System

Abstract

An object of a system according to an embodiment is to analyze biological information of a user in an emergency and promptly take an appropriate action.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, an instruction unit, and a notification unit. The collection unit collects biological information of a user. The analysis unit analyzes the information collected by the collection unit. The generation unit generates emergency response information based on the information analyzed by the analysis unit. The instruction unit issues an instruction to the user based on the information generated by the generation unit. The notification unit tracks the position information of the user and notifies the emergency service.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult to collect a user's biometric information in real time in an emergency and to take appropriate action quickly.

[0005] The system according to the embodiment aims to analyze the biometric information of a user in an emergency and to take appropriate measures promptly. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, an instruction unit, and a notification unit. The collection unit collects biometric information of a user. The analysis unit analyzes the information collected by the collection unit. The generation unit generates emergency response information based on the information analyzed by the analysis unit. The instruction unit issues instructions to the user based on the information generated by the generation unit. The notification unit tracks the user's location information and notifies emergency services. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the biometric information of a user in an emergency and quickly take appropriate measures. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An emergency support system according to an embodiment of the present invention collects a user's biometric information in real time, generates emergency response information using a generation AI, and guides the user to a safe evacuation shelter using a combination of navigation and audio guidance. The emergency support system collects the user's biometric information, analyzes it using a generation AI, and generates emergency response information to instruct the user on appropriate actions. The emergency support system can also track the user's location information in real time and notify emergency services. For example, the emergency support system collects biometric information such as the user's heart rate, body temperature, and blood pressure. The emergency support system then uses a generation AI to analyze the collected biometric information and detect abnormalities. For example, if the generation AI detects an abnormal heart rate, it responds immediately. The emergency support system then generates information corresponding to an emergency scenario. For example, in the event of a fire, the generation AI calculates the optimal evacuation route and guides the user to a safe evacuation shelter using a combination of navigation and audio guidance. The emergency support system then tracks the user's location information in real time and notifies emergency services. This allows for rapid rescue. The emergency support system also continuously monitors the user's health status and notifies medical institutions if an abnormality is detected. For example, if signs of a heart attack are detected, the generative AI can immediately call an ambulance and provide first aid instructions to the user. This significantly improves user support during emergencies, protecting the user's safety and health. This allows the emergency support system to collect the user's biometric information in real time and provide appropriate responses in emergencies. For example, detailed collection of the user's biometric information and rapid detection of abnormalities enables rapid and accurate responses. Generative AI can also generate information to respond appropriately to emergencies and instruct the user on appropriate actions. Furthermore, tracking the user's location and quickly notifying emergency services can enable rapid rescue. This protects the user's safety and health.

[0029] The emergency support system according to the embodiment includes a collection unit, an analysis unit, a generation unit, an instruction unit, and a notification unit. The collection unit collects biometric information of a user. The biometric information of the user includes, for example, a heart rate, a body temperature, and blood pressure, but is not limited to these examples. The collection unit measures, for example, a heart rate using a heart rate sensor. The collection unit can also measure a body temperature using a thermometer. The collection unit can also measure a blood pressure using a sphygmomanometer. For example, the collection unit measures a heart rate in real time using a heart rate sensor. The collection unit can also periodically measure a body temperature using a thermometer. The collection unit can also periodically measure a blood pressure using a sphygmomanometer. The analysis unit analyzes the biometric information collected by the collection unit. The analysis is performed, for example, using an anomaly detection algorithm, but is not limited to these examples. For example, the analysis unit detects an abnormality in the heart rate using the anomaly detection algorithm. The analysis unit can also detect an abnormality in the body temperature using the anomaly detection algorithm. The analysis unit can also detect an abnormality in the blood pressure using the anomaly detection algorithm. For example, the analysis unit detects an abnormality based on a value outside the normal range to detect an abnormal heart rate. The analysis unit can also detect an abnormality based on a value outside the normal range to detect an abnormal body temperature. The analysis unit can also detect an abnormality based on a value outside the normal range to detect an abnormal blood pressure. The generation unit generates emergency response information based on the information analyzed by the analysis unit. The generation generates, for example, information corresponding to an emergency scenario, but is not limited to such an example. For example, the generation unit generates information corresponding to a fire outbreak. The generation unit can also generate information corresponding to an earthquake outbreak. The generation unit can also generate information corresponding to a traffic accident outbreak. For example, the generation unit calculates an optimal evacuation route in the event of a fire. The generation unit can also calculate an optimal evacuation route in the event of an earthquake. The generation unit can also calculate an optimal evacuation route in the event of a traffic accident. The instruction unit issues instructions to the user based on the information generated by the generation unit. The instructions are given, for example, by voice instruction or text instruction, but are not limited to such an example.For example, the instruction unit instructs the user on an evacuation route using voice instructions. The instruction unit can also instruct the user on an evacuation route using text instructions. The instruction unit can also instruct the user on an evacuation route by combining voice instructions and text instructions. For example, the instruction unit instructs the user on an optimal evacuation route using voice instructions. The instruction unit can also instruct the user on an optimal evacuation route using text instructions. The instruction unit can also instruct the user on an optimal evacuation route by combining voice instructions and text instructions. The notification unit tracks the user's location information and notifies an emergency service. Notification is performed using, for example, GPS, but is not limited to this example. For example, the notification unit tracks the user's location information using GPS. The notification unit can also track the user's location information using Wi-Fi location information. The notification unit can also notify the emergency service of the user's location information. For example, the notification unit tracks the user's location information using GPS and notifies the emergency service. The notification unit can also track the user's location information using Wi-Fi location information and notify the emergency service. The notification unit can also notify emergency services of the user's location information. As a result, the emergency support system according to the embodiment can collect the user's biometric information in real time and provide appropriate responses in emergencies. For example, detailed collection of the user's biometric information and rapid detection of abnormalities enables rapid and accurate responses. Furthermore, by using a generative AI, it is possible to generate information to respond appropriately to emergencies and instruct the user on appropriate actions. Furthermore, by tracking the user's location information and quickly notifying emergency services, rapid rescue can be expected. This can protect the user's safety and health.

[0030] The collection unit can collect biometric information including heart rate, body temperature, and blood pressure. The collection unit, for example, measures heart rate using a heart rate sensor. For example, the collection unit measures heart rate in real time using the heart rate sensor. The collection unit can also measure body temperature using a thermometer. For example, the collection unit periodically measures body temperature using a thermometer. The collection unit can also measure blood pressure using a sphygmomanometer. For example, the collection unit periodically measures blood pressure using a sphygmomanometer. This makes it possible to collect detailed biometric information of the user. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input heart rate data acquired by a heart rate sensor to the generation AI, and the generation AI can analyze the heart rate data.

[0031] The analysis unit can analyze the collected biometric information and detect abnormalities. The analysis unit can, for example, detect abnormalities in heart rate using an anomaly detection algorithm. For example, to detect abnormalities in heart rate, the analysis unit can detect abnormalities based on values ​​outside the normal range. The analysis unit can also detect abnormalities in body temperature using an anomaly detection algorithm. For example, to detect abnormalities in body temperature, the analysis unit can detect abnormalities based on values ​​outside the normal range. The analysis unit can also detect abnormalities in blood pressure using an anomaly detection algorithm. For example, to detect abnormalities in blood pressure, the analysis unit can detect abnormalities based on values ​​outside the normal range. This allows for rapid detection of abnormalities in the user's biometric information. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected biometric information into a generation AI, and the generation AI can detect abnormalities.

[0032] The generation unit can generate information corresponding to an emergency scenario. The generation unit generates information corresponding to, for example, a case where a fire occurs. For example, the generation unit calculates an optimal evacuation route when a fire occurs. The generation unit can also generate information corresponding to, for example, a case where an earthquake occurs. For example, the generation unit calculates an optimal evacuation route when an earthquake occurs. The generation unit can also generate information corresponding to, for example, a case where a traffic accident occurs. For example, the generation unit calculates an optimal evacuation route when a traffic accident occurs. This makes it possible to generate information that appropriately responds to an emergency. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to generate information corresponding to an emergency scenario.

[0033] The instruction unit can instruct the user to take an appropriate action based on the generated information. The instruction unit, for example, uses voice instructions to instruct the user on an evacuation route. For example, the instruction unit uses voice instructions to instruct the user on an optimal evacuation route. The instruction unit can also instruct the user on an evacuation route using text instructions. For example, the instruction unit uses text instructions to instruct the user on an optimal evacuation route. The instruction unit can also instruct the user on an evacuation route by combining voice instructions and text instructions. For example, the instruction unit instructs the user on an optimal evacuation route by combining voice instructions and text instructions. This makes it possible to instruct the user on an appropriate action. Some or all of the above-described processing in the instruction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the instruction unit can instruct the user on an appropriate action based on the information generated by the generation AI.

[0034] The notification unit can track the user's location information and notify emergency services. The notification unit can track the user's location information using, for example, GPS. For example, the notification unit can track the user's location information in real time using GPS. The notification unit can also track the user's location information using Wi-Fi location information. For example, the notification unit can track the user's location information in real time using Wi-Fi location information. The notification unit can also notify emergency services of the user's location information. For example, the notification unit can track the user's location information using GPS and notify emergency services. The notification unit can also track the user's location information using Wi-Fi location information and notify emergency services. This makes it possible to track the user's location information and quickly notify emergency services. Some or all of the above-described processing in the notification unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the notification unit can analyze the user's location information using a generation AI and notify emergency services.

[0035] The generation unit can generate information corresponding to emergency scenarios including fires, earthquakes, and traffic accidents. The generation unit generates information corresponding to, for example, a case where a fire occurs. For example, the generation unit calculates an optimal evacuation route in the case of a fire. The generation unit can also generate information corresponding to, for example, a case where an earthquake occurs. For example, the generation unit calculates an optimal evacuation route in the case of an earthquake. The generation unit can also generate information corresponding to, for example, a case where a traffic accident occurs. For example, the generation unit calculates an optimal evacuation route in the case of a traffic accident. This makes it possible to generate information corresponding to a variety of emergency situations. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to generate information corresponding to an emergency scenario.

[0036] The instruction unit can guide the user to a safe shelter by combining navigation and voice guidance. The instruction unit, for example, guides the user to a safe shelter using navigation. For example, the instruction unit guides the user to a safe shelter using GPS navigation. The instruction unit can also guide the user to a safe shelter using voice guidance. For example, the instruction unit guides the user to a safe shelter using a voice assistant. The instruction unit can also guide the user to a safe shelter by combining navigation and voice guidance. For example, the instruction unit guides the user to a safe shelter by combining GPS navigation and a voice assistant. This allows the user to be quickly guided to a safe shelter. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the instruction unit can guide the user to a safe shelter based on navigation information and voice guidance generated by the generation AI.

