical activity of the heart. Using these technologies, the monitoring unit acquires the user's heart rate data in real time, providing foundational data for immediate detection of abnormalities. The monitoring unit can be integrated into wearable devices and smartwatches, allowing users to constantly monitor their heart rate by wearing them daily. This enables continuous verification of whether the user's heart rate is within the normal range and allows for rapid response if an abnormality occurs. Furthermore, the monitoring unit can use multiple sensors in combination to improve data accuracy. For example, using a PPG sensor and an ECG sensor simultaneously can acquire more accurate heart rate data. Additionally, the monitoring unit can achieve flexible monitoring tailored to the user's activity level and environment by adjusting the data collection frequency and analysis algorithm. This allows the monitoring unit to monitor the user's heart rate with high accuracy, supporting early detection of abnormalities and prompt response.

[0067] The detection unit detects anomalies based on heart rate data monitored by the monitoring unit. The detection unit can detect anomalies based, for example, on heart rate thresholds or abnormal rhythm patterns. Specifically, the detection unit identifies anomalies when the heart rate rapidly increases or decreases, or when an abnormal rhythm is detected, by comparing it to a pre-set normal heart rate range. The detection unit uses AI to analyze heart rate data and immediately identify anomalies. The AI ​​learns from past data and statistical information, enabling it to detect anomaly patterns with high accuracy. For example, if the heart rate changes rapidly, the detection unit detects an anomaly and issues an alert. Furthermore, the detection unit can issue different levels of alerts depending on the type and severity of the anomaly. For example, it can issue a warning alert for minor anomalies and an emergency alert for serious anomalies. This allows the detection unit to quickly and accurately analyze the user's heart rate data and immediately identify anomalies. Additionally, the detection unit can use a combination of multiple analysis algorithms to improve the accuracy of anomaly detection. For example, combining machine learning algorithms with rule-based algorithms can improve the accuracy and reliability of anomaly detection. This allows the detection unit to analyze the user's heart rate data with high accuracy, supporting early detection of abnormalities and prompt response.

[0068] The alert unit emits alerts with sound and light based on anomalies detected by the detection unit. The alert unit can, for example, set the type of alert sound and the color and pattern of the light. Specifically, the alert unit emits different alert sounds and light patterns depending on the type and severity of the anomaly. For example, a short beep and green light are emitted for minor anomalies, while a continuous alarm sound and red light are emitted for serious anomalies. This allows the alert unit to quickly notify those around it of an anomaly. Furthermore, the alert unit can adjust the method of emitting alerts according to the user's situation and environment. For example, in a noisy environment, the volume can be increased or vibration alerts can be added to ensure that an anomaly is notified. The alert unit can also connect with the user's smartphone or other devices to notify family members or medical institutions in remote locations of an anomaly. This allows the alert unit to encourage a quick response and ensure the user's safety. In addition, the alert unit can record the history of alerts and analyze it later. This allows for understanding the frequency and patterns of anomalies and provides data for taking preventive measures. This allows the alert unit to ensure the user's safety and support a quick response.

[0069] The acquisition unit acquires location information based on alerts issued by the alert unit. The acquisition unit can acquire location information using technologies such as GPS, Wi-Fi location information, and cell tower location information. Specifically, the acquisition unit acquires location information from the user's device and provides information to respond quickly to emergencies. For example, the accuracy of location information can be improved by using GPS to pinpoint the user's exact location and supplementing it with Wi-Fi location information and cell tower location information. The acquisition unit can update location information in real time and respond quickly when an anomaly occurs. Furthermore, the acquisition unit can share location information with other systems and departments to support emergency response. For example, by providing acquired location information to emergency services and police, rapid rescue operations can be supported. The acquisition unit can also record the history of location information and analyze it later. This allows for understanding the location and frequency of anomalies and provides data for taking preventive measures. Furthermore, the acquisition unit can flexibly adjust settings regarding the acquisition and sharing of location information to protect user privacy. This allows the acquisition unit to acquire user location information with high accuracy and provide information to respond quickly to emergencies.

[0070] The monitoring unit can monitor heart rate in real time. For example, the monitoring unit can acquire heart rate data in real time and provide basic data for immediate detection of abnormalities. The monitoring unit can also transmit heart rate data to the administrator's Pad via Bluetooth. For example, the monitoring unit can transmit heart rate data using Bluetooth Low Energy (BLE), allowing the administrator to check the data in real time. This enables immediate detection of abnormalities by monitoring heart rate in real time.

