System

Abstract

An object of a system according to an embodiment is to analyze biological information of a user in an emergency and provide an appropriate emergency measure.SOLUTION: A system includes an acquisition unit, an analysis unit, a generation unit, a provision unit, and a navigation unit. The acquisition unit acquires biological information. The analysis unit analyzes the biological information acquired by the acquisition unit. The generation unit generates emergency means based on the data analyzed by the analysis unit. The providing unit provides the information generated by the generating unit. The navigation unit performs navigation based on the information provided by the providing unit.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 that it is difficult to quickly and accurately analyze a user's biometric information in an emergency and provide appropriate first aid measures.

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

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a generation unit, a provision unit, and a navigation unit. The acquisition unit acquires biometric information. The analysis unit analyzes the biometric information acquired by the acquisition unit. The generation unit generates emergency measures based on the data analyzed by the analysis unit. The provision unit provides the information generated by the generation unit. The navigation unit performs navigation based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the biometric information of the user in an emergency and provide appropriate first aid 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) A system according to an embodiment of the present invention uses a generative AI embedded in AR glasses to provide user support in emergencies and monitor the user's biometric information. This system monitors the user's biometric information in real time and generates optimal emergency measures if an abnormality is detected. Furthermore, the generative AI generates information corresponding to various emergency scenarios and guides the user to a safe evacuation shelter by combining navigation via the AR glasses and voice guidance. This allows the system to monitor the user's biometric information in real time and provide prompt and appropriate emergency measures if an abnormality is detected. For example, if a sudden increase in heart rate occurs, the generative AI determines the possibility of a heart attack and suggests appropriate first aid measures. Furthermore, in the event of a disaster such as a fire or earthquake, the generative AI calculates the optimal evacuation route and displays it on the AR glasses' display. This allows the user to evacuate quickly and safely.

[0029] An emergency response system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, a provision unit, and a navigation unit. The acquisition unit acquires biometric information. For example, the acquisition unit acquires biometric information such as heart rate, body temperature, and blood pressure using a sensor. The acquisition unit can also collect this data in real time. The analysis unit analyzes the biometric information acquired by the acquisition unit. For example, the analysis unit analyzes heart rate fluctuations to detect abnormalities. The analysis unit can also analyze body temperature and blood pressure data to detect abnormalities. The generation unit generates first aid measures based on the data analyzed by the analysis unit. For example, if the heart rate suddenly increases, the generation unit determines the possibility of a heart attack and generates appropriate first aid measures. If the body temperature is abnormally high, the generation unit can also determine the possibility of heatstroke and generate appropriate countermeasures. The provision unit provides the information generated by the generation unit. For example, the provision unit notifies the user of the generated first aid measures. The provision unit can also display the generated information on the display of the AR glasses. The navigation unit performs navigation based on the information provided by the provision unit. For example, the navigation unit calculates the optimal evacuation route using a generation AI and displays it on the display of the AR glasses. The navigation unit can also guide the user to a safe evacuation shelter using audio guidance. As a result, the emergency response system according to the embodiment can monitor the user's biometric information in real time, and if an abnormality is detected, provide prompt and appropriate emergency measures and perform navigation.

[0030] The acquisition unit can acquire at least one of biometric information from heart rate, body temperature, and blood pressure. For example, the acquisition unit measures heart rate using a sensor and collects data. The acquisition unit can also measure body temperature and collect data. Furthermore, the acquisition unit can measure blood pressure and collect data. In this way, abnormalities can be detected by acquiring biometric information such as the user's heart rate, body temperature, and blood pressure. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input heart rate data to the generation AI and cause the generation AI to analyze the heart rate.

[0031] The analysis unit can analyze the acquired biometric information and detect abnormalities. The analysis unit, for example, analyzes fluctuations in heart rate and detects abnormalities. For example, the analysis unit detects an abnormality if the heart rate suddenly increases. The analysis unit can also analyze fluctuations in body temperature and detect abnormalities. For example, the analysis unit detects an abnormality if the body temperature is abnormally high. The analysis unit can also analyze fluctuations in blood pressure and detect abnormalities. For example, the analysis unit detects an abnormality if the blood pressure is abnormally low. In this way, by analyzing the acquired biometric information and detecting abnormalities, appropriate emergency measures can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the acquired biometric information to a generation AI and have the generation AI detect abnormalities.

