system

Abstract

The system according to this embodiment aims to monitor changes in physical condition or abnormalities during bathing in real time and to respond quickly. [Solution] The system according to the embodiment comprises a monitoring unit, a detection unit, a notification unit, and a safety measures unit. The monitoring unit monitors body temperature, heart rate, and movement. The detection unit detects abnormalities based on the data monitored by the monitoring unit. The notification unit sounds an alarm or wakes the user with vibration based on the abnormality detected by the detection unit. The safety measures unit automatically takes safety measures if the user does not respond to the notification unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to monitor changes and abnormalities in the physical condition during bathing in real time and respond promptly.

[0005] The system according to the embodiment aims to monitor changes and abnormalities in the physical condition during bathing in real time and respond promptly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a detection unit, a notification unit, and a safety measures unit. The monitoring unit monitors body temperature, heart rate, and movement. The detection unit detects abnormalities based on the data monitored by the monitoring unit. The notification unit sounds an alarm or wakes the user with vibration based on the abnormality detected by the detection unit. The safety measures unit automatically takes safety measures if the user does not respond to the notification unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor changes in physical condition or abnormalities during bathing in real time and respond quickly. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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] [[ID=D7]] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention provides a safety monitoring system in which sensors built into the bathtub monitor body temperature, heart rate, and movement, and if an abnormality is detected, an alarm sounds or the user is woken up with vibration. The safety monitoring system monitors body temperature, heart rate, and movement in real time using sensors built into the bathtub, and if an abnormality is detected, an alarm sounds or the user is woken up with vibration. Furthermore, if the user does not respond, safety measures are automatically taken. For example, the safety monitoring system may take measures such as automatically draining the water from the bathtub or sending a notification to an emergency contact. This mechanism can prevent accidents even if an elderly person falls asleep while bathing. Thus, the safety monitoring system can monitor body temperature, heart rate, and movement, notify the user with an alarm or vibration if an abnormality is detected, and automatically take safety measures.

[0029] The safety monitoring system according to this embodiment comprises a monitoring unit, a detection unit, a notification unit, and a safety countermeasure unit. The monitoring unit monitors body temperature, heart rate, and movement. The monitoring unit measures body temperature, for example, using a skin temperature sensor. The monitoring unit may also use a pulse oximeter to measure heart rate. Furthermore, the monitoring unit may monitor movement using an acceleration sensor. For example, the monitoring unit measures body temperature in real time using a skin temperature sensor and detects abnormal body temperature fluctuations. The monitoring unit may also measure heart rate in real time using a pulse oximeter and detect abnormal heart rate fluctuations. Furthermore, the monitoring unit may monitor movement in real time using an acceleration sensor and detect abnormal movement patterns. The detection unit detects abnormalities based on the data monitored by the monitoring unit. The detection unit may, for example, set a range for body temperature and detect an abnormality if it exceeds that range. The detection unit may also set a range for heart rate and detect an abnormality if it exceeds that range. Furthermore, the detection unit may analyze movement patterns and detect abnormal movement. For example, the detection unit detects an abnormality when the body temperature exceeds a set range. The detection unit can also detect an abnormality when the heart rate exceeds a set range. Furthermore, the detection unit can detect an abnormality when the movement pattern is abnormal. The notification unit sounds an alarm or wakes the user with vibration based on the abnormality detected by the detection unit. The notification unit can, for example, set the type and volume of sound and sound an alarm when an abnormality is detected. It can also set the strength and pattern of vibration and wake the user with vibration when an abnormality is detected. For example, the notification unit sounds an alarm and notifies the user when an abnormality is detected. It can also wake the user with vibration when an abnormality is detected. Furthermore, the notification unit can combine alarms and vibrations in its notification. The safety measures unit automatically takes safety measures if the user does not respond to the notification unit. For example, the safety measures unit automatically drains the bathtub water. The safety measures unit can also send notifications to emergency contacts. Furthermore, the safety measures unit can contact medical institutions.For example, the safety unit automatically drains the bathtub water if the user does not respond. The safety unit can also send notifications to emergency contacts to encourage a quick response. Furthermore, the safety unit can contact medical institutions to ensure appropriate medical care. As a result, the safety monitoring system according to this embodiment can monitor body temperature, heart rate, and movement, and if an abnormality is detected, it can notify with an alarm or vibration and automatically take safety measures.

[0030] The monitoring unit monitors body temperature, heart rate, and movement. For example, the monitoring unit measures body temperature using a skin temperature sensor. Specifically, the skin temperature sensor can measure body temperature in real time by directly contacting the user's skin. This sensor is sensitive to even minute temperature changes and can immediately detect rapid increases or decreases in body temperature. The monitoring unit can also use a pulse oximeter to measure heart rate. A pulse oximeter is attached to a fingertip or earlobe and measures blood oxygen saturation and heart rate by transmitting light. This device can monitor heart rate fluctuations in real time and detect abnormal patterns. Furthermore, the monitoring unit can also monitor movement using an accelerometer. The accelerometer captures the user's movement in three dimensions and detects abnormal actions such as falls or sudden movements. For example, the monitoring unit can measure body temperature in real time using a skin temperature sensor and detect abnormal body temperature fluctuations. It can also measure heart rate in real time using a pulse oximeter and detect abnormal heart rate fluctuations. Furthermore, the monitoring unit can use an acceleration sensor to monitor movement in real time and detect abnormal movement patterns. This allows the monitoring unit to comprehensively monitor the user's health status and respond quickly if an abnormality occurs.

[0031] The detection unit detects abnormalities based on data monitored by the monitoring unit. For example, the detection unit sets a range for body temperature and detects an abnormality if the temperature exceeds that range. Specifically, the detection unit pre-sets a normal body temperature range for the user and issues an alert if the temperature exceeds this range. The detection unit can also set a range for heart rate and detect an abnormality if the temperature exceeds that range. Similarly, a normal range is set for heart rate, and an abnormality is detected if the heart rate deviates from this range. Furthermore, the detection unit can analyze movement patterns and detect abnormal movements. For example, the detection unit detects an abnormality if the body temperature exceeds a set range. The detection unit can also detect an abnormality if the heart rate exceeds a set range. In addition, the detection unit can detect abnormalities if the movement pattern is abnormal. Regarding movement patterns, it detects sudden movements or falls that differ from normal movements and recognizes them as abnormalities. As a result, the detection unit can quickly and accurately detect abnormalities related to the user's health condition and prompt appropriate action.

[0032] The notification unit sounds an alarm or wakes the user with vibration based on anomalies detected by the detection unit. For example, the notification unit sets the type and volume of sound and sounds an alarm when an anomaly is detected. Specifically, the notification unit sets different types and volumes of sound depending on the type and urgency of the anomaly to issue an appropriate warning to the user. The notification unit can also set the strength and pattern of vibration and wake the user with vibration when an anomaly is detected. For example, the notification unit sounds an alarm to notify the user when an anomaly is detected. The notification unit can also wake the user with vibration when an anomaly is detected. Furthermore, the notification unit can combine alarms and vibrations in its notification. This allows the notification unit to quickly and reliably inform the user of an anomaly and encourage appropriate action. In addition, the notification unit can monitor the user's response and adjust the notification method as needed. For example, if the user does not respond to the alarm, it may increase the intensity of the vibration. This allows the notification unit to reliably inform the user of an anomaly and encourage a quick response.

[0033] The safety measures unit automatically takes safety measures if the user does not respond to the notification unit. For example, the safety measures unit automatically drains the bathtub. Specifically, if the user does not respond, the safety measures unit automatically opens the bathtub drain valve and drains the water to avoid the risk of drowning. The safety measures unit can also send notifications to emergency contacts. Emergency contacts include family, friends, and caregivers, and notifications are sent to encourage a quick response. Furthermore, the safety measures unit can also contact medical institutions. For example, if the user does not respond, the safety measures unit automatically drains the bathtub. The safety measures unit can also send notifications to emergency contacts to encourage a quick response. Furthermore, the safety measures unit can contact medical institutions to ensure appropriate medical care. In this way, the safety measures unit can automatically implement safety measures and ensure user safety even if the user does not respond. In addition, the safety measures unit can monitor the operation of the entire system and respond quickly if an anomaly occurs. For example, even if a part of the system fails, it is designed so that other parts continue to operate normally. In this way, the safety measures unit can ensure user safety and improve the reliability of the entire system.

[0034] The monitoring unit can monitor body temperature, heart rate, and movement in real time. For example, the monitoring unit can measure body temperature in real time using a skin temperature sensor. The monitoring unit can also measure heart rate in real time using a pulse oximeter. The monitoring unit can also monitor movement in real time using an accelerometer. This allows for early detection of abnormalities by monitoring body temperature, heart rate, and movement in real time. Real-time monitoring is performed based on the data update frequency and delay time. For example, the monitoring unit can achieve real-time monitoring by updating data every second and minimizing the delay time. 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 data acquired in real time into a generating AI and have the generating AI perform abnormality detection.