[0071] The detection unit can analyze heart rate data and immediately identify abnormalities. For example, the detection unit identifies abnormalities using time-series analysis of heart rate data or an anomaly detection algorithm. By analyzing heart rate data and immediately identifying abnormalities, the detection unit enables a rapid response. For example, the detection unit inputs heart rate data into a generating AI, which then identifies the abnormality. The generating AI analyzes the heart rate data and immediately identifies the abnormality. This allows for a rapid response by analyzing heart rate data and immediately identifying abnormalities.

[0072] The alert unit can emit alerts to the surroundings using sound and light. For example, the alert unit allows you to set the type of alert sound, the color and pattern of the light. The alert unit prompts a quick response by notifying those around it of an anomaly with sound and light. For example, if an anomaly is detected, the alert unit will notify those around it by flashing a red light. The alert unit can also notify those around it by emitting a warning sound when an anomaly is detected. Furthermore, the alert unit can change the light pattern to indicate the type of anomaly when an anomaly is detected. In this way, by emitting alerts with sound and light, it is possible to quickly notify people in the surroundings of an anomaly.

[0073] The acquisition unit can acquire location information and respond quickly to emergencies. The acquisition unit can acquire location information using technologies such as GPS, Wi-Fi location information, and cell tower location information. The acquisition unit acquires location information and provides information for a rapid response to emergencies. For example, when an anomaly is detected, the acquisition unit uses GPS to acquire accurate location information. The acquisition unit can also acquire indoor location information using Wi-Fi location information. Furthermore, the acquisition unit can acquire wide-area location information using cell tower location information. This allows for a rapid response to emergencies by acquiring location information.

[0074] The monitoring unit can transmit heart rate data to the administrator's Pad via Bluetooth. For example, the monitoring unit can transmit heart rate data using Bluetooth Low Energy (BLE), allowing the administrator to view the data in real time. By transmitting heart rate data via Bluetooth, the monitoring unit enables the administrator to view the data in real time. For instance, the monitoring unit can transmit heart rate data via Bluetooth, allowing the administrator to immediately detect any abnormalities. This allows the administrator to view the data in real time by transmitting heart rate data via Bluetooth.

[0075] The acquisition unit can acquire location information using a generating AI. For example, the acquisition unit analyzes location information using a generating AI to obtain accurate location information. By utilizing a generating AI, the acquisition unit can acquire location information quickly and accurately. For example, the acquisition unit inputs location information data into the generating AI, and the generating AI analyzes the location information. The generating AI analyzes the location information data and can obtain accurate location information. As a result, by utilizing a generating AI, location information can be acquired quickly and accurately.

[0076] The monitoring unit can estimate the user's emotions and adjust the accuracy of heart rate monitoring based on the estimated emotions. For example, if the user is stressed, the monitoring unit can increase the monitoring accuracy to track heart rate fluctuations in detail. If the user is relaxed, the monitoring unit can return the monitoring accuracy to normal to avoid excessive data collection. If the user is excited, the monitoring unit can set the monitoring accuracy to a moderate level to make it easier to detect rapid fluctuations. This allows for more accurate heart rate monitoring by adjusting the monitoring accuracy 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The monitoring unit can dynamically change the reference heart rate value according to the user's activity level during monitoring. For example, when the user is exercising, the monitoring unit sets the reference heart rate value higher to improve the accuracy of anomaly detection. When the user is resting, the monitoring unit sets the reference heart rate value lower to enable early detection of anomalies. When the user is sleeping, the monitoring unit sets the reference heart rate value lower than usual to make it easier to detect abnormal fluctuations. In this way, the accuracy of anomaly detection is improved by changing the reference heart rate value according to the user's activity level. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's activity level data into a generating AI and have the generating AI perform the dynamic change of the reference heart rate value.

[0078] The monitoring unit can detect abnormalities early by referring to the user's past health data during monitoring. For example, the monitoring unit can refer to the user's past heart rate data to detect abnormal patterns early. The monitoring unit can refer to the user's past health checkup results to detect signs of abnormalities early. The monitoring unit can refer to the user's past exercise history to detect abnormal heart rate fluctuations early. This makes it possible to detect abnormalities early by referring to past health data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past health data into a generating AI and have the generating AI perform early detection of abnormalities.