[0037] The notification unit continuously monitors the user's health condition and can notify a medical institution if an abnormality is detected. The notification unit continuously monitors, for example, the user's biometric information, such as heart rate, body temperature, and blood pressure. For example, the notification unit monitors the heart rate in real time using a heart rate sensor. The notification unit can also periodically monitor the body temperature using a thermometer. The notification unit can also periodically monitor the blood pressure using a sphygmomanometer. For example, the notification unit monitors the heart rate in real time using a heart rate sensor and notifies a medical institution if an abnormality is detected. The notification unit can also periodically monitor the body temperature using a thermometer and notify a medical institution if an abnormality is detected. The notification unit can also periodically monitor the blood pressure using a sphygmomanometer and notify a medical institution if an abnormality is detected. This makes it possible to continuously monitor the user's health condition and quickly notify a medical institution if an abnormality is detected. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the notification unit can analyze the user's biometric information using a generation AI and notify a medical institution if an abnormality is detected.

[0038] The collection unit can analyze the user's past health data and select an appropriate collection method. The collection unit, for example, analyzes the user's past heart rate data. For example, the collection unit can analyze the user's past heart rate data and increase the collection frequency during time periods when abnormalities are common. The collection unit can also concentrate collection during time periods when body temperature spikes based on the user's past body temperature data. The collection unit can also adjust the collection frequency during time periods when blood pressure is unstable based on the user's past blood pressure data. For example, the collection unit can analyze the user's past heart rate data and increase the collection frequency during time periods when abnormalities are common. The collection unit can also concentrate collection during time periods when body temperature spikes based on the user's past body temperature data. The collection unit can also adjust the collection frequency during time periods when blood pressure is unstable based on the user's past blood pressure data. This makes it possible to select an optimal collection method based on the user's past health data. Some or all of the above-described processing by the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can analyze the user's past health data using a generation AI and select an optimal collection method.

[0039] When collecting biometric information, the collection unit can filter the biometric information based on the user's current activity status. For example, when the user is exercising, the collection unit prioritizes collecting data related to the exercise. For example, when the user is exercising, the collection unit prioritizes collecting heart rate and oxygen saturation data. The collection unit can also prioritize collecting resting data when the user is resting. For example, when the user is resting, the collection unit prioritizes collecting body temperature and respiratory rate data. The collection unit can also prioritize collecting data related to stress levels when the user is working. For example, when the user is working, the collection unit prioritizes collecting heart rate and blood pressure data. This allows the biometric information to be collected to be optimized according to the user's activity status. Some or all of the above-described processing by the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can analyze the user's activity status using a generation AI and filter the biometric information to be collected based on the activity status.

[0040] When collecting biometric information, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user is using voice input, the collection unit prioritizes collection of voice data. For example, when the user is using voice input, the collection unit collects voice data using a microphone. The collection unit can also prioritize collection of text data when the user is using text input. For example, when the user is using text input, the collection unit collects text data using a keyboard. The collection unit can also prioritize collection of image data when the user is using image input. For example, when the user is using image input, the collection unit collects image data using a camera. This makes it possible to select the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can analyze the user's input method using a generation AI and select the optimal collection means.

[0041] When collecting biometric information, the collection unit can prioritize collecting highly relevant information taking into account the user's geographical location information. For example, when the user is at high altitude, the collection unit prioritizes collecting oxygen saturation data. For example, when the user is at high altitude, the collection unit prioritizes collecting oxygen saturation data. The collection unit can also prioritize collecting body temperature data when the user is in a cold region. For example, when the user is in a cold region, the collection unit prioritizes collecting body temperature data. The collection unit can also prioritize collecting stress level data when the user is in an urban area. For example, when the user is in an urban area, the collection unit prioritizes collecting heart rate and blood pressure data. This makes it possible to prioritize collecting highly relevant biometric information based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can analyze the user's geographical location information using a generation AI and prioritize collecting highly relevant biometric information.

[0042] When collecting biometric information, the collection unit can analyze the user's social media activity and collect related information. For example, if the user posts on social media that they are feeling stressed, the collection unit prioritizes collecting heart rate and blood pressure data. For example, if the user posts on social media that they are feeling stressed, the collection unit prioritizes collecting heart rate and blood pressure data. The collection unit can also prioritize collecting body temperature and respiratory rate data if the user posts on social media that they are relaxing. For example, if the user posts on social media that they are relaxing, the collection unit prioritizes collecting body temperature and respiratory rate data. The collection unit can also collect all biometric information equally if the user posts on social media that they are facing an emergency. For example, if the user posts on social media that they are facing an emergency, the collection unit collects all biometric information equally. This makes it possible to collect related biometric information based on the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can analyze the user's social media activity using a generation AI and collect related biometric information.

[0043] When collecting biometric information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, if the user has previously requested heart rate data collection, the collection unit prioritizes collecting heart rate data. For example, if the user has previously requested heart rate data collection, the collection unit prioritizes collecting heart rate data. Furthermore, if the user has previously requested body temperature data collection, the collection unit can also prioritize collecting body temperature data. For example, if the user has previously requested body temperature data collection, the collection unit prioritizes collecting body temperature data. Furthermore, if the user has previously requested blood pressure data collection, the collection unit can also prioritize collecting blood pressure data. For example, if the user has previously requested blood pressure data collection, the collection unit prioritizes collecting blood pressure data. This makes it possible to optimize the collection method based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can customize the collection method by analyzing the user's past feedback using a generation AI.

[0044] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected biometric information. For example, if heart rate data is abnormal, the analysis unit performs a detailed analysis. For example, if heart rate data is abnormal, the analysis unit performs a detailed analysis. The analysis unit can also perform a detailed analysis if body temperature data is abnormal. For example, if body temperature data is abnormal, the analysis unit performs a detailed analysis. The analysis unit can also perform a detailed analysis if blood pressure data is abnormal. For example, if blood pressure data is abnormal, the analysis unit performs a detailed analysis. This makes it possible to optimize the level of detail of the analysis according to the importance of the collected biometric information. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the importance of the collected biometric information using a generation AI, and adjust the level of detail of the analysis based on the importance.

[0045] During analysis, the analysis unit can apply different analysis algorithms according to different biometric information categories. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a body temperature variability analysis algorithm to body temperature data. For example, the analysis unit applies a body temperature variability analysis algorithm to body temperature data. The analysis unit can also apply a blood pressure variability analysis algorithm to blood pressure data. For example, the analysis unit applies a blood pressure variability analysis algorithm to blood pressure data. This makes it possible to apply an optimal analysis algorithm according to different biometric information categories. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze different biometric information categories using a generation AI and apply an optimal analysis algorithm.

[0046] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve the analysis accuracy of current heart rate data by referring to the user's past heart rate analysis results. For example, the analysis unit can improve the analysis accuracy of current heart rate data by referring to the user's past heart rate analysis results. The analysis unit can also improve the analysis accuracy of current body temperature data by referring to the user's past body temperature analysis results. For example, the analysis unit can improve the analysis accuracy of current body temperature data by referring to the user's past body temperature analysis results. The analysis unit can also improve the analysis accuracy of current blood pressure data by referring to the user's past blood pressure analysis results. For example, the analysis unit can improve the analysis accuracy of current blood pressure data by referring to the user's past blood pressure analysis results. This allows the accuracy of analysis to be improved based on the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the user's past analysis results using a generation AI to improve the accuracy of the analysis.

[0047] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the collected biometric information. The analysis unit, for example, prioritizes analysis of the most recent heart rate data. For example, the analysis unit prioritizes analysis of the most recent heart rate data. The analysis unit can also prioritize analysis of the most recent body temperature data. For example, the analysis unit prioritizes analysis of the most recent body temperature data. The analysis unit can also prioritize analysis of the most recent blood pressure data. For example, the analysis unit prioritizes analysis of the most recent blood pressure data. This makes it possible to optimize the priority of analysis based on the time of submission of the collected biometric information. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the time of submission of the collected biometric information using a generation AI, and determine the priority of analysis based on the time of submission.

[0048] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the collected biometric information. For example, if heart rate data and blood pressure data are related to each other, the analysis unit analyzes them simultaneously. For example, if heart rate data and blood pressure data are related to each other, the analysis unit analyzes them simultaneously. The analysis unit can also analyze body temperature data and respiratory rate data simultaneously if they are related to each other. For example, if body temperature data and respiratory rate data are related to each other, the analysis unit analyzes them simultaneously. The analysis unit can also analyze blood pressure data and body temperature data simultaneously if they are related to each other. For example, if blood pressure data and body temperature data are related to each other, the analysis unit analyzes them simultaneously. This makes it possible to optimize the order of analysis based on the relevance of the collected biometric information. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the relevance of the collected biometric information using a generation AI, and adjust the order of analysis based on the relevance.

[0049] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is a medical professional, the analysis unit provides analysis results that use a lot of technical terms. For example, if the user is a medical professional, the analysis unit provides analysis results that use a lot of technical terms. Furthermore, if the user is a layperson, the analysis unit can also provide analysis results that avoid technical terms. For example, if the user is a layperson, the analysis unit can provide analysis results that avoid technical terms. Furthermore, if the user is a student, the analysis unit can also provide analysis results that include educational commentary. For example, if the user is a student, the analysis unit provides analysis results that include educational commentary. This allows the analysis results to be optimized according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the user's level of expertise using a generation AI and adjust the use of technical terms in the analysis according to the level of expertise.

[0050] When generating emergency response information, the generation unit can adjust the level of detail of the generation based on the importance of the emergency situation. For example, when a fire occurs, the generation unit generates a detailed evacuation route. For example, when a fire occurs, the generation unit generates a detailed evacuation route. The generation unit can also generate a detailed evacuation route when an earthquake occurs. For example, when an earthquake occurs, the generation unit generates a detailed evacuation route. The generation unit can also generate a detailed evacuation route when a traffic accident occurs. For example, when a traffic accident occurs, the generation unit generates a detailed evacuation route. This makes it possible to optimize the level of detail of the generation according to the importance of the emergency situation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can analyze the importance of the emergency situation using a generation AI, and adjust the level of detail of the generation based on the importance.

[0051] When generating emergency response information, the generation unit can apply different generation algorithms according to different emergency scenarios. For example, in the case of a fire, the generation unit applies a generation algorithm dedicated to fires. For example, in the case of a fire, the generation unit applies a generation algorithm dedicated to fires. Furthermore, in the case of an earthquake, the generation unit can also apply a generation algorithm dedicated to earthquakes. For example, in the case of an earthquake, the generation unit applies a generation algorithm dedicated to earthquakes. Furthermore, in the case of a traffic accident, the generation unit can also apply a generation algorithm dedicated to traffic accidents. For example, in the case of a traffic accident, the generation unit applies a generation algorithm dedicated to traffic accidents. This makes it possible to apply an optimal generation algorithm according to different emergency scenarios. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can analyze different emergency scenarios using a generation AI and apply an optimal generation algorithm.

[0052] When generating emergency response information, the generation unit can improve the accuracy of generation by referring to the user's past emergency response results. For example, the generation unit can improve the accuracy of generation of current fire response information by referring to the user's past fire response results. For example, the generation unit can improve the accuracy of generation of current fire response information by referring to the user's past fire response results. The generation unit can also improve the accuracy of generation of current earthquake response information by referring to the user's past earthquake response results. For example, the generation unit can improve the accuracy of generation of current earthquake response information by referring to the user's past earthquake response results. The generation unit can also improve the accuracy of generation of current traffic accident response information by referring to the user's past traffic accident response results. For example, the generation unit can improve the accuracy of generation of current traffic accident response information by referring to the user's past traffic accident response results. This allows the accuracy of generation to be improved based on the user's past emergency response results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can analyze the user's past emergency response results using a generation AI to improve the accuracy of generation.