[0035] The detection unit can detect anomalies based on data monitored by the monitoring unit. For example, the detection unit can set a range for body temperature and detect an anomaly if it exceeds that range. The detection unit can also set a range for heart rate and detect an anomaly if it exceeds that range. The detection unit can also analyze movement patterns and detect abnormal movements. This enables a rapid response by detecting anomalies based on monitored data. The definition and criteria for anomalies are set based on body temperature, heart rate, and movement ranges and patterns. For example, the detection unit detects an anomaly if the body temperature exceeds the range of 36.5 to 37.5 degrees Celsius. The detection unit can also detect an anomaly if the heart rate exceeds the range of 60 to 100 beats per minute. The detection unit can also detect an anomaly if the movement pattern exceeds the normal range. 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 data acquired by the monitoring unit into a generating AI and have the generating AI perform anomaly detection.

[0036] The notification unit can sound an alarm or wake the user with vibration based on an anomaly detected by the detection unit. For example, the notification unit can set the type and volume of sound and sound an alarm when an anomaly is detected. The notification unit can also set the strength and pattern of vibration and wake the user with vibration when an anomaly is detected. This allows the user to be quickly notified when an anomaly is detected. Alarm and vibration settings are based on the type of sound, volume, duration, vibration strength, pattern, and duration. For example, the notification unit can sound a high-pitched alarm to notify the user when an anomaly is detected. The notification unit can also wake the user with strong vibration when an anomaly is detected. The notification unit can also combine alarm and vibration for notification. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the anomaly detected by the detection unit into a generating AI and have the generating AI execute the optimal notification method.

[0037] The safety measures unit can automatically take safety measures if the user does not respond to the notification unit. For example, the safety measures unit can automatically drain the water from the bathtub. The safety measures unit can also send a notification to an emergency contact. The safety measures unit can also contact a medical institution. This allows accidents to be prevented by automatically taking safety measures even if the user does not respond. The content and method of safety measures are set based on emergency contact, draining, contacting a medical institution, etc. For example, the safety measures unit can automatically drain the water from the bathtub if the user does not respond. The safety measures unit can also send a notification to an emergency contact to encourage a quick response. The safety measures unit can also contact a medical institution to ensure appropriate medical care. Some or all of the above processes in the safety measures unit may be performed using AI, for example, or without AI. For example, the safety measures unit can input information into a generating AI when the user does not respond, and have the generating AI execute the optimal safety measures.

[0038] The safety unit can automatically drain the water from the bathtub. For example, the safety unit can set the drainage speed and start conditions, and automatically drain the water from the bathtub if an abnormality is detected. The safety unit can also adjust the drainage speed to drain the water quickly. The safety unit can also set drainage start conditions and automatically start draining when an abnormality is detected. This reduces the risk of drowning by automatically draining the water from the bathtub. The drainage method and standards are set based on the drainage speed and drainage start conditions. For example, the safety unit can set the drainage speed to the maximum when an abnormality is detected to drain the water quickly. The safety unit can also set conditions to automatically start draining when an abnormality is detected. Some or all of the above processing in the safety unit may be performed using AI, for example, or without AI. For example, the safety unit can input the detected abnormality into a generating AI and have the generating AI execute the drainage start.

[0039] The Security Measures Department can send notifications to emergency contacts. The Security Measures Department can, for example, set up emergency contact information and automatically send notifications when an anomaly is detected. The Security Measures Department can also, for example, make phone calls to emergency contacts. The Security Measures Department can also, for example, send emails to emergency contacts. This allows for a quick response by sending notifications to emergency contacts. The definition and criteria for emergency contacts are set based on family, medical institutions, police, etc. For example, the Security Measures Department will call family members when an anomaly is detected. The Security Measures Department can also, for example, send emails to medical institutions when an anomaly is detected. The Security Measures Department can also, for example, send notifications to the police when an anomaly is detected. Some or all of the above processes in the Security Measures Department may be performed using AI, for example, or not using AI. For example, when an anomaly is detected, the Security Measures Department can input it into a generating AI and have the generating AI execute a notification to emergency contacts.

[0040] 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 body temperature data to detect abnormal body temperature fluctuations early. The monitoring unit can also refer to the user's past heart rate data to detect abnormal heart rate fluctuations early. The monitoring unit can also refer to the user's past movement data to detect abnormal movement patterns early. This makes it possible to detect abnormalities early by referring to past health data. The type and method of referencing past health data are set based on electronic medical records, wearable device data, etc. For example, the monitoring unit can refer to the user's past body temperature data to detect abnormal body temperature fluctuations early. The monitoring unit can also refer to the user's past heart rate data to detect abnormal heart rate fluctuations early. The monitoring unit can also refer to the user's past movement data to detect abnormal movement patterns early. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the user's past health data into a generating AI, allowing the AI ​​to perform early detection of abnormalities.

[0041] The monitoring unit can improve the accuracy of monitoring by considering the user's bathing history during monitoring. For example, the monitoring unit can refer to the user's past bathing time to detect abnormally long bathing sessions early. The monitoring unit can also refer to the user's past bathing frequency to detect abnormal fluctuations in frequency early. The monitoring unit can also refer to the user's past bathing temperature to detect abnormal temperature fluctuations early. This improves the accuracy of monitoring by considering the bathing history. The type and method of referencing the bathing history are set based on bathing time, bathing frequency, bathing temperature, etc. For example, the monitoring unit can refer to the user's past bathing time to detect abnormally long bathing sessions early. The monitoring unit can also refer to the user's past bathing frequency to detect abnormal fluctuations in frequency early. The monitoring unit can also refer to the user's past bathing temperature to detect abnormal temperature fluctuations early. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input user bathing history data into a generating AI, which can then perform tasks to improve the accuracy of the monitoring.

[0042] The monitoring unit can determine monitoring priorities by referring to the user's lifestyle data during monitoring. For example, the monitoring unit can refer to the user's sleep data and prioritize monitoring heart rate if the user is sleep-deprived. The monitoring unit can also refer to the user's diet data and prioritize monitoring body temperature after meals. The monitoring unit can also refer to the user's exercise data and prioritize monitoring heart rate after exercise. This allows for appropriate determination of monitoring priorities by referring to lifestyle data. The types and methods of referencing lifestyle data are set based on diet, exercise, sleep, etc. For example, the monitoring unit can refer to the user's sleep data and prioritize monitoring heart rate if the user is sleep-deprived. The monitoring unit can also refer to the user's diet data and prioritize monitoring body temperature after meals. The monitoring unit can also refer to the user's exercise data and prioritize monitoring heart rate after exercise. 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 user lifestyle data into a generating AI and have the generating AI determine the priorities for monitoring.

[0043] The monitoring unit can improve the accuracy of monitoring by considering the user's meal data during monitoring. For example, the monitoring unit may refer to the user's meal content and monitor changes in body temperature after eating. The monitoring unit may also refer to the user's meal time and monitor changes in heart rate after eating. The monitoring unit may also refer to the user's meal amount and monitor changes in movement after eating. This improves the accuracy of monitoring by considering meal data. The type and method of referencing meal data are set based on the content of the meal, calorie intake, meal time, etc. For example, the monitoring unit may refer to the user's meal content and monitor changes in body temperature after eating. The monitoring unit may also refer to the user's meal time and monitor changes in heart rate after eating. The monitoring unit may also refer to the user's meal amount and monitor changes in movement after eating. 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 meal data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0044] The detection unit can improve detection accuracy by referring to the user's past abnormal data when detection occurs. For example, the detection unit can refer to the user's past abnormal body temperature data to detect abnormal body temperature fluctuations early. The detection unit can also refer to the user's past abnormal heart rate data to detect abnormal heart rate fluctuations early. The detection unit can also refer to the user's past abnormal movement data to detect abnormal movement patterns early. This improves detection accuracy by referring to past abnormal data. The type and method of referencing past abnormal data are set based on the type of abnormality, the date and time of occurrence, the response method, etc. For example, the detection unit can refer to the user's past abnormal body temperature data to detect abnormal body temperature fluctuations early. The detection unit can also refer to the user's past abnormal heart rate data to detect abnormal heart rate fluctuations early. The detection unit can also refer to the user's past abnormal movement data to detect abnormal movement patterns early. 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 the generating AI, causing the generating AI to improve the accuracy of the detection.

[0045] The detection unit can optimize the anomaly detection algorithm based on the user's age and gender when detection occurs. For example, the detection unit can set criteria for abnormal body temperature fluctuations based on the user's age. The detection unit can also set criteria for abnormal heart rate based on the user's gender. The detection unit can also set criteria for abnormal movement based on the user's age and gender. This allows for more accurate anomaly detection by optimizing the anomaly detection algorithm based on age and gender. The age and gender ranges and criteria are set based on categories such as children, adults, the elderly, males, females, and others. For example, the detection unit can set criteria for abnormal body temperature fluctuations based on the user's age. The detection unit can also set criteria for abnormal heart rate based on the user's gender. The detection unit can also set criteria for abnormal movement based on the user's age and gender. 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 age and gender data into a generating AI and have the generating AI optimize the anomaly detection algorithm.