[0032] The generation unit can generate appropriate first aid measures when an abnormality is detected. For example, if the heart rate suddenly increases, the generation unit determines the possibility of a heart attack and generates appropriate first aid measures. For example, the generation unit suggests how to perform cardiac massage. The generation unit can also determine the possibility of heat stroke when the body temperature is abnormally high and generate appropriate countermeasures. For example, the generation unit suggests how to hydrate. The generation unit can also determine the possibility of hypotension when the blood pressure is abnormally low and generate appropriate countermeasures. For example, the generation unit suggests how to lie down and elevate the legs. This enables a prompt and appropriate response by generating optimal first aid measures when an abnormality is detected. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input biometric information in which an abnormality is detected into the generation AI and have the generation AI generate first aid measures.

[0033] The providing unit can provide the generated emergency measures to the user. The providing unit, for example, notifies the user of the generated emergency measures. For example, the providing unit displays the emergency measures on a display of the AR glasses. The providing unit can also communicate the generated emergency measures to the user by voice. For example, the providing unit instructs the user by voice, "Please perform cardiac massage." The providing unit can also notify the generated emergency measures to a smartphone. For example, the providing unit displays the emergency measures on a smartphone app. By providing the generated emergency measures to the user, the user can perform appropriate first aid. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated emergency measures to a generation AI and cause the generation AI to notify the user.

[0034] The navigation unit can use the generation AI to calculate an appropriate evacuation route and display it on the display of the AR glasses. For example, in the event of a fire, the generation AI calculates a route with less smoke and displays it as an arrow on the display of the AR glasses. In addition, in the event of an earthquake, the navigation unit can use the generation AI to calculate a route with a low risk of collapse and display it on the display of the AR glasses. In addition, in the event of a flood, the navigation unit can use the generation AI to calculate a route with a low risk of flood damage and display it on the display of the AR glasses. In this way, the generation AI can calculate an optimal evacuation route and display it on the display of the AR glasses, thereby guiding the user to a safe evacuation shelter. Some or all of the above-described processing in the navigation unit may be performed using, or without, the generation AI. For example, the navigation unit can have the generation AI calculate an evacuation route and display the calculation results on the display of the AR glasses.

[0035] The navigation unit can guide the user to a safe evacuation shelter using audio guidance. For example, in the event of a fire, the navigation unit can provide audio guidance such as "Turn right" based on the evacuation route calculated by the generation AI. Furthermore, in the event of an earthquake, the navigation unit can also provide audio guidance such as "Move to a safe location." Furthermore, in the event of a flood, the navigation unit can also provide audio guidance such as "Evacuate to higher ground." Thus, by guiding the user to a safe evacuation shelter using audio guidance, the user can be safely evacuated using both visual and auditory senses. Some or all of the above-described processing in the navigation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the navigation unit can cause the generation AI to generate an audio guidance and provide audio instructions to the user.

[0036] The acquisition unit can analyze the user's past biometric information history and select the optimal acquisition method. For example, the acquisition unit can analyze the user's past heart rate data and acquire data focusing on time periods when abnormalities are likely to occur. The acquisition unit can also analyze the user's past body temperature fluctuation patterns and acquire data during seasons or time periods when abnormalities are likely to occur. Furthermore, the acquisition unit can analyze the user's past blood pressure data and acquire data in situations when abnormalities are likely to occur (such as during stress or after exercise). This allows the user's past biometric information history to be analyzed to select the optimal acquisition method and detect abnormalities early. Some or all of the above-described processing in the acquisition unit can be performed using, for example, a generation AI. For example, the acquisition unit can input the user's past biometric information data into the generation AI and have the generation AI select the optimal acquisition method.

[0037] When acquiring biometric information, the acquisition unit can perform filtering based on the user's current activity status and environment. For example, when the user is exercising, the acquisition unit can filter out temporary increases in heart rate due to exercise and not determine them as abnormal. Furthermore, when the user is relaxed, the acquisition unit can filter out minute fluctuations in heart rate and body temperature and not determine them as abnormal. Furthermore, when the user is feeling stressed, the acquisition unit can filter out temporary increases in blood pressure due to stress and not determine them as abnormal. Thus, by performing filtering based on the user's current activity status and environment, false detections can be prevented and accurate biometric information can be acquired. Some or all of the above-described processing in the acquisition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the acquisition unit can input the user's activity status data into the generation AI and have the generation AI perform filtering.

[0038] When acquiring biometric information, the acquisition unit can select the optimal acquisition means according to the user's input method. For example, if the user vocally instructs, "Measure my heart rate," the acquisition unit acquires the heart rate based on the voice input. Furthermore, if the user inputs, "Measure my body temperature" as text, the acquisition unit can acquire the body temperature based on the text input. Furthermore, if the user performs a specific gesture, the acquisition unit can acquire the blood pressure based on the gesture. This allows for selecting the optimal acquisition means according to the user's input method, thereby improving user convenience. Some or all of the above-described processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the user's input data into the generation AI and have the generation AI select the optimal acquisition means.