[0079] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring unit can increase the monitoring frequency to track heart rate variability in detail. If the user is relaxed, the monitoring unit can return the monitoring frequency to normal to avoid excessive data collection. If the user is excited, the monitoring unit can set the monitoring frequency to a moderate level to make it easier to detect rapid fluctuations. This allows for more appropriate data collection by adjusting the monitoring frequency 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The monitoring unit can correct for environmental factors by considering the user's geographical location information during monitoring. For example, if the user is at high altitude, the monitoring unit can adjust the baseline heart rate to improve the accuracy of anomaly detection. If the user is in a hot environment, the monitoring unit can adjust the baseline heart rate to detect anomalies earlier. If the user is in a cold region, the monitoring unit can adjust the baseline heart rate to make it easier to detect abnormal fluctuations. In this way, by considering geographical location information, environmental factors are corrected and the accuracy of anomaly detection is improved. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information into a generating AI and have the generating AI perform the correction of environmental factors.

[0081] The monitoring unit can analyze the user's social media activity during monitoring, estimate stress levels, and detect abnormal heart rates. For example, the monitoring unit can analyze the content of the user's social media posts, estimate stress levels, and detect abnormal heart rates. The monitoring unit can analyze the frequency of the user's social media use, estimate stress levels, and detect abnormal heart rates. The monitoring unit can analyze the user's emotional expressions on social media, estimate stress levels, and detect abnormal heart rates. In this way, stress levels can be estimated and abnormal heart rates can be detected by analyzing social media activity. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's social media data into a generating AI and have the generating AI perform stress level estimation and heart rate abnormality detection.

[0082] The detection unit can estimate the user's emotions and adjust the anomaly detection threshold based on the estimated emotions. For example, if the user is tense, the detection unit can set the anomaly detection threshold low to detect anomalies early. If the user is relaxed, the detection unit can return the anomaly detection threshold to normal to avoid excessive alerts. If the user is excited, the detection unit can set the anomaly detection threshold to a moderate level to make it easier to detect sudden changes. In this way, by adjusting the anomaly detection threshold according to the user's emotions, anomalies can be detected early. 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.

[0083] The detection unit can analyze the heart rate fluctuation pattern upon detection to identify the type of abnormality. For example, the detection unit can analyze a sudden increase in heart rate to determine whether it is due to exercise or stress. The detection unit can analyze a sudden decrease in heart rate to determine whether it is due to rest or an abnormality. The detection unit can analyze irregular fluctuations in heart rate to determine whether they are due to health problems or environmental factors. In this way, the type of abnormality can be identified by analyzing the heart rate fluctuation pattern. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input heart rate data into a generating AI and have the generating AI perform the identification of the type of abnormality.

[0084] The detection unit can predict the recurrence of an anomaly by referring to the user's past anomaly data when an anomaly is detected. For example, the detection unit can refer to the user's past anomaly data and predict the likelihood of a similar anomaly recurring. The detection unit can refer to the user's past anomaly data and propose measures to prevent the recurrence of an anomaly. The detection unit can refer to the user's past anomaly data and set a threshold for early detection of an anomaly recurrence. This allows the recurrence of an anomaly to be predicted by referring to past anomaly data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the user's past anomaly data into a generating AI and have the generating AI perform anomaly recurrence prediction.

[0085] The detection unit can estimate the user's emotions and determine the priority of anomaly detection based on the estimated emotions. For example, if the user is tense, the detection unit will set the priority of anomaly detection to high and respond quickly. If the user is relaxed, the detection unit will return the priority of anomaly detection to normal to avoid excessive alerts. If the user is excited, the detection unit will set the priority of anomaly detection to medium, making it easier to detect sudden changes. This enables a quick response by determining the priority of anomaly detection 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.