[0046] The detection unit can improve the accuracy of anomaly detection by referring to the user's exercise data when detecting anomalies. For example, the detection unit can refer to the user's post-exercise body temperature data to detect abnormal body temperature fluctuations early. The detection unit can also refer to the user's post-exercise heart rate data to detect abnormal heart rate fluctuations early. The detection unit can also refer to the user's post-exercise movement data to detect abnormal movement patterns early. This improves the accuracy of anomaly detection by referring to exercise data. The type and method of referencing exercise data are set based on the type of exercise, exercise intensity, exercise frequency, etc. For example, the detection unit can refer to the user's post-exercise body temperature data to detect abnormal body temperature fluctuations early. The detection unit can also refer to the user's post-exercise heart rate data to detect abnormal heart rate fluctuations early. The detection unit can also refer to the user's post-exercise movement data to detect abnormal movement patterns early. 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 user movement data into a generating AI, which can then perform tasks to improve the accuracy of anomaly detection.

[0047] The detection unit can optimize its anomaly detection algorithm by considering the user's sleep data when detecting an anomaly. For example, the detection unit can refer to the user's body temperature data when sleep-deprived to detect abnormal body temperature fluctuations early. The detection unit can also refer to the user's heart rate data when sleep-deprived to detect abnormal heart rate fluctuations early. The detection unit can also refer to the user's movement data when sleep-deprived to detect abnormal movement patterns early. As a result, by considering sleep data, the anomaly detection algorithm is optimized and accuracy is improved. The type and method of referencing sleep data are set based on sleep duration, sleep quality, sleep patterns, etc. For example, the detection unit can refer to the user's body temperature data when sleep-deprived to detect abnormal body temperature fluctuations early. The detection unit can also refer to the user's heart rate data when sleep-deprived to detect abnormal heart rate fluctuations early. The detection unit can also refer to the user's movement data when sleep-deprived to detect abnormal movement patterns early. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user sleep data into a generating AI and have the generating AI optimize the anomaly detection algorithm.

[0048] The notification unit can select the optimal notification method by referring to the user's past response data when sending a notification. For example, the notification unit may prioritize using notification methods to which the user has responded in the past. The notification unit may also avoid notification methods to which the user has not responded in the past. The notification unit may also analyze the user's past response data to select the optimal notification method. This allows the optimal notification method to be selected by referring to past response data. The type and method of referencing past response data are set based on the response time to the notification, the type of response, etc. For example, the notification unit may prioritize using notification methods to which the user has responded in the past. The notification unit may also avoid notification methods to which the user has not responded in the past. The notification unit may also analyze the user's past response data to select the optimal notification method. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the user's past response data into a generating AI and have the generating AI select the optimal notification method.

[0049] The notification unit can customize the notification method based on the user's hearing and vision status when a notification is sent. For example, if the user's hearing is impaired, the notification unit may notify by vibration. For example, if the user's vision is impaired, the notification unit may also notify by sound. For example, the notification unit may also select the optimal notification method based on the user's hearing and vision status. This allows for more effective notifications by customizing the notification method based on the user's hearing and vision status. The types and criteria for hearing and vision status are set based on hearing test results, hearing aid usage, vision test results, eyeglass usage, etc. For example, if the user's hearing is impaired, the notification unit may notify by vibration. For example, if the user's vision is impaired, the notification unit may also notify by sound. For example, the notification unit may also select the optimal notification method based on the user's hearing and vision status. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input the user's hearing and vision data into a generating AI and have the generating AI perform the customization of the notification method.

[0050] The notification unit can select the optimal notification method by referring to the user's device information when a notification is sent. For example, if the user is using a smartphone, the notification unit will notify using a combination of sound and vibration. For example, if the user is using a tablet, the notification unit can also notify using a screen display and sound. For example, if the user is using a smartwatch, the notification unit can also notify using vibration. This allows the system to select the optimal notification method by referring to device information. The type of device information and the method of reference are set based on the device, such as a smartphone, tablet, or wearable device. For example, if the user is using a smartphone, the notification unit will notify using a combination of sound and vibration. For example, if the user is using a tablet, the notification unit can also notify using a screen display and sound. For example, if the user is using a smartwatch, the notification unit can also notify using vibration. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI and have the generating AI select the optimal notification method.

[0051] The notification unit can optimize the notification method by considering the user's environmental data when sending a notification. For example, if the user is in a quiet environment, the notification unit may notify with sound. For example, if the user is in a noisy environment, the notification unit may notify with vibration. The notification unit can also analyze the user's environmental data and select the optimal notification method. This allows the optimal notification method to be selected by considering the environmental data. The type and method of referencing environmental data are set based on temperature, humidity, noise level, etc. For example, if the user is in a quiet environment, the notification unit may notify with sound. For example, if the user is in a noisy environment, the notification unit may notify with vibration. The notification unit can also analyze the user's environmental data and select the optimal notification method. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's environmental data into a generating AI and have the generating AI perform the optimization of the notification method.

[0052] The safety measures unit can select the optimal safety measures by referring to the user's past accident data when implementing safety measures. For example, the safety measures unit can refer to the user's past fall accident data and implement safety measures to prevent falls. For example, the safety measures unit can refer to the user's past drowning accident data and implement safety measures to prevent drowning. For example, the safety measures unit can comprehensively analyze the user's past accident data and select the optimal safety measures. This allows for the selection of the optimal safety measures by referring to past accident data. The types and methods of referencing past accident data are set based on the type of accident, the date and time of occurrence, the response method, etc. For example, the safety measures unit can refer to the user's past fall accident data and implement safety measures to prevent falls. For example, the safety measures unit can refer to the user's past drowning accident data and implement safety measures to prevent drowning. For example, the safety measures unit can comprehensively analyze the user's past accident data and select the optimal safety measures. Some or all of the above processing in the safety measures unit may be performed using AI, for example, or without using AI. For example, the safety measures department can input the user's past accident data into a generating AI and have the AI ​​select the optimal safety measures.

[0053] The safety measures unit can customize safety measures based on the user's health condition. For example, if the user's health condition is good, the safety measures unit can implement mild safety measures. For example, if the user's health condition is deteriorating, the safety measures unit can also implement strong safety measures. For example, the safety measures unit can also select the optimal safety measures based on the user's health condition. This allows for more appropriate safety measures by customizing safety measures based on health condition. The type and method of referencing health condition are set based on medical history, current health condition, medication information, etc. For example, if the user's health condition is good, the safety measures unit can implement mild safety measures. For example, if the user's health condition is deteriorating, the safety measures unit can also implement strong safety measures. For example, the safety measures unit can also select the optimal safety measures based on the user's health condition. Some or all of the above processing in the safety measures unit may be performed using AI, for example, or without AI. For example, the safety measures unit can input user health condition data into a generating AI and have the generating AI perform the customization of safety measures.

[0054] The safety measures unit can select the optimal safety measures by referring to the user's residence information when implementing safety measures. For example, if the user's residence is a high-rise apartment building, the safety measures unit will implement safety measures that take into account the use of elevators. For example, if the user's residence is a detached house, the safety measures unit can also implement safety measures that take into account the use of stairs. The safety measures unit can also select the optimal safety measures by comprehensively analyzing the user's residence information. This allows for the selection of the optimal safety measures by referring to the residence information. The type and method of referencing residence information are set based on the address, the structure of the residence, the facilities of the residence, etc. For example, if the user's residence is a high-rise apartment building, the safety measures unit will implement safety measures that take into account the use of elevators. For example, if the user's residence is a detached house, the safety measures unit can also implement safety measures that take into account the use of stairs. The safety measures unit can also select the optimal safety measures by comprehensively analyzing the user's residence information. Some or all of the above processing in the safety measures unit may be performed using AI, for example, or without using AI. For example, the safety measures department can input the user's residential information into a generating AI and have the AI ​​select the most suitable safety measures.

[0055] The security measures unit can optimize security measures by considering the user's family structure data when implementing security measures. For example, if the user lives alone, the security measures unit will prioritize notifying emergency contacts. For example, if the user lives with family, the security measures unit may also prioritize notifying family members. The security measures unit can also comprehensively analyze the user's family structure data and select the most appropriate security measures. This allows for the selection of the most appropriate security measures by considering family structure data. The type and method of referencing family structure data are set based on the number of family members, ages, health status, etc. For example, if the user lives alone, the security measures unit will prioritize notifying emergency contacts. For example, if the user lives with family, the security measures unit may also prioritize notifying family members. The security measures unit can also comprehensively analyze the user's family structure data and select the most appropriate security measures. Some or all of the above processing in the security measures unit may be performed using AI, for example, or without AI. For example, the safety measures department can input the user's family structure data into a generating AI and have the AI ​​optimize the means of safety measures.