[0039] When acquiring biometric information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, if the user is at high altitude, the acquisition unit can prioritize acquiring oxygen saturation and check for any abnormalities. Furthermore, if the user is in a cold region, the acquisition unit can prioritize acquiring body temperature and check for the risk of hypothermia. Furthermore, if the user is in an urban area, the acquisition unit can prioritize acquiring heart rate and blood pressure and check for the risk of stress or overwork. Thus, by prioritizing the acquisition of highly relevant information by taking into account the user's geographical location information, more appropriate information can be acquired. Some or all of the above-described processing in the acquisition unit may be performed, for example, using or without the generation AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant information.

[0040] When acquiring biometric information, the acquisition unit can analyze the user's social media activity and acquire related information. For example, if the user posts on social media that they are feeling stressed, the acquisition unit can prioritize acquiring their heart rate and blood pressure. Furthermore, if the user posts on social media that they are exercising, the acquisition unit can prioritize acquiring their heart rate and oxygen saturation. Furthermore, if the user posts on social media that they are feeling unwell, the acquisition unit can prioritize acquiring their body temperature and respiratory rate. This allows for the acquisition of related information and early detection of abnormalities by analyzing the user's social media activity. Some or all of the above-described processing in the acquisition unit can be performed, for example, using or without the generation AI. For example, the acquisition unit can input the user's social media data into the generation AI and cause the generation AI to acquire related information.

[0041] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring biometric information. For example, if the user has previously provided feedback to increase the frequency of heart rate acquisition, the acquisition unit can increase the frequency of heart rate acquisition. The acquisition unit can also change the body temperature acquisition method if the user has previously provided feedback to change the body temperature acquisition method. Furthermore, the acquisition unit can also adjust the timing of blood pressure acquisition if the user has previously provided feedback to adjust the timing of blood pressure acquisition. This allows the acquisition method to be customized by reflecting the user's past feedback, and optimal information can be acquired for the user. Some or all of the above-described processing in the acquisition unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the biometric information. For example, if the heart rate is abnormally high, the analysis unit performs a detailed analysis to identify the cause of the abnormality. Furthermore, if the body temperature is within the normal range, the analysis unit can perform a simplified analysis to confirm that there is no abnormality. Furthermore, if the blood pressure is abnormally low, the analysis unit can perform a detailed analysis to identify the cause of the abnormality. Thus, by adjusting the level of detail of the analysis based on the importance of the biometric information, important information can be analyzed in detail. 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 input biometric information data with high importance to the generation AI and have the generation AI perform a detailed analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of biometric information during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to analyze heart rate. The analysis unit can also apply a body temperature variability analysis algorithm to analyze body temperature. The analysis unit can also apply a blood pressure variability analysis algorithm to analyze blood pressure. By applying different analysis algorithms depending on the category of biometric information, more accurate analysis results can be obtained. Some or all of the above-mentioned 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 biometric information data for each category into the generation AI and have the generation AI apply an appropriate analysis algorithm.

[0044] 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 accuracy of the current heart rate analysis by referring to the user's past heart rate analysis results. The analysis unit can also improve the accuracy of the current body temperature analysis by referring to the user's past body temperature analysis results. Furthermore, the analysis unit can also improve the accuracy of the current blood pressure analysis by referring to the user's past blood pressure analysis results. In this way, the accuracy of the current analysis can be improved by referring to the user's past analysis results. 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 input the user's past analysis data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time when the biological information was acquired. For example, the analysis unit can prioritize analyzing recently acquired heart rate data to check for any abnormalities. The analysis unit can also prioritize analyzing recently acquired body temperature data to check for any abnormalities. Furthermore, the analysis unit can prioritize analyzing recently acquired blood pressure data to check for any abnormalities. In this way, by determining the priority of analysis based on the time when the biological information was acquired, it is possible to prioritize analyzing the most recent information. 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 input data on the time of acquisition into the generation AI and have the generation AI determine the priority.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the biometric information. For example, the analysis unit may prioritize the analysis of the heart rate, taking into account the relevance between the heart rate and blood pressure. The analysis unit may also prioritize the analysis of the body temperature, taking into account the relevance between the body temperature and respiratory rate. Furthermore, the analysis unit may prioritize the analysis of the blood pressure, taking into account the relevance between the blood pressure and oxygen saturation. In this way, by adjusting the order of analysis based on the relevance of the biometric information, highly relevant information can be analyzed preferentially. 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 the generation AI. For example, the analysis unit may input highly relevant data into the generation AI and have the generation AI adjust the order of analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is a medical professional, the analysis unit can provide the analysis results using detailed technical terminology. Furthermore, if the user is a layperson, the analysis unit can also provide the analysis results in easy-to-understand language. Furthermore, if the user has medical knowledge, the analysis unit can also provide the analysis results using appropriate technical terminology. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, the analysis results can be provided in a format that is easy for the user to understand. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terminology.