[0053] When generating emergency response information, the generation unit can determine the priority of generation based on the time of occurrence of the emergency. For example, the generation unit prioritizes generating the latest fire information. For example, the generation unit prioritizes generating the latest fire information. The generation unit can also prioritize generating the latest earthquake information. For example, the generation unit prioritizes generating the latest earthquake information. The generation unit can also prioritize generating the latest traffic accident information. For example, the generation unit prioritizes generating the latest traffic accident information. This makes it possible to optimize the priority of generation based on the time of occurrence of the emergency. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can analyze the time of occurrence of the emergency using a generation AI and determine the priority of generation based on the time of occurrence.

[0054] When generating emergency response information, the generation unit can adjust the order of generation based on the relevance of the emergency situation. For example, the generation unit prioritizes generating information related to fires. For example, the generation unit prioritizes generating information related to fires. The generation unit can also prioritize generating information related to earthquakes. For example, the generation unit prioritizes generating information related to earthquakes. The generation unit can also prioritize generating information related to traffic accidents. For example, the generation unit prioritizes generating information related to traffic accidents. This makes it possible to optimize the order of generation based on the relevance of the emergency situation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can analyze the relevance of the emergency situation using a generation AI and adjust the order of generation based on the relevance.

[0055] When generating emergency response information, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user is a medical professional, the generation unit generates information that uses a lot of technical terminology. For example, if the user is a medical professional, the generation unit generates information that uses a lot of technical terminology. Furthermore, if the user is a layperson, the generation unit can also generate information that avoids technical terminology. For example, if the user is a layperson, the generation unit can generate information that avoids technical terminology. Furthermore, if the user is a student, the generation unit can also generate information that includes educational commentary. For example, if the user is a student, the generation unit generates information that includes educational commentary. This makes it possible to optimize the emergency response information according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can analyze the user's level of expertise using a generation AI and adjust the use of technical terminology in the generation according to the level of expertise.

[0056] The instruction unit can adjust the level of detail of the instruction based on the importance of the emergency response information when giving an instruction. The instruction unit provides detailed evacuation instructions, for example, when a fire has occurred. For example, the instruction unit provides detailed evacuation instructions when a fire has occurred. The instruction unit can also provide detailed evacuation instructions when an earthquake has occurred. For example, the instruction unit provides detailed evacuation instructions when an earthquake has occurred. The instruction unit can also provide detailed evacuation instructions when a traffic accident has occurred. For example, the instruction unit provides detailed evacuation instructions when a traffic accident has occurred. This makes it possible to optimize the level of detail of the instructions according to the importance of the emergency response information. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the instruction unit can analyze the importance of the emergency response information using a generation AI, and adjust the level of detail of the instructions based on the importance.

[0057] When issuing an instruction, the instruction unit can apply different instruction algorithms according to different emergency scenarios. For example, in the case of a fire, the instruction unit applies a fire-specific instruction algorithm. For example, in the case of a fire, the instruction unit applies a fire-specific instruction algorithm. Furthermore, in the case of an earthquake, the instruction unit can also apply an earthquake-specific instruction algorithm. For example, in the case of an earthquake, the instruction unit applies an earthquake-specific instruction algorithm. Furthermore, in the case of a traffic accident, the instruction unit can also apply a traffic accident-specific instruction algorithm. For example, in the case of a traffic accident, the instruction unit applies a traffic accident-specific instruction algorithm. This makes it possible to apply an optimal instruction algorithm according to different emergency scenarios. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the instruction unit can analyze different emergency scenarios using a generation AI and apply an optimal instruction algorithm.

[0058] When giving an instruction, the instruction unit can improve the accuracy of the instruction by referring to the user's past instruction results. The instruction unit, for example, can improve the accuracy of the current fire response instruction by referring to the user's past fire response results. For example, the instruction unit can improve the accuracy of the current fire response instruction by referring to the user's past fire response results. The instruction unit can also improve the accuracy of the current earthquake response instruction by referring to the user's past earthquake response results. For example, the instruction unit can improve the accuracy of the current earthquake response instruction by referring to the user's past traffic accident response results. For example, the instruction unit can improve the accuracy of the current traffic accident response instruction by referring to the user's past traffic accident response results. In this way, the accuracy of the instruction can be improved based on the user's past instruction results. Some or all of the above-described processing in the instruction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the instruction unit can analyze the user's past instruction results using a generation AI to improve the accuracy of the instruction.

[0059] When giving an instruction, the instruction unit can determine the priority of the instruction based on the time of occurrence of the emergency. For example, the instruction unit gives priority to the latest fire information. For example, the instruction unit gives priority to the latest fire information. The instruction unit can also give priority to the latest earthquake information. For example, the instruction unit gives priority to the latest earthquake information. The instruction unit can also give priority to the latest traffic accident information. For example, the instruction unit gives priority to the latest traffic accident information. This makes it possible to optimize the priority of the instructions based on the time of occurrence of the emergency. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the instruction unit can analyze the time of occurrence of the emergency using a generation AI and determine the priority of the instructions based on the time of occurrence.

[0060] When giving instructions, the instruction unit can adjust the order of instructions based on the relevance of the emergency situation. For example, the instruction unit gives priority to information related to fires. For example, the instruction unit gives priority to information related to fires. The instruction unit can also give priority to information related to earthquakes. For example, the instruction unit gives priority to information related to earthquakes. The instruction unit can also give priority to information related to traffic accidents. For example, the instruction unit gives priority to information related to traffic accidents. This makes it possible to optimize the order of instructions based on the relevance of the emergency situation. Some or all of the above-described processing in the instruction unit may be performed using, or without, a generation AI. For example, the instruction unit can analyze the relevance of the emergency situation using the generation AI and adjust the order of instructions based on the relevance.

[0061] When giving instructions, the instruction unit can adjust the use of technical terms in the instructions according to the user's level of expertise. For example, if the user is a medical professional, the instruction unit provides instructions that use a lot of technical terms. For example, if the user is a medical professional, the instruction unit provides instructions that use a lot of technical terms. Furthermore, if the user is a general public, the instruction unit can also provide instructions that avoid technical terms. For example, if the user is a general public, the instruction unit can also provide instructions that include educational explanations if the user is a student. For example, if the user is a student, the instruction unit provides instructions that include educational explanations. This allows the instructions to be optimized according to the user's level of expertise. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the instruction unit can analyze the user's level of expertise using a generation AI and adjust the use of technical terms in the instructions according to the level of expertise.

[0062] The notification unit can adjust the level of detail of the notification based on the importance of the emergency response information at the time of notification. The notification unit, for example, provides a detailed evacuation notification when a fire occurs. For example, the notification unit provides a detailed evacuation notification when a fire occurs. The notification unit can also provide a detailed evacuation notification when an earthquake occurs. For example, the notification unit provides a detailed evacuation notification when an earthquake occurs. The notification unit can also provide a detailed evacuation notification when a traffic accident occurs. For example, the notification unit provides a detailed evacuation notification when a traffic accident occurs. This makes it possible to optimize the level of detail of the notification according to the importance of the emergency response information. Some or all of the above-mentioned processing in the notification unit may be performed using, or without, a generation AI. For example, the notification unit can analyze the importance of the emergency response information using a generation AI and adjust the level of detail of the notification based on the importance.

[0063] The notification unit can apply different notification algorithms according to different emergency scenarios when making a notification. For example, in the case of a fire, the notification unit applies a notification algorithm dedicated to the fire. For example, in the case of a fire, the notification unit applies a notification algorithm dedicated to the fire. Furthermore, in the case of an earthquake, the notification unit can also apply a notification algorithm dedicated to the earthquake. For example, in the case of an earthquake, the notification unit applies a notification algorithm dedicated to the earthquake. Furthermore, in the case of a traffic accident, the notification unit can also apply a notification algorithm dedicated to the traffic accident. For example, in the case of a traffic accident, the notification unit applies a notification algorithm dedicated to the traffic accident. This makes it possible to apply an optimal notification algorithm according to different emergency scenarios. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the notification unit can analyze different emergency scenarios using a generation AI and apply an optimal notification algorithm.

[0064] The notification unit can improve the accuracy of notifications by referring to the user's past notification results when making notifications. For example, the notification unit can improve the accuracy of a current fire notification by referring to the user's past fire notification results. For example, the notification unit can improve the accuracy of a current fire notification by referring to the user's past fire notification results. The notification unit can also improve the accuracy of a current earthquake notification by referring to the user's past earthquake notification results. For example, the notification unit can improve the accuracy of a current earthquake notification by referring to the user's past earthquake notification results. The notification unit can also improve the accuracy of a current traffic accident notification by referring to the user's past traffic accident notification results. For example, the notification unit can improve the accuracy of a current traffic accident notification by referring to the user's past traffic accident notification results. This allows the accuracy of notifications to be improved based on the user's past notification results. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the notification unit can analyze the user's past notification results using a generation AI to improve the accuracy of notifications.

[0065] At the time of notification, the notification unit can determine the priority of notifications based on the time of occurrence of an emergency. The notification unit, for example, prioritizes notification of the latest fire information. For example, the notification unit prioritizes notification of the latest fire information. The notification unit can also prioritize notification of the latest earthquake information. For example, the notification unit prioritizes notification of the latest earthquake information. The notification unit can also prioritize notification of the latest traffic accident information. For example, the notification unit prioritizes notification of the latest traffic accident information. This makes it possible to optimize the priority of notifications based on the time of occurrence of an emergency. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the notification unit can analyze the time of occurrence of an emergency using a generation AI and determine the priority of notifications based on the time of occurrence.

[0066] The notification unit can adjust the order of notifications based on the relevance of the emergency at the time of notification. The notification unit, for example, prioritizes notifying information related to fires. For example, the notification unit prioritizes notifying information related to fires. The notification unit can also prioritize notifying information related to earthquakes. For example, the notification unit prioritizes notifying information related to earthquakes. The notification unit can also prioritize notifying information related to traffic accidents. For example, the notification unit prioritizes notifying information related to traffic accidents. This makes it possible to optimize the order of notifications based on the relevance of the emergency. Some or all of the above-described processing in the notification unit may be performed using, or without, a generation AI. For example, the notification unit can analyze the relevance of the emergency using a generation AI and adjust the order of notifications based on the relevance.

