system

Abstract

The system according to this embodiment aims to quickly make emergency calls in the event of a sudden change in a patient's condition. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a notification unit. The collection unit collects health data using a biorecognition sensor. The analysis unit analyzes the data collected by the collection unit and detects abnormalities. The notification unit automatically makes an emergency call when an abnormality is detected by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 risk that the notification in case of an emergency change may be delayed, which is a serious problem especially for elderly people living alone.

[0005] The system according to the embodiment aims to perform an emergency notification quickly in case of an emergency change.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a notification unit. The collection unit collects physical condition data using a biometric sensor. The analysis unit analyzes the data collected by the collection unit and detects an abnormality. The notification unit automatically performs an emergency notification when an abnormality is detected by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can quickly make an emergency call in the event of a sudden change in condition. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​emergency notification system according to an embodiment of the present invention is a system aimed at resolving the problem of delayed notification in the event of a sudden change in a person's condition. This system is intended for elderly people living alone who have difficulty moving. This system continuously monitors the physical condition of elderly people using biorecognition sensors, performs AI alert processing, and automatically makes an emergency notification when an abnormality is detected. First, the biorecognition sensors continuously collect data on the elderly person's physical condition. For example, data such as heart rate, blood pressure, and body temperature are monitored in real time. This data is transmitted to the AI ​​alert processing system. Next, the AI ​​alert processing system analyzes the collected data and detects abnormalities. For example, it determines that an abnormality has occurred when data exceeding the normal range is detected, such as when the heart rate suddenly increases or when blood pressure drops abnormally. If an abnormality is detected, the AI ​​alert processing system automatically makes an emergency notification. Specifically, it notifies pre-registered emergency contacts and provides necessary information. For example, it notifies the elderly person's current physical condition data and location information. With this system, even if an elderly person living alone has difficulty moving and their condition suddenly changes, an emergency notification can be made quickly, which is expected to improve the survival rate. It can also provide peace of mind to family members and caregivers. This means the AI ​​emergency call system can quickly make an emergency call even if an elderly person living alone who has difficulty moving suddenly experiences a change in their condition.

[0029] The AI ​​emergency call system according to this embodiment comprises a data collection unit, an analysis unit, and a notification unit. The data collection unit collects health data using biorecognition sensors. The data collection unit collects data such as heart rate, blood pressure, and body temperature in real time. The data collection unit measures heart rate using a heart rate sensor, for example. The data collection unit can also measure blood pressure using a blood pressure sensor. Furthermore, the data collection unit can also measure body temperature using a body temperature sensor. For example, the data collection unit measures heart rate in real time using a heart rate sensor and transmits the data to the analysis unit. The blood pressure sensor measures blood pressure in real time and transmits the data to the analysis unit. The body temperature sensor measures body temperature in real time and transmits the data to the analysis unit. The analysis unit analyzes the data collected by the data collection unit and detects abnormalities. The analysis unit includes, for example, a criteria setting unit that sets criteria for detecting abnormalities based on the collected data. The criteria setting unit sets criteria such as when the heart rate exceeds a certain range or when blood pressure fluctuates rapidly. The analysis unit determines an anomaly when it detects data that exceeds the normal range, such as a sudden increase in heart rate or an abnormal drop in blood pressure. The analysis unit can also use AI to analyze the collected data and detect anomalies. For example, the analysis unit can use machine learning algorithms to analyze the collected data and detect anomalies. It can also use deep learning algorithms to analyze the collected data and detect anomalies. The notification unit automatically makes an emergency call when an anomaly is detected by the analysis unit. For example, when an anomaly is detected, the notification unit notifies pre-registered emergency contacts and provides health data and location information. The notification unit can, for example, make a phone call. It can also send a message. For example, the notification unit can make a phone call and provide the elderly person's current health data and location information. It can also send a message and provide the elderly person's current health data and location information. As a result, the AI ​​emergency notification system according to this embodiment can quickly make an emergency call even if an elderly person living alone who has difficulty moving suddenly experiences a change in their condition.

[0030] The data collection unit can collect heart rate, blood pressure, and body temperature data in real time. For example, the data collection unit can measure heart rate in real time using a heart rate sensor. For example, the data collection unit can measure heart rate every second using a heart rate sensor and transmit the data to the analysis unit. The data collection unit can also measure blood pressure in real time using a blood pressure sensor. For example, the data collection unit can measure blood pressure every minute using a blood pressure sensor and transmit the data to the analysis unit. Furthermore, the data collection unit can also measure body temperature in real time using a body temperature sensor. For example, the data collection unit can measure body temperature every five minutes using a body temperature sensor and transmit the data to the analysis unit. This allows for early detection of abnormalities by collecting health data in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by the heart rate sensor into a generation AI and have the generation AI perform real-time data analysis.

[0031] The analysis unit may include a criteria setting unit that sets criteria for detecting anomalies based on the collected data. The criteria setting unit sets criteria such as when the heart rate exceeds a certain range or when blood pressure fluctuates rapidly. For example, the criteria setting unit may set criteria such as when the heart rate exceeds 100 or when blood pressure falls below 90. The criteria setting unit can also set criteria such as when the body temperature exceeds 38 degrees or falls below 36 degrees. For example, the criteria setting unit can set a criterion to determine an anomaly when the heart rate exceeds 100. It can also set a criterion to determine an anomaly when blood pressure falls below 90. It can also set a criterion to determine an anomaly when body temperature exceeds 38 degrees. By setting criteria for anomaly detection, more accurate anomaly detection becomes possible. Some or all of the above processing in the criteria setting unit may be performed using AI, for example, or without using AI. For example, the criteria setting unit can input the collected data into a generating AI and have the generating AI set the criteria for anomaly detection.

[0032] The notification unit can notify pre-registered emergency contacts and provide health data or location information when an abnormality is detected. For example, the notification unit can make a telephone notification to pre-registered emergency contacts when an abnormality is detected. For example, the notification unit can notify the elderly person of their current health data and location information by telephone when an abnormality is detected. The notification unit can also send a message to pre-registered emergency contacts when an abnormality is detected. For example, the notification unit can send the elderly person's current health data and location information by message when an abnormality is detected. This enables a rapid response by quickly notifying emergency contacts and providing necessary information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, when an abnormality is detected, the notification unit can have a generation AI generate notification content and send it to the emergency contacts.

[0033] The analysis unit can analyze the collected data using AI and detect anomalies. For example, the analysis unit can analyze the collected data using a machine learning algorithm and detect anomalies. For example, the analysis unit can analyze heart rate data using a machine learning algorithm and detect anomalies. The analysis unit can also analyze the collected data using a deep learning algorithm and detect anomalies. For example, the analysis unit can analyze blood pressure data using a deep learning algorithm and detect anomalies. Furthermore, the analysis unit can analyze the collected data using AI and detect anomalies. For example, the analysis unit can analyze body temperature data using AI and detect anomalies. This improves the accuracy of anomaly detection by using AI. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform anomaly detection.