[0048] The generation unit can adjust the level of detail of the first aid measures to be generated based on the severity of the abnormality during generation. For example, the generation unit generates detailed first aid measures when the heart rate is abnormally high. The generation unit can also generate simplified first aid measures when the body temperature is within the normal range. Furthermore, the generation unit can generate detailed first aid measures when the blood pressure is abnormally low. In this way, by adjusting the level of detail of the first aid measures based on the severity of the abnormality, detailed first aid measures can be provided for serious abnormalities. 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 input abnormality severity data into the generation AI and cause the generation AI to adjust the level of detail.

[0049] The generation unit can apply different generation algorithms depending on the category of abnormality during generation. For example, for an abnormal heart rate, the generation unit applies an algorithm that generates a first aid method that takes into account the possibility of a heart attack. For an abnormal body temperature, the generation unit can also apply an algorithm that generates a first aid method that takes into account the possibility of fever or hypothermia. For an abnormal blood pressure, the generation unit can also apply an algorithm that generates a first aid method that takes into account the possibility of high blood pressure or low blood pressure. In this way, by applying different generation algorithms depending on the category of abnormality, more appropriate first aid methods can be provided. 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 input abnormality category data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0050] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit can improve the accuracy of generation for a current abnormal heart rate by referring to the generation results for the user's past abnormal heart rate. The generation unit can also improve the accuracy of generation for a current abnormal body temperature by referring to the generation results for the user's past abnormal body temperature. Furthermore, the generation unit can also improve the accuracy of generation for a current abnormal blood pressure by referring to the generation results for the user's past abnormal blood pressure. In this way, by referring to the user's past generation results, the accuracy of current generation can be improved. 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 input the user's past generation data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0051] At the time of generation, the generation unit can determine the priority of the emergency measures to be generated based on the time of occurrence of the abnormality. For example, the generation unit can prioritize analyzing a recently occurring heart rate abnormality and generate emergency measures. The generation unit can also prioritize analyzing a recently occurring body temperature abnormality and generate emergency measures. Furthermore, the generation unit can prioritize analyzing a recently occurring blood pressure abnormality and generate emergency measures. In this way, by determining the priority of emergency measures based on the time of occurrence of the abnormality, it is possible to respond quickly to the latest abnormality. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the time of occurrence of the abnormality to the generation AI and have the generation AI determine the priority.

[0052] The generation unit can adjust the order of emergency measures to be generated based on the correlation of abnormalities during generation. For example, the generation unit may consider the correlation between heart rate and blood pressure and generate emergency measures for abnormal heart rate with priority. The generation unit may also consider the correlation between body temperature and respiratory rate and generate emergency measures for abnormal body temperature with priority. Furthermore, the generation unit may consider the correlation between blood pressure and oxygen saturation and generate emergency measures for abnormal blood pressure with priority. By adjusting the order of emergency measures based on the correlation of abnormalities, it is possible to prioritize responses to abnormalities with high correlation. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input abnormality correlation data into the generation AI and cause the generation AI to adjust the order.

[0053] The generation unit can adjust the use of technical terminology in the emergency procedures to be generated according to the user's level of expertise during generation. For example, if the user is a medical professional, the generation unit can generate emergency procedures using detailed technical terminology. Furthermore, if the user is a layperson, the generation unit can also generate emergency procedures using easy-to-understand language. Furthermore, if the user has medical knowledge, the generation unit can generate emergency procedures using appropriate technical terminology. This allows emergency procedures to be provided in a format that is easy for the user to understand by adjusting the use of technical terminology in the emergency procedures according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's level of expertise into the generation AI and have the generation AI use technical terminology.

[0054] The providing unit can adjust the level of detail of the information to be provided based on the importance of the information when providing the information. For example, in the case of important information, the providing unit provides information including a detailed explanation. In addition, in the case of information with low importance, the providing unit can also provide simplified information. Furthermore, in the case of information with high urgency, the providing unit can also provide information that focuses on the main points so that it can be understood quickly. In this way, by adjusting the level of detail of the information based on the importance of the information, it is possible to provide important information in detail. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail.