[0086] The detection unit can evaluate the extent of the anomaly's impact by considering the user's geographical location information when an anomaly is detected. For example, if the user is at high altitude, the detection unit can evaluate the extent of the anomaly's impact and propose an appropriate response. If the user is in a hot environment, the detection unit can evaluate the extent of the anomaly's impact and propose a rapid response. If the user is in a cold region, the detection unit can evaluate the extent of the anomaly's impact and propose an appropriate response. In this way, by considering geographical location information, it becomes possible to evaluate the extent of the anomaly's impact and take an appropriate response. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the user's geographical location information into a generating AI and have the generating AI perform an evaluation of the extent of the anomaly's impact.

[0087] The detection unit can analyze the user's social media activity and identify the cause of the anomaly upon detection. For example, the detection unit can analyze the content of the user's social media posts to identify the cause of the anomaly. The detection unit can analyze the frequency of the user's social media use to identify the cause of the anomaly. The detection unit can analyze the emotional expressions of the user on social media to identify the cause of the anomaly. In this way, the cause of the anomaly can be identified by analyzing social media activity. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's social media data into a generating AI and have the generating AI perform the task of identifying the cause of the anomaly.

[0088] The alert unit can estimate the user's emotions and adjust the alert intensity based on the estimated emotions. For example, if the user is tense, the alert unit can increase the alert intensity to quickly notify those around them. If the user is relaxed, the alert unit can return the alert intensity to normal to avoid excessive alerts. If the user is excited, the alert unit can set the alert intensity to a moderate level to make it easier to detect sudden changes. This allows for more appropriate alerts to be issued by adjusting the alert intensity 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The alert unit can analyze ambient noise and set the optimal volume when an alert is issued. For example, if the surroundings are noisy, the alert unit increases the volume to notify those nearby. If the surroundings are quiet, the alert unit can return the volume to normal, avoiding excessive volume. The alert unit can analyze ambient noise in real time and set the optimal volume. This maximizes the effectiveness of the alert by setting the optimal volume according to the ambient noise. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input ambient noise data into a generating AI and have the generating AI set the optimal volume.

[0090] The alert unit can indicate the type of anomaly by changing the color or pattern of light when an alert is issued. For example, the alert unit can notify the surroundings by flashing a red light depending on the type of anomaly. The alert unit can notify the surroundings by flashing a blue light depending on the type of anomaly. The alert unit can notify the surroundings by flashing a green light depending on the type of anomaly. In this way, the type of anomaly can be visually indicated by changing the color or pattern of light. Some or all of the above processing in the alert unit may be performed using AI, for example, or without using AI. For example, the alert unit can have a generating AI execute the setting of the color or pattern of light according to the type of anomaly.

[0091] The alert unit can estimate the user's emotions and adjust how the alert is displayed based on those emotions. For example, if the user is stressed, the alert unit can provide a simple and highly visible display. If the user is relaxed, the alert unit can provide a display that includes detailed information. If the user is in a hurry, the alert unit can provide a display that gets straight to the point. By adjusting the alert display according to the user's emotions, more effective notifications become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The alert unit can select the optimal alert method by considering the user's geographical location when issuing an alert. For example, if the user is at high altitude, the alert unit can increase the volume of the alert to notify those around them. If the user is in a hot environment, the alert unit can return the volume to normal to avoid excessive volume. If the user is in a cold region, the alert unit can adjust the volume of the alert to provide appropriate notification. In this way, by considering geographical location, the optimal alert method can be selected, enabling effective notification. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal alert method.

[0093] The alert unit can analyze the user's social media activity and generate an appropriate alert message when an alert is issued. For example, the alert unit can analyze the content of the user's social media posts and generate an appropriate alert message. The alert unit can analyze the frequency of the user's social media use and generate an appropriate alert message. The alert unit can analyze the user's emotional expression on social media and generate an appropriate alert message. This enables effective notification by generating appropriate alert messages through analysis of social media activity. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input the user's social media data into a generating AI and have the generating AI generate an appropriate alert message.

[0094] The acquisition unit can estimate the user's emotions and adjust the frequency of location data acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition unit can increase the frequency of location data acquisition to detect emergencies early. If the user is relaxed, the acquisition unit can return the frequency of location data acquisition to normal to avoid excessive data collection. If the user is excited, the acquisition unit can set the frequency of location data acquisition to a moderate level to make it easier to detect sudden changes. In this way, by adjusting the frequency of location data acquisition according to the user's emotions, emergencies can be detected early. 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.