[0034] The data collection unit can refer to the user's past health data and increase the collection frequency if signs of abnormality are detected. For example, if the data collection unit detects an abnormality in heart rate from past data, it will increase the collection frequency. For example, if the data collection unit detects an abnormality in heart rate from past data, it will collect heart rate data every second. The data collection unit can also increase the collection frequency to obtain more detailed data if there are large fluctuations in blood pressure. For example, if there are large fluctuations in blood pressure, it will collect blood pressure data every minute. Furthermore, if the body temperature is abnormally high or low, the data collection unit can increase the collection frequency to detect signs of abnormality early. For example, if the body temperature is abnormally high, it will collect body temperature data every five minutes. This allows for early detection of signs of abnormality and adjustment of the collection frequency by referring to past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI, have the generating AI detect signs of abnormality, and adjust the collection frequency.

[0035] The data collection unit can optimize the timing of data collection based on the user's daily rhythm. For example, the data collection unit can start collecting data in accordance with the time the user wakes up. For example, the data collection unit can collect heart rate data in accordance with the time the user wakes up. The data collection unit can also stop collecting data in accordance with the time the user goes to bed. For example, the data collection unit can stop collecting body temperature data in accordance with the time the user goes to bed. Furthermore, the data collection unit can adjust the timing of data collection to match the user's meal times and monitor changes in physical condition after meals. For example, the data collection unit can collect blood pressure data in accordance with the user's meal times. By optimizing the timing of data collection based on the daily rhythm, more accurate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's daily rhythm data into a generating AI and have the generating AI perform the optimization of the data collection timing.

[0036] The data collection unit can collect health data based on environmental factors, taking into account the user's geographical location. For example, if the user is at high altitude, the data collection unit can collect oxygen concentration and heart rate data. For example, if the user is at high altitude, the data collection unit can collect oxygen concentration data every minute and heart rate data every second. The data collection unit can also collect body temperature and sweat amount data if the user is in a hot place. For example, if the user is in a hot place, the data collection unit can collect body temperature data every 5 minutes and sweat amount data every 10 minutes. Furthermore, if the user is in a cold place, the data collection unit can also collect body temperature and blood pressure data. For example, if the user is in a cold place, the data collection unit can collect body temperature data every 5 minutes and blood pressure data every minute. In this way, by taking geographical location into account, health data based on environmental factors can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input the user's geographical location information into the generating AI, and have the generating AI collect health data based on environmental factors.

[0037] The data collection unit can analyze a user's social media activity and adjust the collected data based on their stress level. For example, if a user is stressed on social media, the data collection unit can collect heart rate and blood pressure data. For example, if a user is stressed on social media, the data collection unit can collect heart rate data every second and blood pressure data every minute. The data collection unit can also collect body temperature and respiratory rate data if a user is relaxed on social media. For example, if a user is relaxed on social media, the data collection unit can collect body temperature data every 5 minutes and respiratory rate data every 10 minutes. Furthermore, if a user is anxious on social media, the data collection unit can also collect heart rate variability and blood oxygen saturation data. For example, if a user is anxious on social media, the data collection unit can collect heart rate variability data every second and blood oxygen saturation data every 5 minutes. This makes it possible to collect data based on stress levels by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user social media activity data into a generating AI, which can then adjust data collection based on stress levels.

[0038] The analysis unit can optimize the anomaly detection algorithm by referring to past anomaly detection data. For example, the analysis unit can optimize the heart rate anomaly detection algorithm based on past anomaly detection data. For example, the analysis unit optimizes the heart rate anomaly detection algorithm using a machine learning algorithm based on past anomaly detection data. The analysis unit can also optimize the blood pressure anomaly detection algorithm based on blood pressure anomaly detection data. For example, the analysis unit optimizes the blood pressure anomaly detection algorithm using a deep learning algorithm based on past anomaly detection data. Furthermore, the analysis unit can optimize the body temperature anomaly detection algorithm based on body temperature anomaly detection data. For example, the analysis unit optimizes the body temperature anomaly detection algorithm using a rule-based algorithm based on past anomaly detection data. In this way, the anomaly detection algorithm can be optimized by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past anomaly detection data into a generating AI and have the generating AI perform the optimization of the anomaly detection algorithm.

[0039] The analysis unit can improve the accuracy of anomaly detection by considering the user's lifestyle data. For example, the analysis unit can improve the accuracy of blood glucose anomaly detection by considering the user's eating patterns. For example, the analysis unit optimizes the blood glucose anomaly detection algorithm based on the user's eating patterns. The analysis unit can also improve the accuracy of heart rate anomaly detection by considering the user's exercise habits. For example, the analysis unit optimizes the heart rate anomaly detection algorithm based on the user's exercise habits. Furthermore, the analysis unit can improve the accuracy of body temperature anomaly detection by considering the user's sleep patterns. For example, the analysis unit optimizes the body temperature anomaly detection algorithm based on the user's sleep patterns. In this way, the accuracy of anomaly detection is improved by considering lifestyle data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI perform the optimization of the anomaly detection algorithm.

[0040] The reporting department can select the most appropriate reporting method by referring to past reporting history. For example, the reporting department can select the reporting method that received the quickest response from past reporting history. For example, based on past reporting history, the reporting department will select telephone reporting if it received the quickest response. The reporting department can also set priority for emergency contacts based on past reporting history. For example, based on past reporting history, the reporting department will set the emergency contact that received the quickest response as the highest priority. Furthermore, the reporting department can analyze past reporting history and select the optimal timing for reporting. For example, based on past reporting history, the reporting department will report during the time period that received the quickest response. In this way, the most appropriate reporting method can be selected by referring to past reporting history. Some or all of the above processes in the reporting department may be performed using AI, for example, or without AI. For example, the reporting department can input past reporting history data into a generating AI and have the generating AI select the most appropriate reporting method.

[0041] The notification unit can select the most suitable emergency contact based on the user's current location. For example, if the user is at home, the notification unit will select a nearby emergency contact. For example, if the user is at home, the notification unit will notify a nearby emergency contact. The notification unit can also select the emergency contact closest to the user's current location if the user is out. For example, if the user is out, the notification unit will notify the emergency contact closest to the user's current location. Furthermore, if the user is traveling, the notification unit can select an emergency contact at the travel destination. For example, if the user is traveling, the notification unit will notify an emergency contact at the travel destination. This enables a rapid response by selecting the most suitable emergency contact based on the current location. 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 current location information into a generating AI and have the generating AI select the most suitable emergency contact.