[0055] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, in the case of medical information, the providing unit can provide the information using detailed technical terms. In addition, in the case of general information, the providing unit can also provide information in easy-to-understand language. Furthermore, in the case of emergency information, the providing unit can also provide information that is brief so that it can be quickly understood. In this way, by applying different providing algorithms depending on the category of information, it is possible to provide information in a more appropriate format. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input information category data into the generation AI and cause the generation AI to apply an appropriate providing algorithm.

[0056] The providing unit can improve the accuracy of information provision by referring to the user's past information provision results when providing information. For example, the providing unit can improve the accuracy of current information provision by referring to the user's past information provision results. The providing unit can also improve the method of information provision by referring to the user's past feedback. Furthermore, the providing unit can also select the optimal information provision method by referring to the user's past usage history. In this way, the accuracy of current information provision can be improved by referring to the user's past information provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's past provided data into the generation AI and cause the generation AI to improve the accuracy of information provision.

[0057] The providing unit can determine the priority of information to be provided based on the time when the information was acquired at the time of provision. For example, the providing unit can prioritize providing important information that was recently acquired. The providing unit can also prioritize providing general information that was recently acquired. Furthermore, the providing unit can also prioritize providing information with high urgency. In this way, by determining the priority of information based on the time when the information was acquired, it is possible to provide the latest information with priority. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input information acquisition time data to the generation AI and have the generation AI determine the priority.

[0058] The providing unit can adjust the order of information to be provided based on the relevance of the information when providing the information. For example, the providing unit can provide important information first, followed by general information. The providing unit can also provide urgent information first, followed by important information. Furthermore, the providing unit can also provide highly relevant information first, followed by general information. In this way, by adjusting the order of information based on the relevance of the information, highly relevant information can be provided first. Some or all of the above-described processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input relevance data of the information to the generation AI and cause the generation AI to adjust the order.

[0059] The providing unit can adjust the use of technical terminology in the information to be provided according to the user's level of expertise. For example, if the user is a medical professional, the providing unit can provide the information using detailed technical terminology. Furthermore, if the user is a layperson, the providing unit can also provide the information in easy-to-understand language. Furthermore, if the user has medical knowledge, the providing unit can also provide the information using appropriate technical terminology. This allows the information to be provided in a format that is easy for the user to understand by adjusting the use of technical terminology in the information according to the user's level of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's level of expertise into the generation AI and have the generation AI execute the use of technical terminology.

[0060] During navigation, the navigation unit can adjust the level of detail of the navigation based on the importance of the evacuation route. For example, the navigation unit provides detailed navigation information for an important evacuation route. The navigation unit can also provide simplified navigation information for a general evacuation route. Furthermore, for a highly urgent evacuation route, the navigation unit can provide navigation information that focuses on the main points so that it can be quickly understood. In this way, by adjusting the level of detail of the navigation based on the importance of the evacuation route, detailed navigation information can be provided for an important evacuation route. Some or all of the above-mentioned processing in the navigation unit may be performed using, or without, a generation AI. For example, the navigation unit can input importance data of the evacuation route to the generation AI and have the generation AI adjust the level of detail.

[0061] During navigation, the navigation unit can apply different navigation algorithms depending on the category of the evacuation route. For example, the navigation unit can apply a navigation algorithm that prioritizes routes with less smoke to evacuation routes in the event of a fire. The navigation unit can also apply a navigation algorithm that prioritizes routes with a low risk of collapse to evacuation routes in the event of an earthquake. Furthermore, the navigation unit can also apply a navigation algorithm that prioritizes routes with a low risk of flooding to evacuation routes in the event of a flood. In this way, by applying different navigation algorithms depending on the category of the evacuation route, it is possible to provide a more appropriate evacuation route. Some or all of the above-mentioned processing in the navigation unit may be performed using, or without, a generation AI. For example, the navigation unit can input evacuation route category data into the generation AI and cause the generation AI to apply an appropriate navigation algorithm.

[0062] The navigation unit can improve the accuracy of navigation during navigation by referring to the user's past navigation results. For example, the navigation unit can improve the accuracy of navigation during a current fire by referring to the user's past navigation results during a fire. The navigation unit can also improve the accuracy of navigation during a current earthquake by referring to the user's past navigation results during an earthquake. Furthermore, the navigation unit can improve the accuracy of navigation during a current flood by referring to the user's past navigation results during a flood. In this way, the accuracy of current navigation can be improved by referring to the user's past navigation results. Some or all of the above-described processing in the navigation unit may be performed using, or without, a generation AI. For example, the navigation unit can input the user's past navigation data into the generation AI and have the generation AI improve the accuracy of navigation.