[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 safety monitoring system can detect abnormalities early by referring to the user's past health data. For example, the monitoring unit can refer to the user's past body temperature data to detect abnormal temperature fluctuations early. It can also refer to the user's past heart rate data to detect abnormal heart rate fluctuations early. Furthermore, it can refer to the user's past movement data to detect abnormal movement patterns early. This makes it possible to detect abnormalities early by utilizing past health data.

[0058] The safety monitoring system can improve its monitoring accuracy by considering the user's bathing history. For example, the monitoring unit can refer to the user's past bathing times to detect abnormally long bathing sessions early. It can also refer to the user's past bathing frequency to detect abnormal fluctuations in frequency early. Furthermore, it can refer to the user's past bathing temperatures to detect abnormal temperature fluctuations early. In this way, the accuracy of monitoring is improved by considering the bathing history.

[0059] The safety monitoring system can determine monitoring priorities by referring to the user's lifestyle data. For example, the monitoring unit can refer to the user's sleep data and prioritize heart rate monitoring if the user is sleep-deprived. It can also refer to the user's diet data and prioritize monitoring body temperature after meals. Furthermore, it can refer to the user's exercise data and prioritize monitoring heart rate after exercise. This allows for appropriate determination of monitoring priorities by utilizing lifestyle data.

[0060] The safety monitoring system can optimize its anomaly detection algorithm based on the user's age and gender. For example, the detection unit can set criteria for abnormal body temperature fluctuations based on the user's age. It can also set criteria for abnormal heart rate based on the user's gender. Furthermore, it can set criteria for abnormal movement based on the user's age and gender. This enables highly accurate anomaly detection based on age and gender.

[0061] The safety monitoring system can improve the accuracy of anomaly detection by referencing the user's exercise data. For example, the detection unit can refer to the user's post-exercise body temperature data to detect abnormal body temperature fluctuations early. It can also refer to the user's post-exercise heart rate data to detect abnormal heart rate fluctuations early. Furthermore, it can refer to the user's post-exercise movement data to detect abnormal movement patterns early. This improves the accuracy of anomaly detection by utilizing exercise data.

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

[0063] Step 1: The monitoring unit monitors body temperature, heart rate, and movement. For example, it measures body temperature using a skin temperature sensor, measures heart rate using a pulse oximeter, and monitors movement using an accelerometer. Step 2: The detection unit detects anomalies based on data monitored by the monitoring unit. For example, it can set ranges for body temperature or heart rate and detect anomalies if those ranges are exceeded. It can also analyze movement patterns and detect abnormal movements. Step 3: The notification unit sounds an alarm or wakes the user with vibration based on the anomaly detected by the detection unit. For example, the type and volume of sound can be set to sound an alarm when an anomaly is detected. Alternatively, the strength and pattern of the vibration can be set to wake the user with vibration when an anomaly is detected. Step 4: The safety measures unit automatically takes safety measures if the user does not respond to the notification unit. For example, it can automatically drain the bathtub water or send a notification to emergency contacts. It can also contact medical facilities.

[0064] (Example of form 2) An embodiment of the present invention provides a safety monitoring system in which sensors built into the bathtub monitor body temperature, heart rate, and movement, and if an abnormality is detected, an alarm sounds or the user is woken up with vibration. The safety monitoring system monitors body temperature, heart rate, and movement in real time using sensors built into the bathtub, and if an abnormality is detected, an alarm sounds or the user is woken up with vibration. Furthermore, if the user does not respond, safety measures are automatically taken. For example, the safety monitoring system may take measures such as automatically draining the water from the bathtub or sending a notification to an emergency contact. This mechanism can prevent accidents even if an elderly person falls asleep while bathing. Thus, the safety monitoring system can monitor body temperature, heart rate, and movement, notify the user with an alarm or vibration if an abnormality is detected, and automatically take safety measures.

[0065] The safety monitoring system according to this embodiment comprises a monitoring unit, a detection unit, a notification unit, and a safety countermeasure unit. The monitoring unit monitors body temperature, heart rate, and movement. The monitoring unit measures body temperature, for example, using a skin temperature sensor. The monitoring unit may also use a pulse oximeter to measure heart rate. Furthermore, the monitoring unit may monitor movement using an acceleration sensor. For example, the monitoring unit measures body temperature in real time using a skin temperature sensor and detects abnormal body temperature fluctuations. The monitoring unit may also measure heart rate in real time using a pulse oximeter and detect abnormal heart rate fluctuations. Furthermore, the monitoring unit may monitor movement in real time using an acceleration sensor and detect abnormal movement patterns. The detection unit detects abnormalities based on the data monitored by the monitoring unit. The detection unit may, for example, set a range for body temperature and detect an abnormality if it exceeds that range. The detection unit may also set a range for heart rate and detect an abnormality if it exceeds that range. Furthermore, the detection unit may analyze movement patterns and detect abnormal movement. For example, the detection unit detects an abnormality when the body temperature exceeds a set range. The detection unit can also detect an abnormality when the heart rate exceeds a set range. Furthermore, the detection unit can detect an abnormality when the movement pattern is abnormal. The notification unit sounds an alarm or wakes the user with vibration based on the abnormality detected by the detection unit. The notification unit can, for example, set the type and volume of sound and sound an alarm when an abnormality is detected. It can also set the strength and pattern of vibration and wake the user with vibration when an abnormality is detected. For example, the notification unit sounds an alarm and notifies the user when an abnormality is detected. It can also wake the user with vibration when an abnormality is detected. Furthermore, the notification unit can combine alarms and vibrations in its notification. The safety measures unit automatically takes safety measures if the user does not respond to the notification unit. For example, the safety measures unit automatically drains the bathtub water. The safety measures unit can also send notifications to emergency contacts. Furthermore, the safety measures unit can contact medical institutions.For example, the safety unit automatically drains the bathtub water if the user does not respond. The safety unit can also send notifications to emergency contacts to encourage a quick response. Furthermore, the safety unit can contact medical institutions to ensure appropriate medical care. As a result, the safety monitoring system according to this embodiment can monitor body temperature, heart rate, and movement, and if an abnormality is detected, it can notify with an alarm or vibration and automatically take safety measures.

[0066] The monitoring unit monitors body temperature, heart rate, and movement. For example, the monitoring unit measures body temperature using a skin temperature sensor. Specifically, the skin temperature sensor can measure body temperature in real time by directly contacting the user's skin. This sensor is sensitive to even minute temperature changes and can immediately detect rapid increases or decreases in body temperature. The monitoring unit can also use a pulse oximeter to measure heart rate. A pulse oximeter is attached to a fingertip or earlobe and measures blood oxygen saturation and heart rate by transmitting light. This device can monitor heart rate fluctuations in real time and detect abnormal patterns. Furthermore, the monitoring unit can also monitor movement using an accelerometer. The accelerometer captures the user's movement in three dimensions and detects abnormal actions such as falls or sudden movements. For example, the monitoring unit can measure body temperature in real time using a skin temperature sensor and detect abnormal body temperature fluctuations. It can also measure heart rate in real time using a pulse oximeter and detect abnormal heart rate fluctuations. Furthermore, the monitoring unit can use an acceleration sensor to monitor movement in real time and detect abnormal movement patterns. This allows the monitoring unit to comprehensively monitor the user's health status and respond quickly if an abnormality occurs.

[0067] The detection unit detects abnormalities based on data monitored by the monitoring unit. For example, the detection unit sets a range for body temperature and detects an abnormality if the temperature exceeds that range. Specifically, the detection unit pre-sets a normal body temperature range for the user and issues an alert if the temperature exceeds this range. The detection unit can also set a range for heart rate and detect an abnormality if the temperature exceeds that range. Similarly, a normal range is set for heart rate, and an abnormality is detected if the heart rate deviates from this range. Furthermore, the detection unit can analyze movement patterns and detect abnormal movements. For example, the detection unit detects an abnormality if the body temperature exceeds a set range. The detection unit can also detect an abnormality if the heart rate exceeds a set range. In addition, the detection unit can detect abnormalities if the movement pattern is abnormal. Regarding movement patterns, it detects sudden movements or falls that differ from normal movements and recognizes them as abnormalities. As a result, the detection unit can quickly and accurately detect abnormalities related to the user's health condition and prompt appropriate action.

[0068] The notification unit sounds an alarm or wakes the user with vibration based on anomalies detected by the detection unit. For example, the notification unit sets the type and volume of sound and sounds an alarm when an anomaly is detected. Specifically, the notification unit sets different types and volumes of sound depending on the type and urgency of the anomaly to issue an appropriate warning to the user. The notification unit can also set the strength and pattern of vibration and wake the user with vibration when an anomaly is detected. For example, the notification unit sounds an alarm to notify the user when an anomaly is detected. The notification unit can also wake the user with vibration when an anomaly is detected. Furthermore, the notification unit can combine alarms and vibrations in its notification. This allows the notification unit to quickly and reliably inform the user of an anomaly and encourage appropriate action. In addition, the notification unit can monitor the user's response and adjust the notification method as needed. For example, if the user does not respond to the alarm, it may increase the intensity of the vibration. This allows the notification unit to reliably inform the user of an anomaly and encourage a quick response.