[0042] The standard setting unit can optimize the criteria by referring to past anomaly detection data. For example, the standard setting unit can optimize the heart rate anomaly detection criteria based on past anomaly detection data. For example, the standard setting unit optimizes the heart rate anomaly detection criteria using a machine learning algorithm based on past anomaly detection data. The standard setting unit can also optimize the blood pressure anomaly detection criteria based on blood pressure anomaly detection data. For example, the standard setting unit optimizes the blood pressure anomaly detection criteria using a deep learning algorithm based on past anomaly detection data. Furthermore, the standard setting unit can also optimize the body temperature anomaly detection criteria based on body temperature anomaly detection data. For example, the standard setting unit optimizes the body temperature anomaly detection criteria using a rule-based algorithm based on past anomaly detection data. In this way, the anomaly detection criteria can be optimized by referring to past data. Some or all of the above processing in the standard setting unit may be performed using AI, for example, or without using AI. For example, the standard setting unit can input past anomaly detection data into a generating AI and have the generating AI perform the optimization of the anomaly detection criteria.

[0043] The standard setting unit can set standards considering the user's lifestyle data. For example, the standard setting unit can set abnormal blood glucose detection standards considering the user's eating patterns. For example, the standard setting unit can set abnormal blood glucose detection standards based on the user's eating patterns. The standard setting unit can also set abnormal heart rate detection standards considering the user's exercise habits. For example, the standard setting unit can set abnormal heart rate detection standards based on the user's exercise habits. Furthermore, the standard setting unit can also set abnormal body temperature detection standards considering the user's sleep patterns. For example, the standard setting unit can set abnormal body temperature detection standards based on the user's sleep patterns. This allows for more appropriate setting of abnormality detection standards by considering lifestyle data. Some or all of the above-described processes in the standard setting unit may be performed using AI, for example, or without AI. For example, the standard setting unit can input the user's lifestyle data into a generating AI and have the generating AI perform the setting of abnormality detection standards.

[0044] The criteria setting unit can set criteria based on environmental factors, taking into account the user's geographical location information. For example, if the user is at high altitude, the criteria setting unit can set a standard for oxygen concentration. For example, if the user is at high altitude, the criteria setting unit can set the standard for oxygen concentration to 90%. The criteria setting unit can also set a standard for body temperature if the user is in a hot place. For example, if the user is in a hot place, the criteria setting unit can set the standard for body temperature to 38 degrees. Furthermore, the criteria setting unit can also set a standard for blood pressure if the user is in a cold place. For example, if the user is in a cold place, the criteria setting unit can set the standard for blood pressure to 100. In this way, criteria based on environmental factors can be set by taking into account geographical location information. Some or all of the above processing in the criteria setting unit may be performed using AI, for example, or without using AI. For example, the criteria setting unit can input the user's geographical location information into a generating AI and have the generating AI perform the setting of criteria based on environmental factors.

[0045] The baseline setting unit can analyze a user's social media activity and set baselines based on their stress level. For example, if a user is experiencing stress on social media, the baseline setting unit can set a baseline for heart rate. For example, if a user is experiencing stress on social media, the baseline setting unit can set the heart rate baseline to 90. The baseline setting unit can also set a baseline for body temperature if a user is relaxed on social media. For example, if a user is relaxed on social media, the baseline setting unit can set the body temperature baseline to 38 degrees. Furthermore, if a user is experiencing anxiety on social media, the baseline setting unit can also set a baseline for blood pressure. For example, if a user is experiencing anxiety on social media, the baseline setting unit can set the blood pressure baseline to 100. This makes it possible to set baselines based on stress levels by analyzing social media activity. Some or all of the above processing in the baseline setting unit may be performed using AI, for example, or without AI. For example, the baseline setting unit can input the user's social media activity data into a generating AI and have the generating AI perform the setting of baselines based on stress levels.

[0046] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0047] The data collection unit can refer to the user's past health data and increase the collection frequency if signs of abnormality are detected. For example, if abnormal heart rate is detected from past data, the collection frequency can be increased. Specifically, heart rate data can be collected every second. Also, if blood pressure fluctuates significantly, the collection frequency can be increased to obtain more detailed data. For example, blood pressure data can be collected every minute. Furthermore, if body temperature is abnormally high or low, the collection frequency can be increased to detect signs of abnormality early. For example, body temperature data can be collected every five minutes. This allows for early detection of signs of abnormality and adjustment of the collection frequency by referring to past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI, have the generating AI detect signs of abnormality, and adjust the collection frequency.

[0048] The data collection unit can optimize the timing of data collection based on the user's daily rhythm. For example, it can start collecting data to coincide with the time the user wakes up. Specifically, it can collect heart rate data. It can also stop collecting data to coincide with the time the user goes to sleep. For example, it can stop collecting body temperature data. Furthermore, it can adjust the timing of data collection to coincide with the user's meal times to monitor changes in physical condition after meals. For example, it can collect blood pressure data. By optimizing the timing of data collection based on the user's daily rhythm, more accurate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's daily rhythm data into a generating AI and have the generating AI perform the optimization of the data collection timing.

[0049] The reporting unit can select the most appropriate reporting method by referring to past reporting history. For example, it can select the reporting method that received the quickest response from past reporting history. Specifically, if a telephone call received the quickest response, it can select a telephone call. It can also set priority levels for emergency contacts based on past reporting history. For example, it can set the emergency contact that received the quickest response to a higher priority. Furthermore, it can analyze past reporting history to select the optimal timing for reporting. For example, it can report during the time period when the quickest response occurred. In this way, the most appropriate reporting method can be selected by referring to past reporting history. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input past reporting history data into a generating AI and have the generating AI select the most appropriate reporting method.

[0050] The analysis unit can optimize the anomaly detection algorithm by referring to past anomaly detection data. For example, it can optimize the heart rate anomaly detection algorithm based on past anomaly detection data. Specifically, the heart rate anomaly detection algorithm can be optimized using a machine learning algorithm. It can also optimize the blood pressure anomaly detection algorithm based on blood pressure anomaly detection data. For example, the blood pressure anomaly detection algorithm can be optimized using a deep learning algorithm. Furthermore, it can optimize the body temperature anomaly detection algorithm based on body temperature anomaly detection data. For example, the body temperature anomaly detection algorithm can be optimized using a rule-based algorithm. In this way, the anomaly detection algorithm can be optimized by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past anomaly detection data into a generating AI and have the generating AI perform the optimization of the anomaly detection algorithm.