[0063] During navigation, the navigation unit can determine the priority of navigation based on the time when the evacuation route was acquired. For example, the navigation unit prioritizes the most recently acquired evacuation route information in the navigation. The navigation unit can also prioritize the evacuation route information with high urgency in the navigation. Furthermore, the navigation unit can also prioritize the most recent information in the navigation while referring to past evacuation route information. In this way, by determining the priority of navigation based on the time when the evacuation route was acquired, the most recent evacuation route information can be provided preferentially. Some or all of the above-described processing in the navigation unit may be performed using, or without, a generation AI. For example, the navigation unit can input data on the time when the evacuation route was acquired into the generation AI and have the generation AI determine the priority.

[0064] During navigation, the navigation unit can adjust the navigation order based on the relevance of evacuation routes. For example, in the event of a fire, the navigation unit prioritizes navigation along routes with less smoke. In addition, in the event of an earthquake, the navigation unit can also prioritize navigation along routes with a low risk of collapse. Furthermore, in the event of a flood, the navigation unit can also prioritize navigation along routes with a low risk of flood damage. In this way, by adjusting the navigation order based on the relevance of evacuation routes, it is possible to provide evacuation routes with high relevance preferentially. Some or all of the above-described processing in the navigation unit may be performed using, or without, a generation AI. For example, the navigation unit can input relevance data of evacuation routes into the generation AI and have the generation AI adjust the order.

[0065] During navigation, the navigation unit can adjust the use of technical terminology in the navigation according to the user's level of expertise. For example, if the user is a disaster prevention expert, the navigation unit can provide navigation using detailed technical terminology. Furthermore, if the user is a general public, the navigation unit can also provide navigation in easy-to-understand language. Furthermore, if the user has disaster prevention knowledge, the navigation unit can also provide navigation using appropriate technical terminology. By adjusting the use of technical terminology in the navigation according to the user's level of expertise, navigation can be provided in a format that is easy for the user to understand. Some or all of the above-described processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the navigation unit can input the user's level of expertise into the generation AI and have the generation AI use technical terminology.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The acquisition unit can analyze the user's past biometric information data and identify time periods and situations in which abnormalities are likely to occur. For example, the acquisition unit can analyze the user's past heart rate data and acquire data focusing on time periods in which abnormalities are likely to occur. The acquisition unit can also analyze the user's past body temperature fluctuation patterns and acquire data on seasons and time periods in which abnormalities are likely to occur. Furthermore, the acquisition unit can analyze the user's past blood pressure data and acquire data on situations in which abnormalities are likely to occur (such as under stress or after exercise). In this way, by analyzing the user's past biometric information data, abnormalities can be detected early.

[0068] When generating appropriate emergency measures when an abnormality is detected, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit can improve the accuracy of generation for the current abnormal heart rate by referring to the generation results for the user's past abnormal heart rate. The generation unit can also improve the accuracy of generation for the current abnormal body temperature by referring to the generation results for the user's past abnormal body temperature. Furthermore, the generation unit can improve the accuracy of generation for the current abnormal blood pressure by referring to the generation results for the user's past abnormal blood pressure. In this way, the accuracy of current generation can be improved by referring to the user's past generation results.

[0069] The acquisition unit can adjust the timing of acquiring biometric information based on the user's current activity status and environment. For example, when the user is exercising, the frequency of acquiring heart rate and blood pressure can be adjusted according to the exercise intensity to monitor for abnormalities. Also, when the user is relaxed, the heart rate and body temperature can be acquired at regular intervals to check for abnormalities. Furthermore, when the user is feeling stressed, temporary increases in blood pressure due to stress can be filtered out so that they are not determined to be abnormal. In this way, by adjusting the acquisition timing based on the user's current activity status and environment, biometric information can be acquired at more appropriate times.

[0070] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the biological information. For example, if the heart rate is abnormally high, a detailed analysis can be performed to identify the cause of the abnormality. Also, if the body temperature is within the normal range, a simplified analysis can be performed to confirm that there is no abnormality. Furthermore, if the blood pressure is abnormally low, a detailed analysis can be performed to identify the cause of the abnormality. Thus, by adjusting the level of detail of the analysis based on the importance of the biological information, important information can be analyzed in detail.

[0071] During generation, the generation unit can apply different generation algorithms depending on the category of abnormality. For example, for an abnormal heart rate, an algorithm can be applied that generates a first aid method that takes into account the possibility of a heart attack. For an abnormal body temperature, an algorithm can be applied that generates a first aid method that takes into account the possibility of fever or hypothermia. Furthermore, for an abnormal blood pressure, an algorithm can be applied that generates a first aid method that takes into account the possibility of high blood pressure or low blood pressure. In this way, by applying different generation algorithms depending on the category of abnormality, more appropriate first aid methods can be provided.