[0069] The safety measures unit automatically takes safety measures if the user does not respond to the notification unit. For example, the safety measures unit automatically drains the bathtub. Specifically, if the user does not respond, the safety measures unit automatically opens the bathtub drain valve and drains the water to avoid the risk of drowning. The safety measures unit can also send notifications to emergency contacts. Emergency contacts include family, friends, and caregivers, and notifications are sent to encourage a quick response. Furthermore, the safety measures unit can also contact medical institutions. For example, if the user does not respond, the safety measures unit automatically drains the bathtub. The safety measures unit can also send notifications to emergency contacts to encourage a quick response. Furthermore, the safety measures unit can contact medical institutions to ensure appropriate medical care. In this way, the safety measures unit can automatically implement safety measures and ensure user safety even if the user does not respond. In addition, the safety measures unit can monitor the operation of the entire system and respond quickly if an anomaly occurs. For example, even if a part of the system fails, it is designed so that other parts continue to operate normally. In this way, the safety measures unit can ensure user safety and improve the reliability of the entire system.

[0070] The monitoring unit can monitor body temperature, heart rate, and movement in real time. For example, the monitoring unit can measure body temperature in real time using a skin temperature sensor. The monitoring unit can also measure heart rate in real time using a pulse oximeter. The monitoring unit can also monitor movement in real time using an accelerometer. This allows for early detection of abnormalities by monitoring body temperature, heart rate, and movement in real time. Real-time monitoring is performed based on the data update frequency and delay time. For example, the monitoring unit can achieve real-time monitoring by updating data every second and minimizing the delay time. 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 data acquired in real time into a generating AI and have the generating AI perform abnormality detection.

[0071] The detection unit can detect anomalies based on data monitored by the monitoring unit. For example, the detection unit can set a range for body temperature and detect an anomaly if it exceeds that range. The detection unit can also set a range for heart rate and detect an anomaly if it exceeds that range. The detection unit can also analyze movement patterns and detect abnormal movements. This enables a rapid response by detecting anomalies based on monitored data. The definition and criteria for anomalies are set based on body temperature, heart rate, and movement ranges and patterns. For example, the detection unit detects an anomaly if the body temperature exceeds the range of 36.5 to 37.5 degrees Celsius. The detection unit can also detect an anomaly if the heart rate exceeds the range of 60 to 100 beats per minute. The detection unit can also detect an anomaly if the movement pattern exceeds the normal range. 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 data acquired by the monitoring unit into a generating AI and have the generating AI perform anomaly detection.

[0072] The notification unit can sound an alarm or wake the user with vibration based on an anomaly detected by the detection unit. For example, the notification unit can set the type and volume of sound and sound an alarm when an anomaly is detected. The notification unit can also set the strength and pattern of vibration and wake the user with vibration when an anomaly is detected. This allows the user to be quickly notified when an anomaly is detected. Alarm and vibration settings are based on the type of sound, volume, duration, vibration strength, pattern, and duration. For example, the notification unit can sound a high-pitched alarm to notify the user when an anomaly is detected. The notification unit can also wake the user with strong vibration when an anomaly is detected. The notification unit can also combine alarm and vibration for notification. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the anomaly detected by the detection unit into a generating AI and have the generating AI execute the optimal notification method.

[0073] The safety measures unit can automatically take safety measures if the user does not respond to the notification unit. For example, the safety measures unit can automatically drain the water from the bathtub. The safety measures unit can also send a notification to an emergency contact. The safety measures unit can also contact a medical institution. This allows accidents to be prevented by automatically taking safety measures even if the user does not respond. The content and method of safety measures are set based on emergency contact, draining, contacting a medical institution, etc. For example, the safety measures unit can automatically drain the water from the bathtub if the user does not respond. The safety measures unit can also send a notification to an emergency contact to encourage a quick response. The safety measures unit can also contact a medical institution to ensure appropriate medical care. Some or all of the above processes in the safety measures unit may be performed using AI, for example, or without AI. For example, the safety measures unit can input information into a generating AI when the user does not respond, and have the generating AI execute the optimal safety measures.

[0074] The safety unit can automatically drain the water from the bathtub. For example, the safety unit can set the drainage speed and start conditions, and automatically drain the water from the bathtub if an abnormality is detected. The safety unit can also adjust the drainage speed to drain the water quickly. The safety unit can also set drainage start conditions and automatically start draining when an abnormality is detected. This reduces the risk of drowning by automatically draining the water from the bathtub. The drainage method and standards are set based on the drainage speed and drainage start conditions. For example, the safety unit can set the drainage speed to the maximum when an abnormality is detected to drain the water quickly. The safety unit can also set conditions to automatically start draining when an abnormality is detected. Some or all of the above processing in the safety unit may be performed using AI, for example, or without AI. For example, the safety unit can input the detected abnormality into a generating AI and have the generating AI execute the drainage start.

[0075] The Security Measures Department can send notifications to emergency contacts. The Security Measures Department can, for example, set up emergency contact information and automatically send notifications when an anomaly is detected. The Security Measures Department can also, for example, make phone calls to emergency contacts. The Security Measures Department can also, for example, send emails to emergency contacts. This allows for a quick response by sending notifications to emergency contacts. The definition and criteria for emergency contacts are set based on family, medical institutions, police, etc. For example, the Security Measures Department will call family members when an anomaly is detected. The Security Measures Department can also, for example, send emails to medical institutions when an anomaly is detected. The Security Measures Department can also, for example, send notifications to the police when an anomaly is detected. Some or all of the above processes in the Security Measures Department may be performed using AI, for example, or not using AI. For example, when an anomaly is detected, the Security Measures Department can input it into a generating AI and have the generating AI execute a notification to emergency contacts.

[0076] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, the monitoring unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions by analyzing behavioral patterns. This allows for early detection of abnormalities while reducing stress by adjusting the monitoring frequency according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and behavioral patterns. For example, if the user is relaxed, the monitoring unit will set a low monitoring frequency to reduce stress. If the user is tense, for example, the monitoring unit will set a high monitoring frequency to aim for early detection of abnormalities. If the user is tired, for example, the monitoring unit will set a moderate monitoring frequency to maintain balance. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI adjust the monitoring frequency.

[0077] 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 body temperature data to detect abnormal body temperature fluctuations early. The monitoring unit can also refer to the user's past heart rate data to detect abnormal heart rate fluctuations early. The monitoring unit can also refer to the user's past movement data to detect abnormal movement patterns early. This makes it possible to detect abnormalities early by referring to past health data. The type and method of referencing past health data are set based on electronic medical records, wearable device data, etc. For example, the monitoring unit can refer to the user's past body temperature data to detect abnormal body temperature fluctuations early. The monitoring unit can also refer to the user's past heart rate data to detect abnormal heart rate fluctuations early. The monitoring unit can also refer to the user's past movement data to detect abnormal movement patterns early. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the user's past health data into a generating AI, allowing the AI ​​to perform early detection of abnormalities.

[0078] The monitoring unit can improve the accuracy of monitoring by considering the user's bathing history during monitoring. For example, the monitoring unit can refer to the user's past bathing time to detect abnormally long bathing sessions early. The monitoring unit can also refer to the user's past bathing frequency to detect abnormal fluctuations in frequency early. The monitoring unit can also refer to the user's past bathing temperature to detect abnormal temperature fluctuations early. This improves the accuracy of monitoring by considering the bathing history. The type and method of referencing the bathing history are set based on bathing time, bathing frequency, bathing temperature, etc. For example, the monitoring unit can refer to the user's past bathing time to detect abnormally long bathing sessions early. The monitoring unit can also refer to the user's past bathing frequency to detect abnormal fluctuations in frequency early. The monitoring unit can also refer to the user's past bathing temperature to detect abnormal temperature fluctuations early. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input user bathing history data into a generating AI, which can then perform tasks to improve the accuracy of the monitoring.

[0079] The monitoring unit can estimate the user's emotions and select items to monitor based on the estimated emotions. For example, the monitoring unit can estimate the user's emotions using facial recognition technology. The monitoring unit can also estimate the user's emotions using voice analysis technology. The monitoring unit can also estimate the user's emotions by analyzing behavioral patterns. This allows for more appropriate monitoring by selecting items to monitor according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and behavioral patterns. For example, if the user is relaxed, the monitoring unit prioritizes monitoring body temperature. If the user is tense, the monitoring unit prioritizes monitoring heart rate. If the user is tired, the monitoring unit prioritizes monitoring movement. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, or without AI. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI select the items to monitor.