[0051] The criteria setting unit can set criteria based on environmental factors, taking into account the user's geographical location information. For example, if the user is at high altitude, it can set an oxygen concentration criterion. Specifically, the oxygen concentration criterion can be set to 90%. Also, if the user is in a hot place, it can set a body temperature criterion. For example, the body temperature criterion can be set to 38 degrees. Furthermore, if the user is in a cold place, it can set a blood pressure criterion. For example, the blood pressure criterion can be set to 100. In this way, criteria based on environmental factors can be set by taking geographical location information into consideration. Some or all of the above processing in the criteria setting unit may be performed using AI, for example, or without using AI. For example, the criteria setting unit can input the user's geographical location information into a generating AI and have the generating AI perform the setting of criteria based on environmental factors.

[0052] The following briefly describes the processing flow for example form 1.

[0053] Step 1: The data collection unit collects health data using biometric sensors. The data collection unit collects data such as heart rate, blood pressure, and body temperature in real time and transmits this data to the analysis unit. For example, it measures data using a heart rate sensor, blood pressure sensor, and body temperature sensor. Step 2: The analysis unit analyzes the data collected by the data collection unit and detects anomalies. Based on the collected data, the analysis unit sets criteria for detecting anomalies, and determines an anomaly to occur, for example, when the heart rate exceeds a certain range or when blood pressure fluctuates rapidly. The analysis unit can also analyze the data using AI, machine learning algorithms, and deep learning algorithms. Step 3: The notification unit automatically makes an emergency call if an abnormality is detected by the analysis unit. The notification unit notifies pre-registered emergency contacts and provides health data and location information. The notification unit can make phone calls or send messages.

[0054] (Example of form 2) The AI ​​emergency notification system according to an embodiment of the present invention is a system aimed at resolving the problem of delayed notification in the event of a sudden change in a person's condition. This system is intended for elderly people living alone who have difficulty moving. This system continuously monitors the physical condition of elderly people using biorecognition sensors, performs AI alert processing, and automatically makes an emergency notification when an abnormality is detected. First, the biorecognition sensors continuously collect data on the elderly person's physical condition. For example, data such as heart rate, blood pressure, and body temperature are monitored in real time. This data is transmitted to the AI ​​alert processing system. Next, the AI ​​alert processing system analyzes the collected data and detects abnormalities. For example, it determines that an abnormality has occurred when data exceeding the normal range is detected, such as when the heart rate suddenly increases or when blood pressure drops abnormally. If an abnormality is detected, the AI ​​alert processing system automatically makes an emergency notification. Specifically, it notifies pre-registered emergency contacts and provides necessary information. For example, it notifies the elderly person's current physical condition data and location information. With this system, even if an elderly person living alone has difficulty moving and their condition suddenly changes, an emergency notification can be made quickly, which is expected to improve the survival rate. It can also provide peace of mind to family members and caregivers. This means the AI ​​emergency call system can quickly make an emergency call even if an elderly person living alone who has difficulty moving suddenly experiences a change in their condition.

[0055] The AI ​​emergency call system according to this embodiment comprises a data collection unit, an analysis unit, and a notification unit. The data collection unit collects health data using biorecognition sensors. The data collection unit collects data such as heart rate, blood pressure, and body temperature in real time. The data collection unit measures heart rate using a heart rate sensor, for example. The data collection unit can also measure blood pressure using a blood pressure sensor. Furthermore, the data collection unit can also measure body temperature using a body temperature sensor. For example, the data collection unit measures heart rate in real time using a heart rate sensor and transmits the data to the analysis unit. The blood pressure sensor measures blood pressure in real time and transmits the data to the analysis unit. The body temperature sensor measures body temperature in real time and transmits the data to the analysis unit. The analysis unit analyzes the data collected by the data collection unit and detects abnormalities. The analysis unit includes, for example, a criteria setting unit that sets criteria for detecting abnormalities based on the collected data. The criteria setting unit sets criteria such as when the heart rate exceeds a certain range or when blood pressure fluctuates rapidly. The analysis unit determines an anomaly when it detects data that exceeds the normal range, such as a sudden increase in heart rate or an abnormal drop in blood pressure. The analysis unit can also use AI to analyze the collected data and detect anomalies. For example, the analysis unit can use machine learning algorithms to analyze the collected data and detect anomalies. It can also use deep learning algorithms to analyze the collected data and detect anomalies. The notification unit automatically makes an emergency call when an anomaly is detected by the analysis unit. For example, when an anomaly is detected, the notification unit notifies pre-registered emergency contacts and provides health data and location information. The notification unit can, for example, make a phone call. It can also send a message. For example, the notification unit can make a phone call and provide the elderly person's current health data and location information. It can also send a message and provide the elderly person's current health data and location information. As a result, the AI ​​emergency notification system according to this embodiment can quickly make an emergency call even if an elderly person living alone who has difficulty moving suddenly experiences a change in their condition.

[0056] The data collection unit can collect heart rate, blood pressure, and body temperature data in real time. For example, the data collection unit can measure heart rate in real time using a heart rate sensor. For example, the data collection unit can measure heart rate every second using a heart rate sensor and transmit the data to the analysis unit. The data collection unit can also measure blood pressure in real time using a blood pressure sensor. For example, the data collection unit can measure blood pressure every minute using a blood pressure sensor and transmit the data to the analysis unit. Furthermore, the data collection unit can also measure body temperature in real time using a body temperature sensor. For example, the data collection unit can measure body temperature every five minutes using a body temperature sensor and transmit the data to the analysis unit. This allows for early detection of abnormalities by collecting health data in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by the heart rate sensor into a generation AI and have the generation AI perform real-time data analysis.

[0057] The analysis unit may include a criteria setting unit that sets criteria for detecting anomalies based on the collected data. The criteria setting unit sets criteria such as when the heart rate exceeds a certain range or when blood pressure fluctuates rapidly. For example, the criteria setting unit may set criteria such as when the heart rate exceeds 100 or when blood pressure falls below 90. The criteria setting unit can also set criteria such as when the body temperature exceeds 38 degrees or falls below 36 degrees. For example, the criteria setting unit can set a criterion to determine an anomaly when the heart rate exceeds 100. It can also set a criterion to determine an anomaly when blood pressure falls below 90. It can also set a criterion to determine an anomaly when body temperature exceeds 38 degrees. By setting criteria for anomaly detection, more accurate anomaly detection becomes possible. Some or all of the above processing in the criteria setting unit may be performed using AI, for example, or without using AI. For example, the criteria setting unit can input the collected data into a generating AI and have the generating AI set the criteria for anomaly detection.