[0072] The providing unit can adjust the level of detail of the information to be provided based on the importance of the information when providing the information. For example, in the case of important information, information including a detailed explanation can be provided. In addition, in the case of information with low importance, simplified information can be provided. Furthermore, in the case of information with high urgency, information that focuses on the main points so that it can be understood quickly can be provided. In this way, by adjusting the level of detail of the information based on the importance of the information, it is possible to provide important information in detail.

[0073] During navigation, the navigation unit can apply different navigation algorithms depending on the category of the evacuation route. For example, a navigation algorithm that prioritizes routes with less smoke can be applied to evacuation routes in the event of a fire. Also, a navigation algorithm that prioritizes routes with a low risk of collapse can be applied to evacuation routes in the event of an earthquake. Furthermore, a navigation algorithm that prioritizes routes with a low risk of flooding can be applied to evacuation routes in the event of a flood. In this way, by applying different navigation algorithms depending on the category of the evacuation route, it is possible to provide more appropriate evacuation routes.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The acquisition unit acquires biological information. For example, the acquisition unit acquires biological information such as heart rate, body temperature, and blood pressure using a sensor, and collects this data in real time. Step 2: The analysis unit analyzes the biological information acquired by the acquisition unit. For example, the analysis unit analyzes fluctuations in heart rate to detect abnormalities. The analysis unit can also analyze body temperature and blood pressure data to detect abnormalities. Step 3: The generation unit generates first aid measures based on the data analyzed by the analysis unit. For example, if the heart rate suddenly rises, it can determine the possibility of a heart attack and generate appropriate first aid measures. Also, if the body temperature is abnormally high, it can determine the possibility of heatstroke and generate appropriate countermeasures. Step 4: The providing unit provides the information generated by the generating unit. For example, the provided first aid information may be notified to the user and displayed on the display of the AR glasses. Step 5: The navigation unit navigates based on the information provided by the provision unit. For example, the generation AI calculates the optimal evacuation route and displays it on the AR glasses' display. It can also guide the user to a safe evacuation shelter using audio guidance.

[0076] (Example 2) A system according to an embodiment of the present invention uses a generative AI embedded in AR glasses to provide user support in emergencies and monitor the user's biometric information. This system monitors the user's biometric information in real time and generates optimal emergency measures if an abnormality is detected. Furthermore, the generative AI generates information corresponding to various emergency scenarios and guides the user to a safe evacuation shelter by combining navigation via the AR glasses and voice guidance. This allows the system to monitor the user's biometric information in real time and provide prompt and appropriate emergency measures if an abnormality is detected. For example, if a sudden increase in heart rate occurs, the generative AI determines the possibility of a heart attack and suggests appropriate first aid measures. Furthermore, in the event of a disaster such as a fire or earthquake, the generative AI calculates the optimal evacuation route and displays it on the AR glasses' display. This allows the user to evacuate quickly and safely.

[0077] An emergency response system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, a provision unit, and a navigation unit. The acquisition unit acquires biometric information. For example, the acquisition unit acquires biometric information such as heart rate, body temperature, and blood pressure using a sensor. The acquisition unit can also collect this data in real time. The analysis unit analyzes the biometric information acquired by the acquisition unit. For example, the analysis unit analyzes heart rate fluctuations to detect abnormalities. The analysis unit can also analyze body temperature and blood pressure data to detect abnormalities. The generation unit generates first aid measures based on the data analyzed by the analysis unit. For example, if the heart rate suddenly increases, the generation unit determines the possibility of a heart attack and generates appropriate first aid measures. If the body temperature is abnormally high, the generation unit can also determine the possibility of heatstroke and generate appropriate countermeasures. The provision unit provides the information generated by the generation unit. For example, the provision unit notifies the user of the generated first aid measures. The provision unit can also display the generated information on the display of the AR glasses. The navigation unit performs navigation based on the information provided by the provision unit. For example, the navigation unit calculates the optimal evacuation route using a generation AI and displays it on the display of the AR glasses. The navigation unit can also guide the user to a safe evacuation shelter using audio guidance. As a result, the emergency response system according to the embodiment can monitor the user's biometric information in real time, and if an abnormality is detected, provide prompt and appropriate emergency measures and perform navigation.

[0078] The acquisition unit can acquire at least one of biometric information from heart rate, body temperature, and blood pressure. For example, the acquisition unit measures heart rate using a sensor and collects data. The acquisition unit can also measure body temperature and collect data. Furthermore, the acquisition unit can measure blood pressure and collect data. In this way, abnormalities can be detected by acquiring biometric information such as the user's heart rate, body temperature, and blood pressure. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input heart rate data to the generation AI and cause the generation AI to analyze the heart rate.