[0067] The notification unit can adjust the use of technical terms in the notification according to the user's level of expertise when providing a notification. For example, if the user is a medical professional, the notification unit provides a notification that uses a lot of technical terms. For example, if the user is a medical professional, the notification unit provides a notification that uses a lot of technical terms. The notification unit can also provide a notification that avoids technical terms if the user is a general public. For example, if the user is a general public, the notification unit can provide a notification that avoids technical terms. The notification unit can also provide a notification that includes educational commentary if the user is a student. For example, if the user is a student, the notification unit provides a notification that includes educational commentary. This allows the notification to be optimized according to the user's level of expertise. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the notification unit can analyze the user's level of expertise using a generation AI and adjust the use of technical terms in the notification according to the level of expertise.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The emergency support system may further include a history analysis unit that analyzes the user's past emergency response history. The history analysis unit records and analyzes what kind of emergencies the user has faced in the past and the responses they have taken. For example, the history analysis unit may record the evacuation routes and actions taken when the user has previously encountered a fire, and suggest the optimal evacuation route the next time a fire occurs. The history analysis unit may also record the evacuation actions taken when the user has previously encountered an earthquake, and instruct the user on appropriate actions the next time an earthquake occurs. Furthermore, the history analysis unit may record the responses taken when the user has previously encountered a traffic accident, and support a prompt response the next time a traffic accident occurs. This makes it possible to provide a more effective emergency response based on the user's past emergency response history.

[0070] The emergency support system may further include an activity detection unit that detects the user's current activity status and customizes emergency response information based on the activity status. The activity detection unit detects different situations, such as whether the user is exercising, resting, or working. For example, if the user is exercising, the activity detection unit may preferentially provide emergency response information related to exercise. If the user is resting, the activity detection unit may also provide emergency response information suitable for resting. Furthermore, if the user is working, the activity detection unit may also provide emergency response information related to stress levels. This makes it possible to provide optimal emergency response information according to the user's activity status.

[0071] The emergency support system may further include a social media analysis unit that analyzes a user's social media activities and provides relevant emergency response information. The social media analysis unit analyzes content posted by the user on social media and extracts information related to an emergency. For example, if the user posts about feeling stressed, the social media analysis unit may prioritize collecting heart rate and blood pressure data and provide appropriate emergency response information. Alternatively, if the user posts about feeling relaxed, the social media analysis unit may prioritize collecting body temperature and respiratory rate data and provide appropriate emergency response information. Furthermore, if the user posts about facing an emergency, the social media analysis unit may equally collect all biometric information to support a rapid response. This makes it possible to provide relevant emergency response information based on the user's social media activities.

[0072] The emergency support system may further include a feedback reflecting unit that customizes the emergency response information by reflecting the user's past feedback. The feedback reflecting unit analyzes the feedback provided by the user in the past and optimizes the method of providing the emergency response information. For example, if the user previously requested collection of heart rate data, the feedback reflecting unit may prioritize collection of heart rate data and provide appropriate emergency response information. Furthermore, if the user previously requested collection of body temperature data, the feedback reflecting unit may prioritize collection of body temperature data and provide appropriate emergency response information. Furthermore, if the user previously requested collection of blood pressure data, the feedback reflecting unit may prioritize collection of blood pressure data and provide appropriate emergency response information. This makes it possible to optimize the emergency response information based on the user's past feedback.

[0073] The emergency support system may further include a geographic information analysis unit that provides emergency response information taking into account the user's geographic location information. The geographic information analysis unit customizes the emergency response information based on the characteristics of the user's current location. For example, if the user is in a high altitude, the geographic information analysis unit may prioritize collecting oxygen saturation data and provide appropriate emergency response information. If the user is in a cold region, the geographic information analysis unit may prioritize collecting body temperature data and provide appropriate emergency response information. Furthermore, if the user is in an urban area, the geographic information analysis unit may prioritize collecting stress level data and provide appropriate emergency response information. This allows optimal emergency response information to be provided based on the user's geographic location information.

[0074] The emergency support system may further include an expertise adjustment unit that provides emergency response information according to the user's level of expertise. The expertise adjustment unit determines whether the user is a medical professional, a layperson, or a student, and provides information according to the determination. For example, if the user is a medical professional, the expertise adjustment unit may provide information that uses a lot of technical terms. If the user is a layperson, the expertise adjustment unit may also provide information that avoids technical terms. Furthermore, if the user is a student, the expertise adjustment unit may also provide information that includes educational commentary. This makes it possible to provide optimal emergency response information according to the user's level of expertise.

[0075] The processing flow of the first embodiment will be briefly explained below.

[0076] Step 1: The collection unit collects the user's biometric information. The user's biometric information includes heart rate, body temperature, blood pressure, etc. The collection unit measures this information in real time or periodically using a heart rate sensor, a thermometer, and a blood pressure monitor. Step 2: The analysis unit analyzes the vital signs collected by the collection unit. The analysis is performed using an anomaly detection algorithm to detect abnormalities in heart rate, body temperature, and blood pressure. Step 3: The generating unit generates emergency response information based on the information analyzed by the analyzing unit. The generated information includes information for responding to emergencies such as fires, earthquakes, and traffic accidents. Step 4: The instruction unit issues instructions to the user based on the information generated by the generation unit. The instructions are given as voice instructions or text instructions, and provide the user with the optimal evacuation route, etc. Step 5: The notification module tracks the user's location and notifies emergency services using GPS and Wi-Fi location.

[0077] (Example 2) An emergency support system according to an embodiment of the present invention collects a user's biometric information in real time, generates emergency response information using a generation AI, and guides the user to a safe evacuation shelter using a combination of navigation and audio guidance. The emergency support system collects the user's biometric information, analyzes it using a generation AI, and generates emergency response information to instruct the user on appropriate actions. The emergency support system can also track the user's location information in real time and notify emergency services. For example, the emergency support system collects biometric information such as the user's heart rate, body temperature, and blood pressure. The emergency support system then uses a generation AI to analyze the collected biometric information and detect abnormalities. For example, if the generation AI detects an abnormal heart rate, it responds immediately. The emergency support system then generates information corresponding to an emergency scenario. For example, in the event of a fire, the generation AI calculates the optimal evacuation route and guides the user to a safe evacuation shelter using a combination of navigation and audio guidance. The emergency support system then tracks the user's location information in real time and notifies emergency services. This allows for rapid rescue. The emergency support system also continuously monitors the user's health status and notifies medical institutions if an abnormality is detected. For example, if signs of a heart attack are detected, the generative AI can immediately call an ambulance and provide first aid instructions to the user. This significantly improves user support during emergencies, protecting the user's safety and health. This allows the emergency support system to collect the user's biometric information in real time and provide appropriate responses in emergencies. For example, detailed collection of the user's biometric information and rapid detection of abnormalities enables rapid and accurate responses. Generative AI can also generate information to respond appropriately to emergencies and instruct the user on appropriate actions. Furthermore, tracking the user's location and quickly notifying emergency services can enable rapid rescue. This protects the user's safety and health.

[0078] The emergency support system according to the embodiment includes a collection unit, an analysis unit, a generation unit, an instruction unit, and a notification unit. The collection unit collects biometric information of a user. The biometric information of the user includes, for example, a heart rate, a body temperature, and blood pressure, but is not limited to these examples. The collection unit measures, for example, a heart rate using a heart rate sensor. The collection unit can also measure a body temperature using a thermometer. The collection unit can also measure a blood pressure using a sphygmomanometer. For example, the collection unit measures a heart rate in real time using a heart rate sensor. The collection unit can also periodically measure a body temperature using a thermometer. The collection unit can also periodically measure a blood pressure using a sphygmomanometer. The analysis unit analyzes the biometric information collected by the collection unit. The analysis is performed, for example, using an anomaly detection algorithm, but is not limited to these examples. For example, the analysis unit detects an abnormality in the heart rate using the anomaly detection algorithm. The analysis unit can also detect an abnormality in the body temperature using the anomaly detection algorithm. The analysis unit can also detect an abnormality in the blood pressure using the anomaly detection algorithm. For example, the analysis unit detects an abnormality based on a value outside the normal range to detect an abnormal heart rate. The analysis unit can also detect an abnormality based on a value outside the normal range to detect an abnormal body temperature. The analysis unit can also detect an abnormality based on a value outside the normal range to detect an abnormal blood pressure. The generation unit generates emergency response information based on the information analyzed by the analysis unit. The generation generates, for example, information corresponding to an emergency scenario, but is not limited to such an example. For example, the generation unit generates information corresponding to a fire outbreak. The generation unit can also generate information corresponding to an earthquake outbreak. The generation unit can also generate information corresponding to a traffic accident outbreak. For example, the generation unit calculates an optimal evacuation route in the event of a fire. The generation unit can also calculate an optimal evacuation route in the event of an earthquake. The generation unit can also calculate an optimal evacuation route in the event of a traffic accident. The instruction unit issues instructions to the user based on the information generated by the generation unit. The instructions are given, for example, by voice instruction or text instruction, but are not limited to such an example.For example, the instruction unit instructs the user on an evacuation route using voice instructions. The instruction unit can also instruct the user on an evacuation route using text instructions. The instruction unit can also instruct the user on an evacuation route by combining voice instructions and text instructions. For example, the instruction unit instructs the user on an optimal evacuation route using voice instructions. The instruction unit can also instruct the user on an optimal evacuation route using text instructions. The instruction unit can also instruct the user on an optimal evacuation route by combining voice instructions and text instructions. The notification unit tracks the user's location information and notifies an emergency service. Notification is performed using, for example, GPS, but is not limited to this example. For example, the notification unit tracks the user's location information using GPS. The notification unit can also track the user's location information using Wi-Fi location information. The notification unit can also notify the emergency service of the user's location information. For example, the notification unit tracks the user's location information using GPS and notifies the emergency service. The notification unit can also track the user's location information using Wi-Fi location information and notify the emergency service. The notification unit can also notify emergency services of the user's location information. As a result, the emergency support system according to the embodiment can collect the user's biometric information in real time and provide appropriate responses in emergencies. For example, detailed collection of the user's biometric information and rapid detection of abnormalities enables rapid and accurate responses. Furthermore, by using a generative AI, it is possible to generate information to respond appropriately to emergencies and instruct the user on appropriate actions. Furthermore, by tracking the user's location information and quickly notifying emergency services, rapid rescue can be expected. This can protect the user's safety and health.

[0079] The collection unit can collect biometric information including heart rate, body temperature, and blood pressure. The collection unit, for example, measures heart rate using a heart rate sensor. For example, the collection unit measures heart rate in real time using the heart rate sensor. The collection unit can also measure body temperature using a thermometer. For example, the collection unit periodically measures body temperature using a thermometer. The collection unit can also measure blood pressure using a sphygmomanometer. For example, the collection unit periodically measures blood pressure using a sphygmomanometer. This makes it possible to collect detailed biometric information of the user. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input heart rate data acquired by a heart rate sensor to the generation AI, and the generation AI can analyze the heart rate data.

[0080] The analysis unit can analyze the collected biometric information and detect abnormalities. The analysis unit can, for example, detect abnormalities in heart rate using an anomaly detection algorithm. For example, to detect abnormalities in heart rate, the analysis unit can detect abnormalities based on values ​​outside the normal range. The analysis unit can also detect abnormalities in body temperature using an anomaly detection algorithm. For example, to detect abnormalities in body temperature, the analysis unit can detect abnormalities based on values ​​outside the normal range. The analysis unit can also detect abnormalities in blood pressure using an anomaly detection algorithm. For example, to detect abnormalities in blood pressure, the analysis unit can detect abnormalities based on values ​​outside the normal range. This allows for rapid detection of abnormalities in the user's biometric information. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected biometric information into a generation AI, and the generation AI can detect abnormalities.