[0058] The notification unit can notify pre-registered emergency contacts and provide health data or location information when an abnormality is detected. For example, the notification unit can make a telephone notification to pre-registered emergency contacts when an abnormality is detected. For example, the notification unit can notify the elderly person of their current health data and location information by telephone when an abnormality is detected. The notification unit can also send a message to pre-registered emergency contacts when an abnormality is detected. For example, the notification unit can send the elderly person's current health data and location information by message when an abnormality is detected. This enables a rapid response by quickly notifying emergency contacts and providing necessary information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, when an abnormality is detected, the notification unit can have a generation AI generate notification content and send it to the emergency contacts.

[0059] The analysis unit can analyze the collected data using AI and detect anomalies. For example, the analysis unit can analyze the collected data using a machine learning algorithm and detect anomalies. For example, the analysis unit can analyze heart rate data using a machine learning algorithm and detect anomalies. The analysis unit can also analyze the collected data using a deep learning algorithm and detect anomalies. For example, the analysis unit can analyze blood pressure data using a deep learning algorithm and detect anomalies. Furthermore, the analysis unit can analyze the collected data using AI and detect anomalies. For example, the analysis unit can analyze body temperature data using AI and detect anomalies. This improves the accuracy of anomaly detection by using AI. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform anomaly detection.

[0060] The data collection unit can estimate the user's emotions and adjust the frequency of collecting health data based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the collection frequency to obtain more detailed data. For instance, if the user is stressed, the data collection unit collects heart rate data every second. The data collection unit can also reduce the burden of data acquisition by lowering the collection frequency when the user is relaxed. For example, if the user is relaxed, the data collection unit collects body temperature data every 10 minutes. Furthermore, if the user is anxious, the data collection unit can set the collection frequency to a moderate level to obtain balanced data. For example, if the user is anxious, the data collection unit collects blood pressure data every 5 minutes. This allows for more appropriate data collection by adjusting the collection frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the collection frequency.

[0061] The data collection unit can refer to the user's past health data and increase the collection frequency if signs of abnormality are detected. For example, if the data collection unit detects an abnormality in heart rate from past data, it will increase the collection frequency. For example, if the data collection unit detects an abnormality in heart rate from past data, it will collect heart rate data every second. The data collection unit can also increase the collection frequency to obtain more detailed data if there are large fluctuations in blood pressure. For example, if there are large fluctuations in blood pressure, it will collect blood pressure data every minute. Furthermore, if the body temperature is abnormally high or low, the data collection unit can increase the collection frequency to detect signs of abnormality early. For example, if the body temperature is abnormally high, it will collect body temperature data every five minutes. This allows for early detection of signs of abnormality and adjustment of the collection frequency by referring to past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI, have the generating AI detect signs of abnormality, and adjust the collection frequency.

[0062] The data collection unit can optimize the timing of data collection based on the user's daily rhythm. For example, the data collection unit can start collecting data in accordance with the time the user wakes up. For example, the data collection unit can collect heart rate data in accordance with the time the user wakes up. The data collection unit can also stop collecting data in accordance with the time the user goes to bed. For example, the data collection unit can stop collecting body temperature data in accordance with the time the user goes to bed. Furthermore, the data collection unit can adjust the timing of data collection to match the user's meal times and monitor changes in physical condition after meals. For example, the data collection unit can collect blood pressure data in accordance with the user's meal times. By optimizing the timing of data collection based on the daily rhythm, more accurate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's daily rhythm data into a generating AI and have the generating AI perform the optimization of the data collection timing.

[0063] The data collection unit can estimate the user's emotions and select the types of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting heart rate and blood pressure data. For instance, if the user is stressed, the data collection unit will collect heart rate data every second and blood pressure data every minute. The data collection unit can also prioritize collecting body temperature and respiratory rate data if the user is relaxed. For example, if the user is relaxed, the data collection unit will collect body temperature data every five minutes and respiratory rate data every ten minutes. Furthermore, if the user is anxious, the data collection unit can also prioritize collecting heart rate variability and blood oxygen saturation data. For example, if the user is anxious, the data collection unit will collect heart rate variability data every second and blood oxygen saturation data every five minutes. This allows for more appropriate data collection by selecting the types of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit may input user sentiment data into the generation AI and have the generation AI select the types of data to collect.

[0064] The data collection unit can collect health data based on environmental factors, taking into account the user's geographical location. For example, if the user is at high altitude, the data collection unit can collect oxygen concentration and heart rate data. For example, if the user is at high altitude, the data collection unit can collect oxygen concentration data every minute and heart rate data every second. The data collection unit can also collect body temperature and sweat amount data if the user is in a hot place. For example, if the user is in a hot place, the data collection unit can collect body temperature data every 5 minutes and sweat amount data every 10 minutes. Furthermore, if the user is in a cold place, the data collection unit can also collect body temperature and blood pressure data. For example, if the user is in a cold place, the data collection unit can collect body temperature data every 5 minutes and blood pressure data every minute. In this way, by taking geographical location into account, health data based on environmental factors can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input the user's geographical location information into the generating AI, and have the generating AI collect health data based on environmental factors.

[0065] The data collection unit can analyze a user's social media activity and adjust the collected data based on their stress level. For example, if a user is stressed on social media, the data collection unit can collect heart rate and blood pressure data. For example, if a user is stressed on social media, the data collection unit can collect heart rate data every second and blood pressure data every minute. The data collection unit can also collect body temperature and respiratory rate data if a user is relaxed on social media. For example, if a user is relaxed on social media, the data collection unit can collect body temperature data every 5 minutes and respiratory rate data every 10 minutes. Furthermore, if a user is anxious on social media, the data collection unit can also collect heart rate variability and blood oxygen saturation data. For example, if a user is anxious on social media, the data collection unit can collect heart rate variability data every second and blood oxygen saturation data every 5 minutes. This makes it possible to collect data based on stress levels by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user social media activity data into a generating AI, which can then adjust data collection based on stress levels.

[0066] The analysis unit can estimate the user's emotions and adjust the anomaly detection threshold based on the estimated emotions. For example, if the user is stressed, the analysis unit can set the anomaly detection threshold low. For example, if the user is stressed, the analysis unit can set the heart rate anomaly detection threshold to 90. The analysis unit can also set the anomaly detection threshold high if the user is relaxed. For example, if the user is relaxed, the analysis unit can set the body temperature anomaly detection threshold to 39 degrees. Furthermore, if the user is anxious, the analysis unit can set the anomaly detection threshold to a moderate level. For example, if the user is anxious, the analysis unit can set the blood pressure anomaly detection threshold to 100. By adjusting the anomaly detection threshold according to the user's emotions, more accurate anomaly detection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the anomaly detection threshold.