[0095] The acquisition unit can analyze the user's movement patterns when acquiring location information to predict emergencies. For example, the acquisition unit can analyze the user's movement patterns, detect abnormal movements, and predict emergencies. The acquisition unit can analyze the user's movement speed, detect sudden changes, and predict emergencies. The acquisition unit can analyze the user's direction of movement, detect abnormal changes in direction, and predict emergencies. In this way, it becomes possible to predict emergencies by analyzing movement patterns. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the user's movement pattern data into a generating AI and have the generating AI perform emergency predictions.

[0096] The acquisition unit can identify the location of an anomaly by referring to the user's past location data when acquiring location information. The acquisition unit can identify the location of an anomaly by referring to the user's past location data. The acquisition unit can identify the location of an anomaly by referring to the user's past movement history. The acquisition unit can identify the location of an anomaly by analyzing the user's past location data. In this way, the location of an anomaly can be identified by referring to past location data. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the user's past location data into a generating AI and have the generating AI perform the task of identifying the location of the anomaly.

[0097] The acquisition unit can estimate the user's emotions and adjust the method of acquiring location information based on the estimated emotions. For example, if the user is tense, the acquisition unit can set the location information acquisition method to high precision to detect emergencies early. If the user is relaxed, the acquisition unit can return the location information acquisition method to normal to avoid excessive data collection. If the user is excited, the acquisition unit can set the location information acquisition method to moderate to make it easier to detect sudden changes. In this way, by adjusting the location information acquisition method according to the user's emotions, emergencies can be detected early. 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.

[0098] The acquisition unit can select the optimal acquisition method by considering the user's geographical location when acquiring location information. For example, if the user is at high altitude, the acquisition unit can acquire location information using GPS. If the user is indoors, the acquisition unit can acquire location information using Wi-Fi. If the user is underground, the acquisition unit can acquire location information using Bluetooth beacons. By considering geographical location information, the acquisition unit can select the optimal acquisition method and acquire accurate location information. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal acquisition method.

[0099] The acquisition unit can analyze the user's social media activity when acquiring location information and identify the location of an emergency. For example, the acquisition unit can analyze the content of the user's social media posts to identify the location of an emergency. The acquisition unit can analyze the frequency of the user's social media use to identify the location of an emergency. The acquisition unit can analyze the emotional expressions of the user on social media to identify the location of an emergency. In this way, the location of an emergency can be identified by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the user's social media data into a generating AI and have the generating AI perform the identification of the emergency location.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The monitoring unit can monitor the user's body temperature and detect abnormalities. For example, the monitoring unit uses a body temperature sensor to acquire the user's body temperature in real time and detect abnormal temperature fluctuations. The monitoring unit can transmit the body temperature data to the administrator's iPad via Bluetooth and issue an alert if an abnormality is detected. This enables more comprehensive health management by monitoring not only heart rate but also body temperature.

[0102] The detection unit can monitor the user's respiratory rate and detect abnormalities. For example, the detection unit uses a respiratory sensor to acquire the user's respiratory rate in real time and detect abnormal breathing patterns. The detection unit can analyze the respiratory rate data and issue an alert if an abnormality is detected. This allows for early detection of abnormalities by monitoring both heart rate and respiratory rate.

[0103] The alert unit can send notifications to the user's smartphone when an anomaly is detected. For example, the alert unit connects to the user's smartphone via Bluetooth and sends push notifications when an anomaly is detected. The alert unit can customize the notification content and display messages according to the type and urgency of the anomaly. This allows the user to quickly understand the anomaly and take appropriate action.

[0104] The data acquisition unit can monitor the user's activity level and detect anomalies. For example, the unit uses an accelerometer to acquire the user's activity level in real time and detect abnormal activity patterns. The unit can analyze the activity level data and issue an alert if an anomaly is detected. This allows for early detection of anomalies by monitoring not only heart rate and body temperature, but also activity levels.

[0105] The monitoring unit can monitor the user's fluid intake and detect abnormalities. For example, the monitoring unit uses a fluid intake sensor to acquire the user's fluid intake in real time and detect abnormal intake patterns. The monitoring unit can transmit the fluid intake data to the administrator's iPad via Bluetooth and issue an alert if an abnormality is detected. This allows for early detection of abnormalities by monitoring not only heart rate and body temperature, but also fluid intake.