[0080] The monitoring unit can determine monitoring priorities by referring to the user's lifestyle data during monitoring. For example, the monitoring unit can refer to the user's sleep data and prioritize monitoring heart rate if the user is sleep-deprived. The monitoring unit can also refer to the user's diet data and prioritize monitoring body temperature after meals. The monitoring unit can also refer to the user's exercise data and prioritize monitoring heart rate after exercise. This allows for appropriate determination of monitoring priorities by referring to lifestyle data. The types and methods of referencing lifestyle data are set based on diet, exercise, sleep, etc. For example, the monitoring unit can refer to the user's sleep data and prioritize monitoring heart rate if the user is sleep-deprived. The monitoring unit can also refer to the user's diet data and prioritize monitoring body temperature after meals. The monitoring unit can also refer to the user's exercise data and prioritize monitoring heart rate after exercise. 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 user lifestyle data into a generating AI and have the generating AI determine the priorities for monitoring.

[0081] The monitoring unit can improve the accuracy of monitoring by considering the user's meal data during monitoring. For example, the monitoring unit may refer to the user's meal content and monitor changes in body temperature after eating. The monitoring unit may also refer to the user's meal time and monitor changes in heart rate after eating. The monitoring unit may also refer to the user's meal amount and monitor changes in movement after eating. This improves the accuracy of monitoring by considering meal data. The type and method of referencing meal data are set based on the content of the meal, calorie intake, meal time, etc. For example, the monitoring unit may refer to the user's meal content and monitor changes in body temperature after eating. The monitoring unit may also refer to the user's meal time and monitor changes in heart rate after eating. The monitoring unit may also refer to the user's meal amount and monitor changes in movement after eating. 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 meal data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0082] The detection unit can estimate the user's emotions and adjust the anomaly detection criteria based on the estimated user emotions. For example, the detection unit can estimate the user's emotions using facial recognition technology. The detection unit can also estimate the user's emotions using voice analysis technology. The detection unit can also estimate the user's emotions by analyzing behavioral patterns. This allows for more appropriate anomaly detection by adjusting the anomaly detection criteria according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and behavioral patterns. For example, the detection unit relaxes the anomaly detection criteria when the user is relaxed. For example, the detection unit tightens the anomaly detection criteria when the user is tense. For example, the detection unit sets the anomaly detection criteria to a moderate level when the user is tired. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into a generating AI and have the generating AI adjust the anomaly detection criteria.

[0083] The detection unit can improve detection accuracy by referring to the user's past abnormal data when detection occurs. For example, the detection unit can refer to the user's past abnormal body temperature data to detect abnormal body temperature fluctuations early. The detection unit can also refer to the user's past abnormal heart rate data to detect abnormal heart rate fluctuations early. The detection unit can also refer to the user's past abnormal movement data to detect abnormal movement patterns early. This improves detection accuracy by referring to past abnormal data. The type and method of referencing past abnormal data are set based on the type of abnormality, the date and time of occurrence, the response method, etc. For example, the detection unit can refer to the user's past abnormal body temperature data to detect abnormal body temperature fluctuations early. The detection unit can also refer to the user's past abnormal heart rate data to detect abnormal heart rate fluctuations early. The detection unit can also refer to the user's past abnormal movement data to detect abnormal movement patterns early. 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 the generating AI, causing the generating AI to improve the accuracy of the detection.

[0084] The detection unit can optimize the anomaly detection algorithm based on the user's age and gender when detection occurs. For example, the detection unit can set criteria for abnormal body temperature fluctuations based on the user's age. The detection unit can also set criteria for abnormal heart rate based on the user's gender. The detection unit can also set criteria for abnormal movement based on the user's age and gender. This allows for more accurate anomaly detection by optimizing the anomaly detection algorithm based on age and gender. The age and gender ranges and criteria are set based on categories such as children, adults, the elderly, males, females, and others. For example, the detection unit can set criteria for abnormal body temperature fluctuations based on the user's age. The detection unit can also set criteria for abnormal heart rate based on the user's gender. The detection unit can also set criteria for abnormal movement based on the user's age and gender. 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 age and gender data into a generating AI and have the generating AI optimize the anomaly detection algorithm.

[0085] The detection unit can estimate the user's emotions and adjust the timing of anomaly detection based on the estimated user emotions. The detection unit can estimate the user's emotions using, for example, facial recognition technology. The detection unit can also estimate the user's emotions using, for example, voice analysis technology. The detection unit can also estimate the user's emotions by analyzing behavioral patterns. This allows for more appropriate detection of anomalies by adjusting the timing of anomaly detection according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and behavioral patterns. For example, the detection unit delays the timing of anomaly detection when the user is relaxed. The detection unit speeds up the timing of anomaly detection when the user is tense, for example. The detection unit sets the timing of anomaly detection to a moderate level when the user is tired, for example. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of anomaly detection.

[0086] The detection unit can improve the accuracy of anomaly detection by referring to the user's exercise data when detecting anomalies. For example, the detection unit can refer to the user's post-exercise body temperature data to detect abnormal body temperature fluctuations early. The detection unit can also refer to the user's post-exercise heart rate data to detect abnormal heart rate fluctuations early. The detection unit can also refer to the user's post-exercise movement data to detect abnormal movement patterns early. This improves the accuracy of anomaly detection by referring to exercise data. The type and method of referencing exercise data are set based on the type of exercise, exercise intensity, exercise frequency, etc. For example, the detection unit can refer to the user's post-exercise body temperature data to detect abnormal body temperature fluctuations early. The detection unit can also refer to the user's post-exercise heart rate data to detect abnormal heart rate fluctuations early. The detection unit can also refer to the user's post-exercise movement data to detect abnormal movement patterns early. 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 user movement data into a generating AI, which can then perform tasks to improve the accuracy of anomaly detection.

[0087] The detection unit can optimize its anomaly detection algorithm by considering the user's sleep data when detecting an anomaly. For example, the detection unit can refer to the user's body temperature data when sleep-deprived to detect abnormal body temperature fluctuations early. The detection unit can also refer to the user's heart rate data when sleep-deprived to detect abnormal heart rate fluctuations early. The detection unit can also refer to the user's movement data when sleep-deprived to detect abnormal movement patterns early. As a result, by considering sleep data, the anomaly detection algorithm is optimized and accuracy is improved. The type and method of referencing sleep data are set based on sleep duration, sleep quality, sleep patterns, etc. For example, the detection unit can refer to the user's body temperature data when sleep-deprived to detect abnormal body temperature fluctuations early. The detection unit can also refer to the user's heart rate data when sleep-deprived to detect abnormal heart rate fluctuations early. The detection unit can also refer to the user's movement data when sleep-deprived to detect abnormal movement patterns early. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user sleep data into a generating AI and have the generating AI optimize the anomaly detection algorithm.

[0088] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, the notification unit can estimate the user's emotions using facial recognition technology. The notification unit can also estimate the user's emotions using voice analysis technology. The notification unit can also estimate the user's emotions by analyzing behavioral patterns. This allows for more effective notifications by adjusting the notification method according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and behavioral patterns. For example, if the user is relaxed, the notification unit will sound an alarm with a gentle sound. If the user is tense, the notification unit will notify with a stronger vibration. If the user is tired, the notification unit will notify with a combination of sound and vibration. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into a generating AI and have the generating AI adjust the notification method.

[0089] The notification unit can select the optimal notification method by referring to the user's past response data when sending a notification. For example, the notification unit may prioritize using notification methods to which the user has responded in the past. The notification unit may also avoid notification methods to which the user has not responded in the past. The notification unit may also analyze the user's past response data to select the optimal notification method. This allows the optimal notification method to be selected by referring to past response data. The type and method of referencing past response data are set based on the response time to the notification, the type of response, etc. For example, the notification unit may prioritize using notification methods to which the user has responded in the past. The notification unit may also avoid notification methods to which the user has not responded in the past. The notification unit may also analyze the user's past response data to select the optimal notification method. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the user's past response data into a generating AI and have the generating AI select the optimal notification method.

[0090] The notification unit can customize the notification method based on the user's hearing and vision status when a notification is sent. For example, if the user's hearing is impaired, the notification unit may notify by vibration. For example, if the user's vision is impaired, the notification unit may also notify by sound. For example, the notification unit may also select the optimal notification method based on the user's hearing and vision status. This allows for more effective notifications by customizing the notification method based on the user's hearing and vision status. The types and criteria for hearing and vision status are set based on hearing test results, hearing aid usage, vision test results, eyeglass usage, etc. For example, if the user's hearing is impaired, the notification unit may notify by vibration. For example, if the user's vision is impaired, the notification unit may also notify by sound. For example, the notification unit may also select the optimal notification method based on the user's hearing and vision status. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input the user's hearing and vision data into a generating AI and have the generating AI perform the customization of the notification method.