[0067] The analysis unit can optimize the anomaly detection algorithm by referring to past anomaly detection data. For example, the analysis unit can optimize the heart rate anomaly detection algorithm based on past anomaly detection data. For example, the analysis unit optimizes the heart rate anomaly detection algorithm using a machine learning algorithm based on past anomaly detection data. The analysis unit can also optimize the blood pressure anomaly detection algorithm based on blood pressure anomaly detection data. For example, the analysis unit optimizes the blood pressure anomaly detection algorithm using a deep learning algorithm based on past anomaly detection data. Furthermore, the analysis unit can optimize the body temperature anomaly detection algorithm based on body temperature anomaly detection data. For example, the analysis unit optimizes the body temperature anomaly detection algorithm using a rule-based algorithm based on past anomaly detection data. In this way, the anomaly detection algorithm can be optimized by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past anomaly detection data into a generating AI and have the generating AI perform the optimization of the anomaly detection algorithm.

[0068] The analysis unit can improve the accuracy of anomaly detection by considering the user's lifestyle data. For example, the analysis unit can improve the accuracy of blood glucose anomaly detection by considering the user's eating patterns. For example, the analysis unit optimizes the blood glucose anomaly detection algorithm based on the user's eating patterns. The analysis unit can also improve the accuracy of heart rate anomaly detection by considering the user's exercise habits. For example, the analysis unit optimizes the heart rate anomaly detection algorithm based on the user's exercise habits. Furthermore, the analysis unit can improve the accuracy of body temperature anomaly detection by considering the user's sleep patterns. For example, the analysis unit optimizes the body temperature anomaly detection algorithm based on the user's sleep patterns. In this way, the accuracy of anomaly detection is improved by considering lifestyle data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI perform the optimization of the anomaly detection algorithm.

[0069] The notification unit can estimate the user's emotions and adjust the notification content based on the estimated emotions. For example, if the user is stressed, the notification unit may include detailed physical data in the notification. For example, if the user is stressed, the notification unit may include detailed data on heart rate, blood pressure, and body temperature in the notification. The notification unit may also include concise physical data in the notification if the user is relaxed. For example, if the user is relaxed, the notification unit may include concise data on body temperature in the notification. Furthermore, if the user is anxious, the notification unit may provide detailed notification content including location information. For example, if the user is anxious, the notification unit may include detailed data on the current location and heart rate in the notification. This allows for more appropriate notifications by adjusting the notification content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the reporting unit can input user emotion data into a generating AI and have the AI ​​adjust the content of the report.

[0070] The reporting department can select the most appropriate reporting method by referring to past reporting history. For example, the reporting department can select the reporting method that received the quickest response from past reporting history. For example, based on past reporting history, the reporting department will select telephone reporting if it received the quickest response. The reporting department can also set priority for emergency contacts based on past reporting history. For example, based on past reporting history, the reporting department will set the emergency contact that received the quickest response as the highest priority. Furthermore, the reporting department can analyze past reporting history and select the optimal timing for reporting. For example, based on past reporting history, the reporting department will report during the time period that received the quickest response. In this way, the most appropriate reporting method can be selected by referring to past reporting history. Some or all of the above processes in the reporting department may be performed using AI, for example, or without AI. For example, the reporting department can input past reporting history data into a generating AI and have the generating AI select the most appropriate reporting method.

[0071] The notification unit can select the most suitable emergency contact based on the user's current location. For example, if the user is at home, the notification unit will select a nearby emergency contact. For example, if the user is at home, the notification unit will notify a nearby emergency contact. The notification unit can also select the emergency contact closest to the user's current location if the user is out. For example, if the user is out, the notification unit will notify the emergency contact closest to the user's current location. Furthermore, if the user is traveling, the notification unit can select an emergency contact at the travel destination. For example, if the user is traveling, the notification unit will notify an emergency contact at the travel destination. This enables a rapid response by selecting the most suitable emergency contact based on the current location. 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 current location information into a generating AI and have the generating AI select the most suitable emergency contact.

[0072] The criteria setting unit can estimate the user's emotions and adjust the anomaly detection criteria based on the estimated emotions. For example, if the user is stressed, the criteria setting unit will set the anomaly detection criteria stricter. For example, if the user is stressed, the criteria setting unit will set the heart rate anomaly detection criterion to 90. The criteria setting unit can also set the anomaly detection criteria leniently if the user is relaxed. For example, if the user is relaxed, the criteria setting unit will set the body temperature anomaly detection criterion to 39 degrees. Furthermore, if the user is anxious, the criteria setting unit can also set the anomaly detection criteria moderately. For example, if the user is anxious, the criteria setting unit will set the blood pressure anomaly detection criterion to 100. By adjusting the anomaly detection criteria according to the user's emotions, more accurate anomaly detection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the criteria setting unit may be performed using AI, for example, or without AI. For example, the criteria setting unit can input user emotion data into a generating AI and have the generating AI perform adjustments to the anomaly detection criteria.

[0073] The standard setting unit can optimize the criteria by referring to past anomaly detection data. For example, the standard setting unit can optimize the heart rate anomaly detection criteria based on past anomaly detection data. For example, the standard setting unit optimizes the heart rate anomaly detection criteria using a machine learning algorithm based on past anomaly detection data. The standard setting unit can also optimize the blood pressure anomaly detection criteria based on blood pressure anomaly detection data. For example, the standard setting unit optimizes the blood pressure anomaly detection criteria using a deep learning algorithm based on past anomaly detection data. Furthermore, the standard setting unit can also optimize the body temperature anomaly detection criteria based on body temperature anomaly detection data. For example, the standard setting unit optimizes the body temperature anomaly detection criteria using a rule-based algorithm based on past anomaly detection data. In this way, the anomaly detection criteria can be optimized by referring to past data. Some or all of the above processing in the standard setting unit may be performed using AI, for example, or without using AI. For example, the standard setting unit can input past anomaly detection data into a generating AI and have the generating AI perform the optimization of the anomaly detection criteria.

[0074] The standard setting unit can set standards considering the user's lifestyle data. For example, the standard setting unit can set abnormal blood glucose detection standards considering the user's eating patterns. For example, the standard setting unit can set abnormal blood glucose detection standards based on the user's eating patterns. The standard setting unit can also set abnormal heart rate detection standards considering the user's exercise habits. For example, the standard setting unit can set abnormal heart rate detection standards based on the user's exercise habits. Furthermore, the standard setting unit can also set abnormal body temperature detection standards considering the user's sleep patterns. For example, the standard setting unit can set abnormal body temperature detection standards based on the user's sleep patterns. This allows for more appropriate setting of abnormality detection standards by considering lifestyle data. Some or all of the above-described processes in the standard setting unit may be performed using AI, for example, or without AI. For example, the standard setting unit can input the user's lifestyle data into a generating AI and have the generating AI perform the setting of abnormality detection standards.