[0081] The generation unit can generate information corresponding to an emergency scenario. The generation unit generates information corresponding to, for example, a case where a fire occurs. For example, the generation unit calculates an optimal evacuation route when a fire occurs. The generation unit can also generate information corresponding to, for example, a case where an earthquake occurs. For example, the generation unit calculates an optimal evacuation route when an earthquake occurs. The generation unit can also generate information corresponding to, for example, a case where a traffic accident occurs. For example, the generation unit calculates an optimal evacuation route when a traffic accident occurs. This makes it possible to generate information that appropriately responds to an emergency. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to generate information corresponding to an emergency scenario.

[0082] The instruction unit can instruct the user to take an appropriate action based on the generated information. The instruction unit, for example, uses voice instructions to instruct the user on an evacuation route. For example, the instruction unit uses voice instructions to instruct the user on an optimal evacuation route. The instruction unit can also instruct the user on an evacuation route using text instructions. For example, the instruction unit uses text instructions to instruct the user on an optimal evacuation route. The instruction unit can also instruct the user on an evacuation route by combining voice instructions and text instructions. For example, the instruction unit instructs the user on an optimal evacuation route by combining voice instructions and text instructions. This makes it possible to instruct the user on an appropriate action. Some or all of the above-described processing in the instruction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the instruction unit can instruct the user on an appropriate action based on the information generated by the generation AI.

[0083] The notification unit can track the user's location information and notify emergency services. The notification unit can track the user's location information using, for example, GPS. For example, the notification unit can track the user's location information in real time using GPS. The notification unit can also track the user's location information using Wi-Fi location information. For example, the notification unit can track the user's location information in real time using Wi-Fi location information. The notification unit can also notify emergency services of the user's location information. For example, the notification unit can track the user's location information using GPS and notify emergency services. The notification unit can also track the user's location information using Wi-Fi location information and notify emergency services. This makes it possible to track the user's location information and quickly notify emergency services. Some or all of the above-described processing in the notification unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the notification unit can analyze the user's location information using a generation AI and notify emergency services.

[0084] The generation unit can generate information corresponding to emergency scenarios including fires, earthquakes, and traffic accidents. The generation unit generates information corresponding to, for example, a case where a fire occurs. For example, the generation unit calculates an optimal evacuation route in the case of a fire. The generation unit can also generate information corresponding to, for example, a case where an earthquake occurs. For example, the generation unit calculates an optimal evacuation route in the case of an earthquake. The generation unit can also generate information corresponding to, for example, a case where a traffic accident occurs. For example, the generation unit calculates an optimal evacuation route in the case of a traffic accident. This makes it possible to generate information corresponding to a variety of emergency situations. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to generate information corresponding to an emergency scenario.

[0085] The instruction unit can guide the user to a safe shelter by combining navigation and voice guidance. The instruction unit, for example, guides the user to a safe shelter using navigation. For example, the instruction unit guides the user to a safe shelter using GPS navigation. The instruction unit can also guide the user to a safe shelter using voice guidance. For example, the instruction unit guides the user to a safe shelter using a voice assistant. The instruction unit can also guide the user to a safe shelter by combining navigation and voice guidance. For example, the instruction unit guides the user to a safe shelter by combining GPS navigation and a voice assistant. This allows the user to be quickly guided to a safe shelter. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the instruction unit can guide the user to a safe shelter based on navigation information and voice guidance generated by the generation AI.

[0086] The notification unit continuously monitors the user's health condition and can notify a medical institution if an abnormality is detected. The notification unit continuously monitors, for example, the user's biometric information, such as heart rate, body temperature, and blood pressure. For example, the notification unit monitors the heart rate in real time using a heart rate sensor. The notification unit can also periodically monitor the body temperature using a thermometer. The notification unit can also periodically monitor the blood pressure using a sphygmomanometer. For example, the notification unit monitors the heart rate in real time using a heart rate sensor and notifies a medical institution if an abnormality is detected. The notification unit can also periodically monitor the body temperature using a thermometer and notify a medical institution if an abnormality is detected. The notification unit can also periodically monitor the blood pressure using a sphygmomanometer and notify a medical institution if an abnormality is detected. This makes it possible to continuously monitor the user's health condition and quickly notify a medical institution if an abnormality is detected. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the notification unit can analyze the user's biometric information using a generation AI and notify a medical institution if an abnormality is detected.

[0087] The collection unit can estimate the user's emotions and adjust the frequency of biometric information collection based on the estimated user emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the collection unit can capture the user's facial expressions using a camera and estimate the emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can record the user's voice using a microphone and estimate the emotions using voice analysis technology. The collection unit can also adjust the frequency of biometric information collection based on the user's emotions. For example, if the user is feeling stressed, the collection unit can increase the collection frequency to obtain more detailed data. If the user is relaxed, the collection unit can reduce the collection frequency to reduce battery consumption. If the user is facing an emergency, the collection unit can maximize the collection frequency to obtain data in real time. This allows the frequency of biometric information collection to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, 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 collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may estimate the user's emotions using the generation AI and adjust the frequency of collecting biometric information based on the emotions.

[0088] The collection unit can analyze the user's past health data and select an appropriate collection method. The collection unit, for example, analyzes the user's past heart rate data. For example, the collection unit can analyze the user's past heart rate data and increase the collection frequency during time periods when abnormalities are common. The collection unit can also concentrate collection during time periods when body temperature spikes based on the user's past body temperature data. The collection unit can also adjust the collection frequency during time periods when blood pressure is unstable based on the user's past blood pressure data. For example, the collection unit can analyze the user's past heart rate data and increase the collection frequency during time periods when abnormalities are common. The collection unit can also concentrate collection during time periods when body temperature spikes based on the user's past body temperature data. The collection unit can also adjust the collection frequency during time periods when blood pressure is unstable based on the user's past blood pressure data. This makes it possible to select an optimal collection method based on the user's past health data. Some or all of the above-described processing by the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can analyze the user's past health data using a generation AI and select an optimal collection method.

[0089] When collecting biometric information, the collection unit can filter the biometric information based on the user's current activity status. For example, when the user is exercising, the collection unit prioritizes collecting data related to the exercise. For example, when the user is exercising, the collection unit prioritizes collecting heart rate and oxygen saturation data. The collection unit can also prioritize collecting resting data when the user is resting. For example, when the user is resting, the collection unit prioritizes collecting body temperature and respiratory rate data. The collection unit can also prioritize collecting data related to stress levels when the user is working. For example, when the user is working, the collection unit prioritizes collecting heart rate and blood pressure data. This allows the biometric information to be collected to be optimized according to the user's activity status. Some or all of the above-described processing by the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can analyze the user's activity status using a generation AI and filter the biometric information to be collected based on the activity status.

[0090] When collecting biometric information, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user is using voice input, the collection unit prioritizes collection of voice data. For example, when the user is using voice input, the collection unit collects voice data using a microphone. The collection unit can also prioritize collection of text data when the user is using text input. For example, when the user is using text input, the collection unit collects text data using a keyboard. The collection unit can also prioritize collection of image data when the user is using image input. For example, when the user is using image input, the collection unit collects image data using a camera. This makes it possible to select the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can analyze the user's input method using a generation AI and select the optimal collection means.

[0091] The collection unit can estimate the user's emotions and determine the priority of the biometric information to be collected based on the estimated user's emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the collection unit can capture the user's facial expressions using a camera and estimate the emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can record the user's voice using a microphone and estimate the emotions using voice analysis technology. The collection unit can also determine the priority of the biometric information to be collected based on the user's emotions. For example, the collection unit can prioritize collecting heart rate and blood pressure data when the user is stressed. The collection unit can prioritize collecting body temperature and respiratory rate data when the user is relaxed. The collection unit can also collect all biometric information equally when the user is facing an emergency. This allows the priority of the biometric information to be collected to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, 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 collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may estimate the user's emotions using the generation AI and determine the priority of the biometric information to be collected based on the emotions.

[0092] When collecting biometric information, the collection unit can prioritize collecting highly relevant information taking into account the user's geographical location information. For example, when the user is at high altitude, the collection unit prioritizes collecting oxygen saturation data. For example, when the user is at high altitude, the collection unit prioritizes collecting oxygen saturation data. The collection unit can also prioritize collecting body temperature data when the user is in a cold region. For example, when the user is in a cold region, the collection unit prioritizes collecting body temperature data. The collection unit can also prioritize collecting stress level data when the user is in an urban area. For example, when the user is in an urban area, the collection unit prioritizes collecting heart rate and blood pressure data. This makes it possible to prioritize collecting highly relevant biometric information based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can analyze the user's geographical location information using a generation AI and prioritize collecting highly relevant biometric information.

[0093] When collecting biometric information, the collection unit can analyze the user's social media activity and collect related information. For example, if the user posts on social media that they are feeling stressed, the collection unit prioritizes collecting heart rate and blood pressure data. For example, if the user posts on social media that they are feeling stressed, the collection unit prioritizes collecting heart rate and blood pressure data. The collection unit can also prioritize collecting body temperature and respiratory rate data if the user posts on social media that they are relaxing. For example, if the user posts on social media that they are relaxing, the collection unit prioritizes collecting body temperature and respiratory rate data. The collection unit can also collect all biometric information equally if the user posts on social media that they are facing an emergency. For example, if the user posts on social media that they are facing an emergency, the collection unit collects all biometric information equally. This makes it possible to collect related biometric information based on the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can analyze the user's social media activity using a generation AI and collect related biometric information.

[0094] When collecting biometric information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, if the user has previously requested heart rate data collection, the collection unit prioritizes collecting heart rate data. For example, if the user has previously requested heart rate data collection, the collection unit prioritizes collecting heart rate data. Furthermore, if the user has previously requested body temperature data collection, the collection unit can also prioritize collecting body temperature data. For example, if the user has previously requested body temperature data collection, the collection unit prioritizes collecting body temperature data. Furthermore, if the user has previously requested blood pressure data collection, the collection unit can also prioritize collecting blood pressure data. For example, if the user has previously requested blood pressure data collection, the collection unit prioritizes collecting blood pressure data. This makes it possible to optimize the collection method based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can customize the collection method by analyzing the user's past feedback using a generation AI.

[0095] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the analysis unit can capture the user's facial expressions using a camera and estimate the emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can record the user's voice using a microphone and estimate the emotions using voice analysis technology. The analysis unit can also adjust the accuracy of the analysis based on the user's emotions. For example, the analysis unit can increase the accuracy of the analysis and provide more detailed data when the user is feeling stressed. The analysis unit can also reduce the accuracy of the analysis to reduce battery consumption when the user is relaxed. The analysis unit can also maximize the accuracy of the analysis and provide data in real time when the user is facing an emergency. This allows the accuracy of the analysis to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, 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 analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may estimate the user's emotions using the generation AI and adjust the accuracy of the analysis based on the emotions.