[0091] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. The notification unit can estimate the user's emotions using, for example, facial recognition technology. The notification unit can also estimate the user's emotions using, for example, voice analysis technology. The notification unit can also estimate the user's emotions by analyzing behavioral patterns. This allows for more appropriate notifications by determining the priority of notifications according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and behavioral patterns. For example, the notification unit sets the notification priority low when the user is relaxed. The notification unit sets the notification priority high when the user is tense, for example. The notification unit sets the notification priority medium when the user is tired, for example. Emotion estimation is implemented using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using, for example, AI, or without AI. For example, the notification unit can input user emotion data into a generating AI and have the AI ​​determine the priority of notifications.

[0092] The notification unit can select the optimal notification method by referring to the user's device information when a notification is sent. For example, if the user is using a smartphone, the notification unit will notify using a combination of sound and vibration. For example, if the user is using a tablet, the notification unit can also notify using a screen display and sound. For example, if the user is using a smartwatch, the notification unit can also notify using vibration. This allows the system to select the optimal notification method by referring to device information. The type of device information and the method of reference are set based on the device, such as a smartphone, tablet, or wearable device. For example, if the user is using a smartphone, the notification unit will notify using a combination of sound and vibration. For example, if the user is using a tablet, the notification unit can also notify using a screen display and sound. For example, if the user is using a smartwatch, the notification unit can also notify using vibration. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI and have the generating AI select the optimal notification method.

[0093] The notification unit can optimize the notification method by considering the user's environmental data when sending a notification. For example, if the user is in a quiet environment, the notification unit may notify with sound. For example, if the user is in a noisy environment, the notification unit may notify with vibration. The notification unit can also analyze the user's environmental data and select the optimal notification method. This allows the optimal notification method to be selected by considering the environmental data. The type and method of referencing environmental data are set based on temperature, humidity, noise level, etc. For example, if the user is in a quiet environment, the notification unit may notify with sound. For example, if the user is in a noisy environment, the notification unit may notify with vibration. The notification unit can also analyze the user's environmental data and select the optimal notification method. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's environmental data into a generating AI and have the generating AI perform the optimization of the notification method.

[0094] The safety measures unit can estimate the user's emotions and adjust safety measures based on those emotions. For example, the safety measures unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions by analyzing behavioral patterns. This allows for more appropriate safety measures by adjusting safety measures according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and behavioral patterns. For example, if the user is relaxed, the safety measures unit will implement safety measures in a gentle manner. If the user is tense, the safety measures unit will implement safety measures in a rapid and forceful manner. If the user is tired, the safety measures unit will implement safety measures in a balanced manner. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the safety measures department may be performed using AI, for example, or without AI. For example, the safety measures department may input user emotion data into a generating AI and have the generating AI adjust the safety measures.

[0095] The safety measures unit can select the optimal safety measures by referring to the user's past accident data when implementing safety measures. For example, the safety measures unit can refer to the user's past fall accident data and implement safety measures to prevent falls. For example, the safety measures unit can refer to the user's past drowning accident data and implement safety measures to prevent drowning. For example, the safety measures unit can comprehensively analyze the user's past accident data and select the optimal safety measures. This allows for the selection of the optimal safety measures by referring to past accident data. The types and methods of referencing past accident data are set based on the type of accident, the date and time of occurrence, the response method, etc. For example, the safety measures unit can refer to the user's past fall accident data and implement safety measures to prevent falls. For example, the safety measures unit can refer to the user's past drowning accident data and implement safety measures to prevent drowning. For example, the safety measures unit can comprehensively analyze the user's past accident data and select the optimal safety measures. Some or all of the above processing in the safety measures unit may be performed using AI, for example, or without using AI. For example, the safety measures department can input the user's past accident data into a generating AI and have the AI ​​select the optimal safety measures.

[0096] The safety measures unit can customize safety measures based on the user's health condition. For example, if the user's health condition is good, the safety measures unit can implement mild safety measures. For example, if the user's health condition is deteriorating, the safety measures unit can also implement strong safety measures. For example, the safety measures unit can also select the optimal safety measures based on the user's health condition. This allows for more appropriate safety measures by customizing safety measures based on health condition. The type and method of referencing health condition are set based on medical history, current health condition, medication information, etc. For example, if the user's health condition is good, the safety measures unit can implement mild safety measures. For example, if the user's health condition is deteriorating, the safety measures unit can also implement strong safety measures. For example, the safety measures unit can also select the optimal safety measures based on the user's health condition. Some or all of the above processing in the safety measures unit may be performed using AI, for example, or without AI. For example, the safety measures unit can input user health condition data into a generating AI and have the generating AI perform the customization of safety measures.

[0097] The safety measures unit can estimate the user's emotions and determine the priority of safety measures based on the estimated emotions. The safety measures unit can estimate the user's emotions using, for example, facial recognition technology. The safety measures unit can also estimate the user's emotions using, for example, voice analysis technology. The safety measures unit can also estimate the user's emotions by analyzing behavioral patterns. This allows for more appropriate safety measures by determining the priority of safety measures according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition, voice analysis, and behavioral patterns. For example, if the user is relaxed, the safety measures unit will set the priority of safety measures low. If the user is tense, for example, the safety measures unit will set the priority of safety measures high. If the user is tired, for example, the safety measures unit will set the priority of safety measures to a medium level. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the safety measures department may be performed using AI, for example, or without AI. For example, the safety measures department can input user emotion data into a generating AI and have the generating AI determine the priority of safety measures.

[0098] The safety measures unit can select the optimal safety measures by referring to the user's residence information when implementing safety measures. For example, if the user's residence is a high-rise apartment building, the safety measures unit will implement safety measures that take into account the use of elevators. For example, if the user's residence is a detached house, the safety measures unit can also implement safety measures that take into account the use of stairs. The safety measures unit can also select the optimal safety measures by comprehensively analyzing the user's residence information. This allows for the selection of the optimal safety measures by referring to the residence information. The type and method of referencing residence information are set based on the address, the structure of the residence, the facilities of the residence, etc. For example, if the user's residence is a high-rise apartment building, the safety measures unit will implement safety measures that take into account the use of elevators. For example, if the user's residence is a detached house, the safety measures unit can also implement safety measures that take into account the use of stairs. The safety measures unit can also select the optimal safety measures by comprehensively analyzing the user's residence information. Some or all of the above processing in the safety measures unit may be performed using AI, for example, or without using AI. For example, the safety measures department can input the user's residential information into a generating AI and have the AI ​​select the most suitable safety measures.

[0099] The security measures unit can optimize security measures by considering the user's family structure data when implementing security measures. For example, if the user lives alone, the security measures unit will prioritize notifying emergency contacts. For example, if the user lives with family, the security measures unit may also prioritize notifying family members. The security measures unit can also comprehensively analyze the user's family structure data and select the most appropriate security measures. This allows for the selection of the most appropriate security measures by considering family structure data. The type and method of referencing family structure data are set based on the number of family members, ages, health status, etc. For example, if the user lives alone, the security measures unit will prioritize notifying emergency contacts. For example, if the user lives with family, the security measures unit may also prioritize notifying family members. The security measures unit can also comprehensively analyze the user's family structure data and select the most appropriate security measures. Some or all of the above processing in the security measures unit may be performed using AI, for example, or without AI. For example, the safety measures department can input the user's family structure data into a generating AI and have the AI ​​optimize the means of safety measures.

[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 safety monitoring system can estimate the user's emotions and adjust the notification method for anomalies based on those emotions. For example, if the user is relaxed, the notification unit can sound an alarm with a gentle sound. If the user is stressed, the notification unit can notify them with a stronger vibration. Furthermore, if the user is tired, the notification unit can combine sound and vibration to notify them. This allows the system to provide the most appropriate notification method according to the user's emotions.

[0102] The safety monitoring system can detect abnormalities early by referring to the user's past health data. For example, the monitoring unit can refer to the user's past body temperature data to detect abnormal temperature fluctuations early. It can also refer to the user's past heart rate data to detect abnormal heart rate fluctuations early. Furthermore, it can refer to the user's past movement data to detect abnormal movement patterns early. This makes it possible to detect abnormalities early by utilizing past health data.

[0103] The safety monitoring system can improve its monitoring accuracy by considering the user's bathing history. For example, the monitoring unit can refer to the user's past bathing times to detect abnormally long bathing sessions early. It can also refer to the user's past bathing frequency to detect abnormal fluctuations in frequency early. Furthermore, it can refer to the user's past bathing temperatures to detect abnormal temperature fluctuations early. In this way, the accuracy of monitoring is improved by considering the bathing history.

[0104] The safety monitoring system can estimate the user's emotions and adjust the monitoring frequency based on those emotions. For example, the monitoring unit can use facial recognition technology to estimate the user's emotions and set the monitoring frequency lower if the user is relaxed. Conversely, it can set the monitoring frequency higher if the user is tense. Furthermore, if the user is tired, it can set the monitoring frequency to a moderate level. This enables appropriate monitoring tailored to the user's emotions.