[0075] The criteria setting unit can estimate the user's emotions and determine the priority of criteria based on the estimated user emotions. For example, if the user is stressed, the criteria setting unit may prioritize the heart rate criterion. For example, if the user is stressed, the criteria setting unit may set the heart rate criterion as the highest priority. The criteria setting unit may also prioritize the body temperature criterion if the user is relaxed. For example, if the user is relaxed, the criteria setting unit may set the body temperature criterion as the highest priority. Furthermore, if the user is anxious, the criteria setting unit may also prioritize the blood pressure criterion. For example, if the user is anxious, the criteria setting unit may set the blood pressure criterion as the highest priority. By determining the priority of criteria according to the user's emotions, more appropriate anomaly detection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the criteria setting unit may be performed using AI, for example, or without using AI. For example, the criteria setting unit can input user emotion data into the generating AI and have the generating AI determine the priority of the criteria.

[0076] The criteria setting unit can set criteria based on environmental factors, taking into account the user's geographical location information. For example, if the user is at high altitude, the criteria setting unit can set a standard for oxygen concentration. For example, if the user is at high altitude, the criteria setting unit can set the standard for oxygen concentration to 90%. The criteria setting unit can also set a standard for body temperature if the user is in a hot place. For example, if the user is in a hot place, the criteria setting unit can set the standard for body temperature to 38 degrees. Furthermore, the criteria setting unit can also set a standard for blood pressure if the user is in a cold place. For example, if the user is in a cold place, the criteria setting unit can set the standard for blood pressure to 100. In this way, criteria based on environmental factors can be set by taking into account geographical location information. Some or all of the above processing in the criteria setting unit may be performed using AI, for example, or without using AI. For example, the criteria setting unit can input the user's geographical location information into a generating AI and have the generating AI perform the setting of criteria based on environmental factors.

[0077] The baseline setting unit can analyze a user's social media activity and set baselines based on their stress level. For example, if a user is experiencing stress on social media, the baseline setting unit can set a baseline for heart rate. For example, if a user is experiencing stress on social media, the baseline setting unit can set the heart rate baseline to 90. The baseline setting unit can also set a baseline for body temperature if a user is relaxed on social media. For example, if a user is relaxed on social media, the baseline setting unit can set the body temperature baseline to 38 degrees. Furthermore, if a user is experiencing anxiety on social media, the baseline setting unit can also set a baseline for blood pressure. For example, if a user is experiencing anxiety on social media, the baseline setting unit can set the blood pressure baseline to 100. This makes it possible to set baselines based on stress levels by analyzing social media activity. Some or all of the above processing in the baseline setting unit may be performed using AI, for example, or without AI. For example, the baseline setting unit can input the user's social media activity data into a generating AI and have the generating AI perform the setting of baselines based on stress levels. === Hard Collateral 1-1 === Each of the multiple elements described above, including the data collection unit, analysis unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the biometric sensors of the smart device 14 to collect data such as heart rate, blood pressure, and body temperature in real time and transmits this data to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 to analyze the collected data and detect abnormalities. The notification unit is implemented in the specific processing unit 290 of the data processing unit 12 to automatically make an emergency call when an abnormality is detected. The notification unit uses the communication I / F 44 of the smart device 14 to notify pre-registered emergency contacts and provide health data and location information. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, analysis unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the biorecognition sensors of the smart glasses 214 to collect data such as heart rate, blood pressure, and body temperature in real time and transmits this data to the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 to analyze the collected data and detect abnormalities. The notification unit is implemented in the identification processing unit 290 of the data processing unit 12 to automatically make an emergency call when an abnormality is detected. The notification unit uses the communication I / F 44 of the smart glasses 214 to notify pre-registered emergency contacts and provide health data and location information. === Hard Collateral 1-3 === Each of the multiple elements described above, including the data collection unit, analysis unit, and notification unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the biorecognition sensor of the headset terminal 314 to collect data such as heart rate, blood pressure, and body temperature in real time and transmits this data to the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and detects abnormalities. The notification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which automatically makes an emergency call when an abnormality is detected. The notification unit uses the communication I / F 44 of the headset terminal 314 to notify pre-registered emergency contacts and provides health data and location information. === Hard Collateral 1-4 === Each of the multiple elements described above, including the data collection unit, analysis unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the robot 414's biorecognition sensors to collect data such as heart rate, blood pressure, and body temperature in real time and transmits this data to the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and detects abnormalities. The notification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which automatically makes an emergency call when an abnormality is detected. The notification unit uses the robot 414's communication I / F 44 to notify pre-registered emergency contacts and provides health data and location information.

[0078] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0079] The analysis unit can estimate the user's emotions and adjust the anomaly detection threshold based on the estimated emotions. For example, if the user is stressed, the anomaly detection threshold can be set low. Specifically, the anomaly detection threshold for heart rate can be set to 90. Conversely, if the user is relaxed, the anomaly detection threshold can be set high. For example, the anomaly detection threshold for body temperature can be set to 39 degrees. Furthermore, if the user is anxious, the anomaly detection threshold can be set to a moderate level. For example, the anomaly detection threshold for blood pressure can be set to 100. By adjusting the anomaly detection threshold according to the user's emotions, more accurate anomaly detection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the anomaly detection threshold.

[0080] The data collection unit can refer to the user's past health data and increase the collection frequency if signs of abnormality are detected. For example, if abnormal heart rate is detected from past data, the collection frequency can be increased. Specifically, heart rate data can be collected every second. Also, if blood pressure fluctuates significantly, the collection frequency can be increased to obtain more detailed data. For example, blood pressure data can be collected every minute. Furthermore, if body temperature is abnormally high or low, the collection frequency can be increased to detect signs of abnormality early. For example, body temperature data can be collected every five minutes. This allows for early detection of signs of abnormality and adjustment of the collection frequency by referring to past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI, have the generating AI detect signs of abnormality, and adjust the collection frequency.

[0081] The notification unit can estimate the user's emotions and adjust the notification content based on the estimated emotions. For example, if the user is stressed, detailed physical data can be included in the notification. Specifically, detailed data such as heart rate, blood pressure, and body temperature can be included in the notification. If the user is relaxed, concise physical data can be included in the notification. For example, concise body temperature data can be included in the notification. Furthermore, if the user is anxious, detailed notification content including location information can be provided. For example, current location information and detailed heart rate data can be included in the notification. This allows for more appropriate notifications by adjusting the notification content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the notification content.

[0082] The data collection unit can optimize the timing of data collection based on the user's daily rhythm. For example, it can start collecting data to coincide with the time the user wakes up. Specifically, it can collect heart rate data. It can also stop collecting data to coincide with the time the user goes to sleep. For example, it can stop collecting body temperature data. Furthermore, it can adjust the timing of data collection to coincide with the user's meal times to monitor changes in physical condition after meals. For example, it can collect blood pressure data. By optimizing the timing of data collection based on the user's daily rhythm, more accurate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's daily rhythm data into a generating AI and have the generating AI perform the optimization of the data collection timing.