[0096] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected biometric information. For example, if heart rate data is abnormal, the analysis unit performs a detailed analysis. For example, if heart rate data is abnormal, the analysis unit performs a detailed analysis. The analysis unit can also perform a detailed analysis if body temperature data is abnormal. For example, if body temperature data is abnormal, the analysis unit performs a detailed analysis. The analysis unit can also perform a detailed analysis if blood pressure data is abnormal. For example, if blood pressure data is abnormal, the analysis unit performs a detailed analysis. This makes it possible to optimize the level of detail of the analysis according to the importance of the collected biometric information. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the importance of the collected biometric information using a generation AI, and adjust the level of detail of the analysis based on the importance.

[0097] During analysis, the analysis unit can apply different analysis algorithms according to different biometric information categories. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a body temperature variability analysis algorithm to body temperature data. For example, the analysis unit applies a body temperature variability analysis algorithm to body temperature data. The analysis unit can also apply a blood pressure variability analysis algorithm to blood pressure data. For example, the analysis unit applies a blood pressure variability analysis algorithm to blood pressure data. This makes it possible to apply an optimal analysis algorithm according to different biometric information categories. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze different biometric information categories using a generation AI and apply an optimal analysis algorithm.

[0098] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve the analysis accuracy of current heart rate data by referring to the user's past heart rate analysis results. For example, the analysis unit can improve the analysis accuracy of current heart rate data by referring to the user's past heart rate analysis results. The analysis unit can also improve the analysis accuracy of current body temperature data by referring to the user's past body temperature analysis results. For example, the analysis unit can improve the analysis accuracy of current body temperature data by referring to the user's past body temperature analysis results. The analysis unit can also improve the analysis accuracy of current blood pressure data by referring to the user's past blood pressure analysis results. For example, the analysis unit can improve the analysis accuracy of current blood pressure data by referring to the user's past blood pressure analysis results. This allows the accuracy of analysis to be improved based on the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the user's past analysis results using a generation AI to improve the accuracy of the analysis.

[0099] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the analysis unit can capture the user's facial expressions using a camera and estimate the emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can record the user's voice using a microphone and estimate the emotions using voice analysis technology. The analysis unit can also determine the analysis priority based on the user's emotions. For example, the analysis unit can prioritize analyzing heart rate and blood pressure data if the user is feeling stressed. The analysis unit can also prioritize analyzing body temperature and respiratory rate data if the user is relaxed. The analysis unit can also analyze all biometric information equally if the user is facing an emergency. This allows the analysis priority to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, 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 analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may estimate the user's emotions using the generation AI and determine the priority of analysis based on the emotions.

[0100] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the collected biometric information. The analysis unit, for example, prioritizes analysis of the most recent heart rate data. For example, the analysis unit prioritizes analysis of the most recent heart rate data. The analysis unit can also prioritize analysis of the most recent body temperature data. For example, the analysis unit prioritizes analysis of the most recent body temperature data. The analysis unit can also prioritize analysis of the most recent blood pressure data. For example, the analysis unit prioritizes analysis of the most recent blood pressure data. This makes it possible to optimize the priority of analysis based on the time of submission of the collected biometric information. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the time of submission of the collected biometric information using a generation AI, and determine the priority of analysis based on the time of submission.

[0101] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the collected biometric information. For example, if heart rate data and blood pressure data are related to each other, the analysis unit analyzes them simultaneously. For example, if heart rate data and blood pressure data are related to each other, the analysis unit analyzes them simultaneously. The analysis unit can also analyze body temperature data and respiratory rate data simultaneously if they are related to each other. For example, if body temperature data and respiratory rate data are related to each other, the analysis unit analyzes them simultaneously. The analysis unit can also analyze blood pressure data and body temperature data simultaneously if they are related to each other. For example, if blood pressure data and body temperature data are related to each other, the analysis unit analyzes them simultaneously. This makes it possible to optimize the order of analysis based on the relevance of the collected biometric information. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the relevance of the collected biometric information using a generation AI, and adjust the order of analysis based on the relevance.

[0102] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is a medical professional, the analysis unit provides analysis results that use a lot of technical terms. For example, if the user is a medical professional, the analysis unit provides analysis results that use a lot of technical terms. Furthermore, if the user is a layperson, the analysis unit can also provide analysis results that avoid technical terms. For example, if the user is a layperson, the analysis unit can provide analysis results that avoid technical terms. Furthermore, if the user is a student, the analysis unit can also provide analysis results that include educational commentary. For example, if the user is a student, the analysis unit provides analysis results that include educational commentary. This allows the analysis results to be optimized according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the user's level of expertise using a generation AI and adjust the use of technical terms in the analysis according to the level of expertise.

[0103] The generation unit can estimate the user's emotions and adjust the presentation method of the emergency response information to be generated based on the estimated user's emotions. The generation unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the generation unit can capture the user's facial expression using a camera and estimate the emotion using facial expression recognition technology. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can record the user's voice using a microphone and estimate the emotion using voice analysis technology. The generation unit can also adjust the presentation method of the emergency response information to be generated based on the user's emotions. For example, the generation unit can generate simple, highly visible information when the user is stressed. The generation unit can also generate information that includes detailed information when the user is relaxed. The generation unit can also generate information that focuses on the main points when the user is facing an emergency. This makes it possible to optimize the presentation method of the emergency response information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, 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 generation unit may be performed using, or without, the generation AI. For example, the generation unit may estimate the user's emotions using the generation AI and adjust the way the emergency response information is presented based on the emotions.

[0104] When generating emergency response information, the generation unit can adjust the level of detail of the generation based on the importance of the emergency situation. For example, when a fire occurs, the generation unit generates a detailed evacuation route. For example, when a fire occurs, the generation unit generates a detailed evacuation route. The generation unit can also generate a detailed evacuation route when an earthquake occurs. For example, when an earthquake occurs, the generation unit generates a detailed evacuation route. The generation unit can also generate a detailed evacuation route when a traffic accident occurs. For example, when a traffic accident occurs, the generation unit generates a detailed evacuation route. This makes it possible to optimize the level of detail of the generation according to the importance of the emergency situation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can analyze the importance of the emergency situation using a generation AI, and adjust the level of detail of the generation based on the importance.

[0105] When generating emergency response information, the generation unit can apply different generation algorithms according to different emergency scenarios. For example, in the case of a fire, the generation unit applies a generation algorithm dedicated to fires. For example, in the case of a fire, the generation unit applies a generation algorithm dedicated to fires. Furthermore, in the case of an earthquake, the generation unit can also apply a generation algorithm dedicated to earthquakes. For example, in the case of an earthquake, the generation unit applies a generation algorithm dedicated to earthquakes. Furthermore, in the case of a traffic accident, the generation unit can also apply a generation algorithm dedicated to traffic accidents. For example, in the case of a traffic accident, the generation unit applies a generation algorithm dedicated to traffic accidents. This makes it possible to apply an optimal generation algorithm according to different emergency scenarios. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can analyze different emergency scenarios using a generation AI and apply an optimal generation algorithm.

[0106] When generating emergency response information, the generation unit can improve the accuracy of generation by referring to the user's past emergency response results. For example, the generation unit can improve the accuracy of generation of current fire response information by referring to the user's past fire response results. For example, the generation unit can improve the accuracy of generation of current fire response information by referring to the user's past fire response results. The generation unit can also improve the accuracy of generation of current earthquake response information by referring to the user's past earthquake response results. For example, the generation unit can improve the accuracy of generation of current earthquake response information by referring to the user's past earthquake response results. The generation unit can also improve the accuracy of generation of current traffic accident response information by referring to the user's past traffic accident response results. For example, the generation unit can improve the accuracy of generation of current traffic accident response information by referring to the user's past traffic accident response results. This allows the accuracy of generation to be improved based on the user's past emergency response results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can analyze the user's past emergency response results using a generation AI to improve the accuracy of generation.

[0107] The generation unit can estimate the user's emotions and adjust the length of the emergency response information to be generated based on the estimated user emotions. The generation unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the generation unit can capture the user's facial expression using a camera and estimate the emotion using facial expression recognition technology. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can record the user's voice using a microphone and estimate the emotion using voice analysis technology. The generation unit can also adjust the length of the emergency response information to be generated based on the user's emotions. For example, the generation unit can generate short, concise information when the user is stressed. The generation unit can also generate longer information with detailed explanations when the user is relaxed. The generation unit can also generate information with visually stimulating effects when the user is facing an emergency. This allows the length of the emergency response information to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, 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 generation unit may be performed using, or without, the generation AI. For example, the generation unit may estimate the user's emotions using the generation AI and adjust the length of the emergency response information based on the emotions.

[0108] When generating emergency response information, the generation unit can determine the priority of generation based on the time of occurrence of the emergency. For example, the generation unit prioritizes generating the latest fire information. For example, the generation unit prioritizes generating the latest fire information. The generation unit can also prioritize generating the latest earthquake information. For example, the generation unit prioritizes generating the latest earthquake information. The generation unit can also prioritize generating the latest traffic accident information. For example, the generation unit prioritizes generating the latest traffic accident information. This makes it possible to optimize the priority of generation based on the time of occurrence of the emergency. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can analyze the time of occurrence of the emergency using a generation AI and determine the priority of generation based on the time of occurrence.

[0109] When generating emergency response information, the generation unit can adjust the order of generation based on the relevance of the emergency situation. For example, the generation unit prioritizes generating information related to fires. For example, the generation unit prioritizes generating information related to fires. The generation unit can also prioritize generating information related to earthquakes. For example, the generation unit prioritizes generating information related to earthquakes. The generation unit can also prioritize generating information related to traffic accidents. For example, the generation unit prioritizes generating information related to traffic accidents. This makes it possible to optimize the order of generation based on the relevance of the emergency situation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can analyze the relevance of the emergency situation using a generation AI and adjust the order of generation based on the relevance.

[0110] When generating emergency response information, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user is a medical professional, the generation unit generates information that uses a lot of technical terminology. For example, if the user is a medical professional, the generation unit generates information that uses a lot of technical terminology. Furthermore, if the user is a layperson, the generation unit can also generate information that avoids technical terminology. For example, if the user is a layperson, the generation unit can generate information that avoids technical terminology. Furthermore, if the user is a student, the generation unit can also generate information that includes educational commentary. For example, if the user is a student, the generation unit generates information that includes educational commentary. This makes it possible to optimize the emergency response information according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can analyze the user's level of expertise using a generation AI and adjust the use of technical terminology in the generation according to the level of expertise.

[0111] The instruction unit can estimate the user's emotion and adjust the way in which instructions are expressed based on the estimated user's emotion. The instruction unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the instruction unit can capture the user's facial expression using a camera and estimate the emotion using facial expression recognition technology. The instruction unit can also estimate the user's emotion using voice analysis technology. For example, the instruction unit can record the user's voice using a microphone and estimate the emotion using voice analysis technology. The instruction unit can also adjust the way in which instructions are expressed based on the user's emotion. For example, the instruction unit can provide simple, highly visible instructions when the user is stressed. The instruction unit can also provide detailed instructions when the user is relaxed. The instruction unit can also provide instructions that focus on the main points when the user is facing an emergency. This makes it possible to optimize the way in which instructions are expressed based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, 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 instruction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the instruction unit may estimate the user's emotions using the generation AI and adjust the way instructions are expressed based on the emotions.