[0106] The monitoring unit can estimate the user's emotions and adjust the content of alerts based on those emotions. For example, if the user is stressed, the alert can be made concise to encourage a quick response. If the user is relaxed, the alert can be made more detailed to accurately convey the situation. By adjusting the alert content according to the user's emotions, more effective notifications can be achieved.

[0107] The detection unit can estimate the user's emotions and set an anomaly priority based on the estimated emotions. For example, if the user is stressed, the anomaly priority can be set high to encourage a quick response. If the user is relaxed, the anomaly priority can be returned to normal to avoid excessive alerts. This allows for a more appropriate response by setting anomaly priorities according to the user's emotions.

[0108] The alert system can estimate the user's emotions and adjust how alerts are delivered based on that estimation. For example, if the user is stressed, an alert can be delivered audibly to prompt a quick response. If the user is relaxed, an alert can be delivered in text, providing more detailed information. By adjusting the alert delivery method according to the user's emotions, more effective notifications can be achieved.

[0109] The acquisition unit can estimate the user's emotions and adjust the frequency of location data acquisition based on the estimated emotions. For example, if the user is stressed, the frequency of location data acquisition can be increased to detect emergencies early. If the user is relaxed, the frequency of location data acquisition can be returned to normal to avoid excessive data collection. In this way, by adjusting the frequency of location data acquisition according to the user's emotions, emergencies can be detected early.