[0083] The criteria setting unit can estimate the user's emotions and adjust the anomaly detection criteria based on the estimated user emotions. For example, if the user is stressed, the anomaly detection criteria can be set strictly. Specifically, the anomaly detection criterion for heart rate can be set to 90. Conversely, if the user is relaxed, the anomaly detection criteria can be set loosely. For example, the anomaly detection criterion for body temperature can be set to 39 degrees. Furthermore, if the user is anxious, the anomaly detection criterion can be set moderately. For example, the anomaly detection criterion for blood pressure can be set to 100. By adjusting the anomaly detection criteria according to the user's emotions, more accurate anomaly detection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the criteria setting unit may be performed using AI, for example, or without AI. For example, the criteria setting unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the anomaly detection criteria.

[0084] The reporting unit can select the most appropriate reporting method by referring to past reporting history. For example, it can select the reporting method that received the quickest response from past reporting history. Specifically, if a telephone call received the quickest response, it can select a telephone call. It can also set priority levels for emergency contacts based on past reporting history. For example, it can set the emergency contact that received the quickest response to a higher priority. Furthermore, it can analyze past reporting history to select the optimal timing for reporting. For example, it can report during the time period when the quickest response occurred. In this way, the most appropriate reporting method can be selected by referring to past reporting history. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input past reporting history data into a generating AI and have the generating AI select the most appropriate reporting method.

[0085] The data collection unit can estimate the user's emotions and select the types of data to collect based on the estimated emotions. For example, if the user is stressed, it can prioritize collecting heart rate and blood pressure data. Specifically, it can collect heart rate data every second and blood pressure data every minute. If the user is relaxed, it can prioritize collecting body temperature and respiratory rate data. For example, it can collect body temperature data every five minutes and respiratory rate data every ten minutes. Furthermore, if the user is anxious, it can prioritize collecting heart rate variability and blood oxygen saturation data. For example, it can collect heart rate variability data every second and blood oxygen saturation data every five minutes. This allows for more appropriate data collection by selecting the types of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI select the types of data to collect.

[0086] The analysis unit can optimize the anomaly detection algorithm by referring to past anomaly detection data. For example, it can optimize the heart rate anomaly detection algorithm based on past anomaly detection data. Specifically, the heart rate anomaly detection algorithm can be optimized using a machine learning algorithm. It can also optimize the blood pressure anomaly detection algorithm based on blood pressure anomaly detection data. For example, the blood pressure anomaly detection algorithm can be optimized using a deep learning algorithm. Furthermore, it can optimize the body temperature anomaly detection algorithm based on body temperature anomaly detection data. For example, the body temperature anomaly detection algorithm can be optimized using a rule-based algorithm. In this way, the anomaly detection algorithm can be optimized by referring to past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past anomaly detection data into a generating AI and have the generating AI perform the optimization of the anomaly detection algorithm.

[0087] The criteria setting unit can estimate the user's emotions and determine the priority of criteria based on the estimated user emotions. For example, if the user is stressed, the heart rate criterion can be prioritized. Specifically, the heart rate criterion can be set as the highest priority. Also, if the user is relaxed, the body temperature criterion can be prioritized. For example, the body temperature criterion can be set as the highest priority. Furthermore, if the user is anxious, the blood pressure criterion can be prioritized. For example, the blood pressure criterion can be set as the highest priority. By determining the priority of criteria according to the user's emotions, more appropriate anomaly detection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the criteria setting unit may be performed using AI, for example, or without using AI. For example, the criteria setting unit can input user emotion data into a generative AI and have the generative AI determine the priority of criteria.

[0088] The criteria setting unit can set criteria based on environmental factors, taking into account the user's geographical location information. For example, if the user is at high altitude, it can set an oxygen concentration criterion. Specifically, the oxygen concentration criterion can be set to 90%. Also, if the user is in a hot place, it can set a body temperature criterion. For example, the body temperature criterion can be set to 38 degrees. Furthermore, if the user is in a cold place, it can set a blood pressure criterion. For example, the blood pressure criterion can be set to 100. In this way, criteria based on environmental factors can be set by taking geographical location information into consideration. Some or all of the above processing in the criteria setting unit may be performed using AI, for example, or without using AI. For example, the criteria setting unit can input the user's geographical location information into a generating AI and have the generating AI perform the setting of criteria based on environmental factors.

[0089] The following briefly describes the processing flow for example form 2.

[0090] Step 1: The data collection unit collects health data using biometric sensors. The data collection unit collects data such as heart rate, blood pressure, and body temperature in real time and transmits this data to the analysis unit. For example, it measures data using a heart rate sensor, blood pressure sensor, and body temperature sensor. Step 2: The analysis unit analyzes the data collected by the data collection unit and detects anomalies. Based on the collected data, the analysis unit sets criteria for detecting anomalies, and determines an anomaly to occur, for example, when the heart rate exceeds a certain range or when blood pressure fluctuates rapidly. The analysis unit can also analyze the data using AI, machine learning algorithms, and deep learning algorithms. Step 3: The notification unit automatically makes an emergency call if an abnormality is detected by the analysis unit. The notification unit notifies pre-registered emergency contacts and provides health data and location information. The notification unit can make phone calls or send messages.

[0091] 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.

[0092] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0093] 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.

[0094] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0095] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.).

[0107] 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.

[0108] 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. 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.

[0109] 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.

[0110] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0111] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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. 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.

[0125] 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.

[0126] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0128] 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.

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

[0130] The 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.

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

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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. 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.

[0142] 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.

[0143] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects health data using biorecognition sensors, An analysis unit analyzes the data collected by the aforementioned collection unit and detects anomalies, The system includes a notification unit that automatically makes an emergency call when an abnormality is detected by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collects heart rate, blood pressure, and body temperature data in real time. The system according to feature 1.

3. The aforementioned analysis unit, It includes a criteria setting unit that sets criteria for detecting anomalies based on the collected data. The system according to feature 1.

4. The aforementioned reporting unit, If an abnormality is detected, a notification will be sent to a pre-registered emergency contact, and health data or location information will be provided. The system according to feature 1.

5. The aforementioned analysis unit, The collected data is analyzed using AI to detect anomalies. The system according to feature 1.

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

7. The aforementioned collection unit is The system references the user's past health data and increases the collection frequency if signs of abnormality are detected. The system according to feature 1.

8. The aforementioned collection unit is Optimize data collection timing based on the user's daily routine. The system according to feature 1.

Images