[0112] The instruction unit can adjust the level of detail of the instruction based on the importance of the emergency response information when giving an instruction. The instruction unit provides detailed evacuation instructions, for example, when a fire has occurred. For example, the instruction unit provides detailed evacuation instructions when a fire has occurred. The instruction unit can also provide detailed evacuation instructions when an earthquake has occurred. For example, the instruction unit provides detailed evacuation instructions when an earthquake has occurred. The instruction unit can also provide detailed evacuation instructions when a traffic accident has occurred. For example, the instruction unit provides detailed evacuation instructions when a traffic accident has occurred. This makes it possible to optimize the level of detail of the instructions according to the importance of the emergency response information. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the instruction unit can analyze the importance of the emergency response information using a generation AI, and adjust the level of detail of the instructions based on the importance.

[0113] When issuing an instruction, the instruction unit can apply different instruction algorithms according to different emergency scenarios. For example, in the case of a fire, the instruction unit applies a fire-specific instruction algorithm. For example, in the case of a fire, the instruction unit applies a fire-specific instruction algorithm. Furthermore, in the case of an earthquake, the instruction unit can also apply an earthquake-specific instruction algorithm. For example, in the case of an earthquake, the instruction unit applies an earthquake-specific instruction algorithm. Furthermore, in the case of a traffic accident, the instruction unit can also apply a traffic accident-specific instruction algorithm. For example, in the case of a traffic accident, the instruction unit applies a traffic accident-specific instruction algorithm. This makes it possible to apply an optimal instruction algorithm according to different emergency scenarios. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the instruction unit can analyze different emergency scenarios using a generation AI and apply an optimal instruction algorithm.

[0114] When giving an instruction, the instruction unit can improve the accuracy of the instruction by referring to the user's past instruction results. The instruction unit, for example, can improve the accuracy of the current fire response instruction by referring to the user's past fire response results. For example, the instruction unit can improve the accuracy of the current fire response instruction by referring to the user's past fire response results. The instruction unit can also improve the accuracy of the current earthquake response instruction by referring to the user's past earthquake response results. For example, the instruction unit can improve the accuracy of the current earthquake response instruction by referring to the user's past traffic accident response results. For example, the instruction unit can improve the accuracy of the current traffic accident response instruction by referring to the user's past traffic accident response results. In this way, the accuracy of the instruction can be improved based on the user's past instruction results. Some or all of the above-described processing in the instruction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the instruction unit can analyze the user's past instruction results using a generation AI to improve the accuracy of the instruction.

[0115] The instruction unit can estimate the user's emotion and adjust the length of the instruction based on the estimated user's emotion. The instruction unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the instruction unit can capture the user's facial expression using a camera and estimate the emotion using facial expression recognition technology. The instruction unit can also estimate the user's emotion using voice analysis technology. For example, the instruction unit can record the user's voice using a microphone and estimate the emotion using voice analysis technology. The instruction unit can also adjust the length of the instruction based on the user's emotion. For example, the instruction unit can provide short and to-the-point instructions when the user is stressed. The instruction unit can also provide longer instructions with detailed explanations when the user is relaxed. The instruction unit can also provide instructions with visually stimulating effects when the user is facing an emergency. This allows the length of the instruction to be optimized according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, 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 instruction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the instruction unit may estimate the user's emotion using the generation AI and adjust the length of the instruction based on the emotion.

[0116] When giving an instruction, the instruction unit can determine the priority of the instruction based on the time of occurrence of the emergency. For example, the instruction unit gives priority to the latest fire information. For example, the instruction unit gives priority to the latest fire information. The instruction unit can also give priority to the latest earthquake information. For example, the instruction unit gives priority to the latest earthquake information. The instruction unit can also give priority to the latest traffic accident information. For example, the instruction unit gives priority to the latest traffic accident information. This makes it possible to optimize the priority of the instructions based on the time of occurrence of the emergency. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the instruction unit can analyze the time of occurrence of the emergency using a generation AI and determine the priority of the instructions based on the time of occurrence.

[0117] When giving instructions, the instruction unit can adjust the order of instructions based on the relevance of the emergency situation. For example, the instruction unit gives priority to information related to fires. For example, the instruction unit gives priority to information related to fires. The instruction unit can also give priority to information related to earthquakes. For example, the instruction unit gives priority to information related to earthquakes. The instruction unit can also give priority to information related to traffic accidents. For example, the instruction unit gives priority to information related to traffic accidents. This makes it possible to optimize the order of instructions based on the relevance of the emergency situation. Some or all of the above-described processing in the instruction unit may be performed using, or without, a generation AI. For example, the instruction unit can analyze the relevance of the emergency situation using the generation AI and adjust the order of instructions based on the relevance.

[0118] When giving instructions, the instruction unit can adjust the use of technical terms in the instructions according to the user's level of expertise. For example, if the user is a medical professional, the instruction unit provides instructions that use a lot of technical terms. For example, if the user is a medical professional, the instruction unit provides instructions that use a lot of technical terms. Furthermore, if the user is a general public, the instruction unit can also provide instructions that avoid technical terms. For example, if the user is a general public, the instruction unit can also provide instructions that include educational explanations if the user is a student. For example, if the user is a student, the instruction unit provides instructions that include educational explanations. This allows the instructions to be optimized according to the user's level of expertise. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the instruction unit can analyze the user's level of expertise using a generation AI and adjust the use of technical terms in the instructions according to the level of expertise.

[0119] The notification unit can estimate the user's emotion and adjust the notification presentation method based on the estimated user's emotion. The notification unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the notification unit can capture the user's facial expression using a camera and estimate the emotion using facial expression recognition technology. The notification unit can also estimate the user's emotion using voice analysis technology. For example, the notification unit can record the user's voice using a microphone and estimate the emotion using voice analysis technology. The notification unit can also adjust the notification presentation method based on the user's emotion. For example, the notification unit can provide a simple, highly visible notification when the user is feeling stressed. The notification unit can also provide a detailed notification when the user is relaxed. The notification unit can also provide a notification that focuses on the main points when the user is facing an emergency. This makes it possible to optimize the notification presentation method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, 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 notification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the notification unit may estimate the user's emotions using the generation AI and adjust the notification expression method based on the emotions.

[0120] The notification unit can adjust the level of detail of the notification based on the importance of the emergency response information at the time of notification. The notification unit, for example, provides a detailed evacuation notification when a fire occurs. For example, the notification unit provides a detailed evacuation notification when a fire occurs. The notification unit can also provide a detailed evacuation notification when an earthquake occurs. For example, the notification unit provides a detailed evacuation notification when an earthquake occurs. The notification unit can also provide a detailed evacuation notification when a traffic accident occurs. For example, the notification unit provides a detailed evacuation notification when a traffic accident occurs. This makes it possible to optimize the level of detail of the notification according to the importance of the emergency response information. Some or all of the above-mentioned processing in the notification unit may be performed using, or without, a generation AI. For example, the notification unit can analyze the importance of the emergency response information using a generation AI and adjust the level of detail of the notification based on the importance.

[0121] The notification unit can apply different notification algorithms according to different emergency scenarios when making a notification. For example, in the case of a fire, the notification unit applies a notification algorithm dedicated to the fire. For example, in the case of a fire, the notification unit applies a notification algorithm dedicated to the fire. Furthermore, in the case of an earthquake, the notification unit can also apply a notification algorithm dedicated to the earthquake. For example, in the case of an earthquake, the notification unit applies a notification algorithm dedicated to the earthquake. Furthermore, in the case of a traffic accident, the notification unit can also apply a notification algorithm dedicated to the traffic accident. For example, in the case of a traffic accident, the notification unit applies a notification algorithm dedicated to the traffic accident. This makes it possible to apply an optimal notification algorithm according to different emergency scenarios. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the notification unit can analyze different emergency scenarios using a generation AI and apply an optimal notification algorithm.

[0122] The notification unit can improve the accuracy of notifications by referring to the user's past notification results when making notifications. For example, the notification unit can improve the accuracy of a current fire notification by referring to the user's past fire notification results. For example, the notification unit can improve the accuracy of a current fire notification by referring to the user's past fire notification results. The notification unit can also improve the accuracy of a current earthquake notification by referring to the user's past earthquake notification results. For example, the notification unit can improve the accuracy of a current earthquake notification by referring to the user's past earthquake notification results. The notification unit can also improve the accuracy of a current traffic accident notification by referring to the user's past traffic accident notification results. For example, the notification unit can improve the accuracy of a current traffic accident notification by referring to the user's past traffic accident notification results. This allows the accuracy of notifications to be improved based on the user's past notification results. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the notification unit can analyze the user's past notification results using a generation AI to improve the accuracy of notifications.

[0123] The notification unit can estimate the user's emotion and adjust the length of the notification based on the estimated user's emotion. The notification unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the notification unit can capture the user's facial expression using a camera and estimate the emotion using facial expression recognition technology. The notification unit can also estimate the user's emotion using voice analysis technology. For example, the notification unit can record the user's voice using a microphone and estimate the emotion using voice analysis technology. The notification unit can also adjust the length of the notification based on the user's emotion. For example, the notification unit can provide a short and to-the-point notification when the user is feeling stressed. The notification unit can also provide a longer notification with a detailed explanation when the user is relaxed. The notification unit can also provide a notification with a visually stimulating effect when the user is facing an emergency. This allows the length of the notification to be optimized according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, 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 notification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the notification unit may estimate the user's emotion using the generation AI and adjust the length of the notification based on the emotion.

[0124] At the time of notification, the notification unit can determine the priority of notifications based on the time of occurrence of an emergency. The notification unit, for example, prioritizes notification of the latest fire information. For example, the notification unit prioritizes notification of the latest fire information. The notification unit can also prioritize notification of the latest earthquake information. For example, the notification unit prioritizes notification of the latest earthquake information. The notification unit can also prioritize notification of the latest traffic accident information. For example, the notification unit prioritizes notification of the latest traffic accident information. This makes it possible to optimize the priority of notifications based on the time of occurrence of an emergency. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the notification unit can analyze the time of occurrence of an emergency using a generation AI and determine the priority of notifications based on the time of occurrence.

[0125] The notification unit can adjust the order of notifications based on the relevance of the emergency at the time of notification. The notification unit, for example, prioritizes notifying information related to fires. For example, the notification unit prioritizes notifying information related to fires. The notification unit can also prioritize notifying information related to earthquakes. For example, the notification unit prioritizes notifying information related to earthquakes. The notification unit can also prioritize notifying information related to traffic accidents. For example, the notification unit prioritizes notifying information related to traffic accidents. This makes it possible to optimize the order of notifications based on the relevance of the emergency. Some or all of the above-described processing in the notification unit may be performed using, or without, a generation AI. For example, the notification unit can analyze the relevance of the emergency using a generation AI and adjust the order of notifications based on the relevance.