[0110] The acquisition unit can estimate the user's emotions and adjust the method of acquiring location information based on the estimated emotions. For example, if the user is stressed, the method of acquiring location information can be set to high precision to detect emergencies early. If the user is relaxed, the method of acquiring location information can be returned to normal to avoid excessive data collection. In this way, by adjusting the method of acquiring location information according to the user's emotions, emergencies can be detected early.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The monitoring unit monitors the heart rate. The monitoring unit can monitor the heart rate using techniques such as photoplethysmography (PPG) or electrocardiogram (ECG). The monitoring unit acquires heart rate data in real time and provides basic data for immediate detection of abnormalities. Step 2: The detection unit detects abnormalities based on heart rate data monitored by the monitoring unit. The detection unit can detect abnormalities based, for example, on a heart rate threshold or an abnormal rhythm pattern. The detection unit analyzes the heart rate data and immediately identifies the abnormality. For example, if the heart rate changes rapidly, the detection unit detects the abnormality and issues an alert. Step 3: The alert unit emits an alert with sound and light based on the anomaly detected by the detection unit. The alert unit can, for example, set the type of alert sound, the color and pattern of the light. The alert unit prompts a quick response by notifying those around it of the anomaly with sound and light. Step 4: The acquisition unit acquires location information based on alerts issued by the alert unit. The acquisition unit can acquire location information using technologies such as GPS, Wi-Fi location information, and cell tower location information. The acquisition unit acquires location information and provides information to respond quickly to emergencies.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the monitoring unit, detection unit, alert unit, and acquisition unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit is implemented by the processor 46 of the smart device 14 and monitors the heart rate in real time. The detection unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes heart rate data to detect abnormalities. The alert unit is implemented by the control unit 46A of the smart device 14 and emits alerts with sound and light. The acquisition unit is implemented by the specific processing unit 290 of the data processing device 12 and acquires location information to respond to emergencies. 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.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the monitoring unit, detection unit, alert unit, and acquisition unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit is implemented by the processor 46 of the smart glasses 214 and monitors the heart rate in real time. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes heart rate data to detect abnormalities. The alert unit is implemented by the control unit 46A of the smart glasses 214 and emits alerts with sound and light. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and acquires location information to respond to emergencies. 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.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the monitoring unit, detection unit, alert unit, and acquisition unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit is implemented by the processor 46 of the headset terminal 314 and monitors heart rate in real time. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes heart rate data to detect abnormalities. The alert unit is implemented by the control unit 46A of the headset terminal 314 and emits alerts with sound and light. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and acquires location information to respond to emergencies. 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.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the monitoring unit, detection unit, alert unit, and acquisition unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit is implemented by the processor 46 of the robot 414 and monitors the heart rate in real time. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and detects abnormalities by analyzing heart rate data. The alert unit is implemented by the control unit 46A of the robot 414 and emits alerts with sound and light. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and acquires location information to respond to emergencies. 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A monitoring unit that monitors heart rate, A detection unit that detects abnormalities based on heart rate data monitored by the monitoring unit, An alert unit that emits an alert with sound and light based on an abnormality detected by the aforementioned detection unit, The system includes an acquisition unit that acquires location information based on an alert issued by the alert unit. A system characterized by the following features. (Note 2) The monitoring unit, Monitor your heart rate in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The detection unit is Analyze heart rate data and instantly identify abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 4) The alert unit is, It emits an alert to the surroundings with sound and light. The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, Acquire location information and respond quickly to emergencies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The monitoring unit, Send heart rate data to the administrator's pad via Bluetooth. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, Location information is obtained using generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 8) The monitoring unit, It estimates the user's emotions and adjusts the accuracy of heart rate monitoring based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The monitoring unit, During monitoring, the baseline heart rate is dynamically changed according to the user's activity level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The monitoring unit, During monitoring, the system references the user's past health data to detect abnormalities early. The system described in Appendix 1, characterized by the features described herein. (Note 11) The monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The monitoring unit, During monitoring, environmental factors are corrected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The monitoring unit, During monitoring, the system analyzes the user's social media activity, estimates stress levels, and detects abnormal heart rates. The system described in Appendix 1, characterized by the features described herein. (Note 14) The detection unit is The system estimates the user's emotions and adjusts the anomaly detection threshold based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit is Upon detection, the heart rate variability pattern is analyzed to identify the type of abnormality. The system described in Appendix 1, characterized by the features described herein. (Note 16) The detection unit is When an anomaly is detected, the system predicts its recurrence by referring to the user's past anomaly data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The detection unit is The system estimates the user's emotions and determines the priority of anomaly detection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The detection unit is When an anomaly is detected, the extent of its impact is evaluated considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit is Upon detection, the system analyzes the user's social media activity to identify the cause of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 20) The alert unit is, It estimates the user's emotions and adjusts the alert intensity based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The alert unit is, When an alert is issued, the system analyzes ambient noise and sets the optimal volume. The system described in Appendix 1, characterized by the features described herein. (Note 22) The alert unit is, When an alert is issued, the color and pattern of the light change to indicate the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 23) The alert unit is, It estimates the user's emotions and adjusts how alerts are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The alert unit is, When issuing an alert, the system selects the most appropriate alert method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The alert unit is, When an alert is issued, the system analyzes the user's social media activity and generates an appropriate alert message. The system described in Appendix 1, characterized by the features described herein. (Note 26) The acquisition unit is, The system estimates the user's emotions and adjusts the frequency of location data acquisition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The acquisition unit is, When acquiring location information, the system analyzes the user's movement patterns to predict emergencies. The system described in Appendix 1, characterized by the features described herein. (Note 28) The acquisition unit is, When acquiring location information, the system refers to the user's past location data to identify the location where the anomaly occurred. The system described in Appendix 1, characterized by the features described herein. (Note 29) The acquisition unit is, The system estimates the user's emotions and adjusts the method of acquiring location information based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The acquisition unit is, When acquiring location information, the optimal acquisition method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The acquisition unit is, When acquiring location information, the system analyzes the user's social media activity to identify the location of the emergency. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A monitoring unit that monitors heart rate, A detection unit that detects abnormalities based on heart rate data monitored by the monitoring unit, An alert unit that emits an alert with sound and light based on an abnormality detected by the aforementioned detection unit, The system includes an acquisition unit that acquires location information based on an alert issued by the alert unit. A system characterized by the following features.

2. The monitoring unit, Monitor your heart rate in real time. The system according to feature 1.

3. The detection unit, Analyze heart rate data and instantly identify abnormalities. The system according to feature 1.

4. The alert unit is, It emits an alert to the surroundings with sound and light. The system according to feature 1.

5. The acquisition unit is, Acquire location information and respond quickly to emergencies. The system according to feature 1.

6. The monitoring unit, Send heart rate data to the administrator. The system according to feature 1.

7. The acquisition unit is, Location information is obtained using generative AI. The system according to feature 1.

8. The monitoring unit, It estimates the user's emotions and adjusts the accuracy of heart rate monitoring based on the estimated emotions. The system according to feature 1.

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