[0105] The safety monitoring system can determine monitoring priorities by referring to the user's lifestyle data. For example, the monitoring unit can refer to the user's sleep data and prioritize heart rate monitoring if the user is sleep-deprived. It can also refer to the user's diet data and prioritize monitoring body temperature after meals. Furthermore, it can refer to the user's exercise data and prioritize monitoring heart rate after exercise. This allows for appropriate determination of monitoring priorities by utilizing lifestyle data.

[0106] The safety monitoring system can estimate the user's emotions and adjust the anomaly detection criteria based on those emotions. For example, the detection unit can estimate the user's emotions using facial recognition technology and loosen the anomaly detection criteria if the user is relaxed. Conversely, it can tighten the criteria if the user is tense. Furthermore, if the user is tired, the criteria can be set to a moderate level. This enables appropriate anomaly detection in accordance with the user's emotions.

[0107] The safety monitoring system can optimize its anomaly detection algorithm based on the user's age and gender. For example, the detection unit can set criteria for abnormal body temperature fluctuations based on the user's age. It can also set criteria for abnormal heart rate based on the user's gender. Furthermore, it can set criteria for abnormal movement based on the user's age and gender. This enables highly accurate anomaly detection based on age and gender.

[0108] The safety monitoring system can estimate the user's emotions and adjust the timing of anomaly detection based on those emotions. For example, the detection unit can estimate the user's emotions using facial recognition technology and delay the anomaly detection timing if the user is relaxed. Conversely, if the user is tense, the anomaly detection timing can be accelerated. Furthermore, if the user is tired, the anomaly detection timing can be set to a moderate level. This allows for anomaly detection at an appropriate time according to the user's emotions.

[0109] The safety monitoring system can improve the accuracy of anomaly detection by referencing the user's exercise data. For example, the detection unit can refer to the user's post-exercise body temperature data to detect abnormal body temperature fluctuations early. It can also refer to the user's post-exercise heart rate data to detect abnormal heart rate fluctuations early. Furthermore, it can refer to the user's post-exercise movement data to detect abnormal movement patterns early. This improves the accuracy of anomaly detection by utilizing exercise data.

[0110] The safety monitoring system can estimate the user's emotions and adjust safety measures based on those estimates. For example, the safety measures unit can use facial recognition technology to estimate the user's emotions and implement safety measures in a gentle manner if the user is relaxed. If the user is tense, safety measures can be implemented quickly and forcefully. Furthermore, if the user is tired, safety measures can be implemented in a balanced manner. This enables appropriate safety measures tailored to the user's emotions.

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

[0112] Step 1: The monitoring unit monitors body temperature, heart rate, and movement. For example, it measures body temperature using a skin temperature sensor, measures heart rate using a pulse oximeter, and monitors movement using an accelerometer. Step 2: The detection unit detects anomalies based on data monitored by the monitoring unit. For example, it can set ranges for body temperature or heart rate and detect anomalies if those ranges are exceeded. It can also analyze movement patterns and detect abnormal movements. Step 3: The notification unit sounds an alarm or wakes the user with vibration based on the anomaly detected by the detection unit. For example, the type and volume of sound can be set to sound an alarm when an anomaly is detected. Alternatively, the strength and pattern of the vibration can be set to wake the user with vibration when an anomaly is detected. Step 4: The safety measures unit automatically takes safety measures if the user does not respond to the notification unit. For example, it can automatically drain the bathtub water or send a notification to emergency contacts. It can also contact medical facilities.

[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, notification unit, and safety measures unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors body temperature, heart rate, and movement in real time using the skin temperature sensor, pulse oximeter, and acceleration sensor of the smart device 14. The detection unit analyzes the monitoring data using the specific processing unit 290 of the data processing unit 12 and detects abnormalities. The notification unit notifies the user with an alarm or vibration using the control unit 46A of the smart device 14. The safety measures unit automatically drains the bathtub water or sends a notification to emergency contacts using the specific processing unit 290 of the data processing unit 12. 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, notification unit, and safety measures unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors body temperature, heart rate, and movement in real time using the skin temperature sensor, pulse oximeter, and accelerometer of the smart glasses 214. The detection unit analyzes the monitoring data using the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The notification unit notifies the user with an alarm or vibration using the control unit 46A of the smart glasses 214. The safety measures unit automatically drains the water from the bathtub or sends a notification to an emergency contact using the identification processing unit 290 of the data processing unit 12. 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, notification unit, and safety measures unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors body temperature, heart rate, and movement in real time using the skin temperature sensor, pulse oximeter, and acceleration sensor of the headset terminal 314. The detection unit analyzes the monitoring data using the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The notification unit notifies the user with an alarm or vibration using the control unit 46A of the headset terminal 314. The safety measures unit automatically drains the bathtub water or sends a notification to an emergency contact using the identification processing unit 290 of the data processing unit 12. 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, notification unit, and safety measures unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors body temperature, heart rate, and movement in real time using the robot 414's skin temperature sensor, pulse oximeter, and acceleration sensor. The detection unit analyzes the monitoring data using the specific processing unit 290 of the data processing unit 12 and detects abnormalities. The notification unit notifies the user with an alarm or vibration using the control unit 46A of the robot 414. The safety measures unit automatically drains the bathtub water or sends a notification to an emergency contact using the specific processing unit 290 of the data processing unit 12. 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 body temperature, heart rate, and movement, A detection unit that detects anomalies based on data monitored by the aforementioned monitoring unit, A notification unit sounds an alarm or wakes the user with vibration based on an abnormality detected by the aforementioned detection unit, The system includes a safety measures unit that automatically takes safety measures if the user does not respond to the notification unit. A system characterized by the following features. (Note 2) The aforementioned monitoring unit, Monitors body temperature, heart rate, and movement in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The detection unit is The monitoring unit detects anomalies based on the data it monitors. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, Based on anomalies detected by the detection unit, the system sounds an alarm or wakes the user with vibration. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned safety measures department, The notification unit automatically takes safety measures if the user does not respond. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned safety measures department, The bathtub water is automatically drained. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned safety measures department, Send a notification to emergency contacts The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned 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 9) The aforementioned 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 10) The aforementioned monitoring unit, During monitoring, the accuracy of monitoring is improved by considering the user's bathing history. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, The system estimates the user's emotions and selects items to monitor based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, During monitoring, the system prioritizes monitoring by referencing the user's lifestyle data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned monitoring unit, During monitoring, the accuracy of monitoring is improved by taking into account the user's dietary data. 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 criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit is When detection occurs, the accuracy of the detection is improved by referring to the user's past anomaly data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The detection unit is When an anomaly is detected, the anomaly detection algorithm is optimized based on the user's age and gender. The system described in Appendix 1, characterized by the features described herein. (Note 17) The detection unit is It estimates the user's emotions and adjusts the timing 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 detection occurs, the system improves the accuracy of anomaly detection by referencing the user's movement data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit is When detection occurs, the anomaly detection algorithm is optimized by taking into account the user's sleep data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, When sending a notification, the system selects the most suitable notification method by referring to the user's past response data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, When a notification is sent, the notification method is customized based on the user's hearing and visual state. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, When sending a notification, the system will refer to the user's device information to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, When sending notifications, the notification method is optimized by taking into account the user's environment data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned safety measures department, The system estimates user emotions and adjusts safety measures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned safety measures department, When implementing safety measures, the system selects the most suitable safety measures by referring to the user's past accident data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned safety measures department, During safety measures, customize the safety measures based on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned safety measures department, The system estimates user sentiment and prioritizes safety measures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned safety measures department, When implementing safety measures, the system selects the most appropriate safety measures by referring to the user's residential information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned safety measures department, When implementing security measures, the system optimizes security measures by taking into account the user's family structure data. 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 body temperature, heart rate, and movement, A detection unit that detects anomalies based on data monitored by the aforementioned monitoring unit, A notification unit sounds an alarm or wakes the user with vibration based on an abnormality detected by the aforementioned detection unit, The system includes a safety measures unit that automatically takes safety measures if the user does not respond to the notification unit. A system characterized by the following features.

2. The aforementioned monitoring unit, Monitors body temperature, heart rate, and movement in real time. The system according to feature 1.

3. The detection unit is Anomalies are detected based on the data monitored by the aforementioned monitoring unit. The system according to feature 1.

4. The aforementioned notification unit, Based on the abnormality detected by the aforementioned detection unit, an alarm sounds or the user is woken up by vibration. The system according to feature 1.

5. The aforementioned safety measures department, If the user does not respond to the notification unit, safety measures will be automatically taken. The system according to feature 1.

6. The aforementioned safety measures department, The bathtub water is automatically drained. The system according to feature 1.

7. The aforementioned safety measures department, Send a notification to emergency contacts The system according to feature 1.

8. The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system according to feature 1.

9. The aforementioned monitoring unit, During monitoring, the system references the user's past health data to detect abnormalities early. The system according to feature 1.

10. The aforementioned monitoring unit, During monitoring, the accuracy of monitoring is improved by considering the user's bathing history. The system according to feature 1.

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