[0126] The notification unit can adjust the use of technical terms in the notification according to the user's level of expertise when providing a notification. For example, if the user is a medical professional, the notification unit provides a notification that uses a lot of technical terms. For example, if the user is a medical professional, the notification unit provides a notification that uses a lot of technical terms. The notification unit can also provide a notification that avoids technical terms if the user is a general public. For example, if the user is a general public, the notification unit can provide a notification that avoids technical terms. The notification unit can also provide a notification that includes educational commentary if the user is a student. For example, if the user is a student, the notification unit provides a notification that includes educational commentary. This allows the notification to be optimized according to the user's level of expertise. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the notification unit can analyze the user's level of expertise using a generation AI and adjust the use of technical terms in the notification according to the level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, instruction unit, and notification unit described above is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect biometric information of the user using a heart rate sensor, thermometer, or blood pressure monitor of the smart device 14. The analysis unit analyzes the collected biometric information by the specific processing unit 290 of the data processing device 12 and detects abnormalities. The generation unit generates emergency response information by the specific processing unit 290 of the data processing device 12. The instruction unit provides voice instructions or text instructions to the user using the control unit 46A of the smart device 14. The notification unit tracks the user's location information using GPS or Wi-Fi location information of the smart device 14 and notifies emergency services. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, generation unit, instruction unit, and notification unit described above is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect biometric information of the user using a heart rate sensor, thermometer, or blood pressure monitor of the smart glasses 214. The analysis unit analyzes the collected biometric information by the specific processing unit 290 of the data processing device 12 and detects abnormalities. The generation unit generates emergency response information by the specific processing unit 290 of the data processing device 12. The instruction unit provides voice instructions or text instructions to the user using the control unit 46A of the smart glasses 214. The notification unit tracks the user's location information using GPS or Wi-Fi location information of the smart glasses 214 and notifies emergency services. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, instruction unit, and notification unit described above is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect biometric information of the user using a heart rate sensor, thermometer, or blood pressure monitor of the headset type terminal 314. The analysis unit analyzes the biometric information collected by the specific processing unit 290 of the data processing device 12 and detects abnormalities. The generation unit generates emergency response information using the specific processing unit 290 of the data processing device 12. The instruction unit provides voice instructions or text instructions to the user using the control unit 46A of the headset type terminal 314. The notification unit tracks the user's location information using GPS or Wi-Fi location information of the headset type terminal 314 and notifies emergency services. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, instruction unit, and notification unit described above is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect biometric information of the user using a heart rate sensor, thermometer, or blood pressure monitor of the robot 414. The analysis unit analyzes the biometric information collected by the specific processing unit 290 of the data processing device 12 and detects abnormalities. The generation unit generates emergency response information using the specific processing unit 290 of the data processing device 12. The instruction unit provides voice instructions or text instructions to the user using the control unit 46A of the robot 414. The notification unit tracks the user's location information using GPS or Wi-Fi location information of the robot 414 and notifies emergency services.

[0127] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0128] The emergency support system may further include a history analysis unit that analyzes the user's past emergency response history. The history analysis unit records and analyzes what kind of emergencies the user has faced in the past and the responses they have taken. For example, the history analysis unit may record the evacuation routes and actions taken when the user has previously encountered a fire, and suggest the optimal evacuation route the next time a fire occurs. The history analysis unit may also record the evacuation actions taken when the user has previously encountered an earthquake, and instruct the user on appropriate actions the next time an earthquake occurs. Furthermore, the history analysis unit may record the responses taken when the user has previously encountered a traffic accident, and support a prompt response the next time a traffic accident occurs. This makes it possible to provide a more effective emergency response based on the user's past emergency response history.

[0129] The emergency support system may further include an emotion prioritization unit that estimates the user's emotion and determines the priority of emergency response information based on the estimated emotion. The emotion prioritization unit uses facial expression recognition technology and voice analysis technology to estimate the user's emotion. For example, if the user is feeling stressed, the emotion prioritization unit may provide particularly important emergency response information with priority. The emotion prioritization unit may also provide detailed information if the user is relaxed. Furthermore, if the user is facing an emergency, the emotion prioritization unit may quickly provide information that is concise to the point. This makes it possible to optimize the priority of emergency response information according to the user's emotion.

[0130] The emergency support system may further include an activity detection unit that detects the user's current activity status and customizes emergency response information based on the activity status. The activity detection unit detects different situations, such as whether the user is exercising, resting, or working. For example, if the user is exercising, the activity detection unit may preferentially provide emergency response information related to exercise. If the user is resting, the activity detection unit may also provide emergency response information suitable for resting. Furthermore, if the user is working, the activity detection unit may also provide emergency response information related to stress levels. This makes it possible to provide optimal emergency response information according to the user's activity status.

[0131] The emergency support system may further include a social media analysis unit that analyzes a user's social media activities and provides relevant emergency response information. The social media analysis unit analyzes content posted by the user on social media and extracts information related to an emergency. For example, if the user posts about feeling stressed, the social media analysis unit may prioritize collecting heart rate and blood pressure data and provide appropriate emergency response information. Alternatively, if the user posts about feeling relaxed, the social media analysis unit may prioritize collecting body temperature and respiratory rate data and provide appropriate emergency response information. Furthermore, if the user posts about facing an emergency, the social media analysis unit may equally collect all biometric information to support a rapid response. This makes it possible to provide relevant emergency response information based on the user's social media activities.

[0132] The emergency support system may further include a feedback reflecting unit that customizes the emergency response information by reflecting the user's past feedback. The feedback reflecting unit analyzes the feedback provided by the user in the past and optimizes the method of providing the emergency response information. For example, if the user previously requested collection of heart rate data, the feedback reflecting unit may prioritize collection of heart rate data and provide appropriate emergency response information. Furthermore, if the user previously requested collection of body temperature data, the feedback reflecting unit may prioritize collection of body temperature data and provide appropriate emergency response information. Furthermore, if the user previously requested collection of blood pressure data, the feedback reflecting unit may prioritize collection of blood pressure data and provide appropriate emergency response information. This makes it possible to optimize the emergency response information based on the user's past feedback.

[0133] The emergency support system may further include an emotional expression adjustment unit that estimates the user's emotions and adjusts the manner in which the emergency response information is presented based on the estimated emotions. The emotional expression adjustment unit uses facial expression recognition technology and voice analysis technology to estimate the user's emotions. For example, if the user is feeling stressed, the emotional expression adjustment unit may provide simple, highly visible information. If the user is relaxed, the emotional expression adjustment unit may also provide detailed information. Furthermore, if the user is facing an emergency, the emotional expression adjustment unit may also provide information that focuses on the main points. This allows the manner in which the emergency response information is presented to be optimized according to the user's emotions.

[0134] The emergency support system may further include a geographic information analysis unit that provides emergency response information taking into account the user's geographic location information. The geographic information analysis unit customizes the emergency response information based on the characteristics of the user's current location. For example, if the user is in a high altitude, the geographic information analysis unit may prioritize collecting oxygen saturation data and provide appropriate emergency response information. If the user is in a cold region, the geographic information analysis unit may prioritize collecting body temperature data and provide appropriate emergency response information. Furthermore, if the user is in an urban area, the geographic information analysis unit may prioritize collecting stress level data and provide appropriate emergency response information. This allows optimal emergency response information to be provided based on the user's geographic location information.

[0135] The emergency support system may further include an emotion length adjustment unit that estimates the user's emotion and adjusts the length of the emergency response information based on the estimated emotion. The emotion length adjustment unit uses facial expression recognition technology or voice analysis technology to estimate the user's emotion. For example, the emotion length adjustment unit may provide short, concise information when the user is stressed. The emotion length adjustment unit may also provide longer information with detailed explanations when the user is relaxed. Furthermore, the emotion length adjustment unit may provide information with visually stimulating effects when the user is facing an emergency. This allows the length of the emergency response information to be optimized according to the user's emotion.

[0136] The emergency support system may further include an expertise adjustment unit that provides emergency response information according to the user's level of expertise. The expertise adjustment unit determines whether the user is a medical professional, a layperson, or a student, and provides information according to the determination. For example, if the user is a medical professional, the expertise adjustment unit may provide information that uses a lot of technical terms. If the user is a layperson, the expertise adjustment unit may also provide information that avoids technical terms. Furthermore, if the user is a student, the expertise adjustment unit may also provide information that includes educational commentary. This makes it possible to provide optimal emergency response information according to the user's level of expertise.

[0137] The emergency support system may further include an emotional expression adjustment unit that estimates the user's emotions and adjusts the manner in which the emergency response information is presented based on the estimated emotions. The emotional expression adjustment unit uses facial expression recognition technology and voice analysis technology to estimate the user's emotions. For example, if the user is feeling stressed, the emotional expression adjustment unit may provide simple, highly visible information. If the user is relaxed, the emotional expression adjustment unit may also provide detailed information. Furthermore, if the user is facing an emergency, the emotional expression adjustment unit may also provide information that focuses on the main points. This allows the manner in which the emergency response information is presented to be optimized according to the user's emotions.

[0138] The processing flow of the second embodiment will be briefly explained below.

[0139] Step 1: The collection unit collects the user's biometric information. The user's biometric information includes heart rate, body temperature, blood pressure, etc. The collection unit measures this information in real time or periodically using a heart rate sensor, a thermometer, and a blood pressure monitor. Step 2: The analysis unit analyzes the vital signs collected by the collection unit. The analysis is performed using an anomaly detection algorithm to detect abnormalities in heart rate, body temperature, and blood pressure. Step 3: The generating unit generates emergency response information based on the information analyzed by the analyzing unit. The generated information includes information for responding to emergencies such as fires, earthquakes, and traffic accidents. Step 4: The instruction unit issues instructions to the user based on the information generated by the generation unit. The instructions are given as voice instructions or text instructions, and provide the user with the optimal evacuation route, etc. Step 5: The notification module tracks the user's location and notifies emergency services using GPS and Wi-Fi location.

[0140] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0141] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

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

[0144] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0145] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0146] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0161] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0162] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0167] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0177] 7, a 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.

[0178] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0179] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0180] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0181] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0182] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0183] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0184] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0185] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0186] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0187] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0188] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0189] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0190] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0191] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0192] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0193] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0194] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0195] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0196] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0197] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0198] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0199] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0200] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0201] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0202] 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.

[0203] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0204] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0205] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0206] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0207] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0208] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0209] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0210] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0211] [Explanation of symbols]

[0212] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects biometric information of a user; an analysis unit that analyzes the information collected by the collection unit; a generation unit that generates emergency response information based on the information analyzed by the analysis unit; an instruction unit that issues instructions to a user based on the information generated by the generation unit; a notification unit that tracks the user's location information and notifies emergency services. A system characterized by:

2. The collecting unit Collect vital signs, including heart rate, temperature, and blood pressure The system of claim 1 .

3. The analysis unit Analyzing collected biometric information and detecting abnormalities The system of claim 1 .

4. The generation unit Generate information to respond to emergency scenarios The system of claim 1 .

5. The instruction unit Providing appropriate instructions to the user based on the generated information The system of claim 1 .

6. The notification unit Track your location and notify emergency services The system of claim 1 .

7. The generation unit Generate information to respond to emergency scenarios, including fires, earthquakes, and traffic accidents The system of claim 1 .

8. The instruction unit Combines navigation and voice guidance to guide users to safe shelters The system of claim 1 .

Images