[0079] The analysis unit can analyze the acquired biometric information and detect abnormalities. The analysis unit, for example, analyzes fluctuations in heart rate and detects abnormalities. For example, the analysis unit detects an abnormality if the heart rate suddenly increases. The analysis unit can also analyze fluctuations in body temperature and detect abnormalities. For example, the analysis unit detects an abnormality if the body temperature is abnormally high. The analysis unit can also analyze fluctuations in blood pressure and detect abnormalities. For example, the analysis unit detects an abnormality if the blood pressure is abnormally low. In this way, by analyzing the acquired biometric information and detecting abnormalities, appropriate emergency measures can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the acquired biometric information to a generation AI and have the generation AI detect abnormalities.

[0080] The generation unit can generate appropriate first aid measures when an abnormality is detected. For example, if the heart rate suddenly increases, the generation unit determines the possibility of a heart attack and generates appropriate first aid measures. For example, the generation unit suggests how to perform cardiac massage. The generation unit can also determine the possibility of heat stroke when the body temperature is abnormally high and generate appropriate countermeasures. For example, the generation unit suggests how to hydrate. The generation unit can also determine the possibility of hypotension when the blood pressure is abnormally low and generate appropriate countermeasures. For example, the generation unit suggests how to lie down and elevate the legs. This enables a prompt and appropriate response by generating optimal first aid measures when an abnormality is detected. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input biometric information in which an abnormality is detected into the generation AI and have the generation AI generate first aid measures.

[0081] The providing unit can provide the generated emergency measures to the user. The providing unit, for example, notifies the user of the generated emergency measures. For example, the providing unit displays the emergency measures on a display of the AR glasses. The providing unit can also communicate the generated emergency measures to the user by voice. For example, the providing unit instructs the user by voice, "Please perform cardiac massage." The providing unit can also notify the generated emergency measures to a smartphone. For example, the providing unit displays the emergency measures on a smartphone app. By providing the generated emergency measures to the user, the user can perform appropriate first aid. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated emergency measures to a generation AI and cause the generation AI to notify the user.

[0082] The navigation unit can use the generation AI to calculate an appropriate evacuation route and display it on the display of the AR glasses. For example, in the event of a fire, the generation AI calculates a route with less smoke and displays it as an arrow on the display of the AR glasses. In addition, in the event of an earthquake, the navigation unit can use the generation AI to calculate a route with a low risk of collapse and display it on the display of the AR glasses. In addition, in the event of a flood, the navigation unit can use the generation AI to calculate a route with a low risk of flood damage and display it on the display of the AR glasses. In this way, the generation AI can calculate an optimal evacuation route and display it on the display of the AR glasses, thereby guiding the user to a safe evacuation shelter. Some or all of the above-described processing in the navigation unit may be performed using, or without, the generation AI. For example, the navigation unit can have the generation AI calculate an evacuation route and display the calculation results on the display of the AR glasses.

[0083] The navigation unit can guide the user to a safe evacuation shelter using audio guidance. For example, in the event of a fire, the navigation unit can provide audio guidance such as "Turn right" based on the evacuation route calculated by the generation AI. Furthermore, in the event of an earthquake, the navigation unit can also provide audio guidance such as "Move to a safe location." Furthermore, in the event of a flood, the navigation unit can also provide audio guidance such as "Evacuate to higher ground." Thus, by guiding the user to a safe evacuation shelter using audio guidance, the user can be safely evacuated using both visual and auditory senses. Some or all of the above-described processing in the navigation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the navigation unit can cause the generation AI to generate an audio guidance and provide audio instructions to the user.

[0084] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring biometric information based on the estimated user emotions. For example, when the user is nervous, the acquisition unit monitors heart rate fluctuations in real time and frequently checks for any abnormalities. Furthermore, when the user is relaxed, the acquisition unit can acquire the heart rate and body temperature at regular intervals to check for any abnormalities. Furthermore, when the user is exercising, the acquisition unit can adjust the frequency of heart rate and blood pressure acquisition according to the exercise intensity and monitor for any abnormalities. This allows the acquisition timing of biometric information to be adjusted according to the user's emotions, thereby acquiring biometric information at more appropriate times. 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 acquisition unit may be performed using, for example, the generation AI. For example, the acquisition unit can input the user's emotion data into the generation AI and have the generation AI adjust the acquisition timing.

[0085] The acquisition unit can analyze the user's past biometric information history and select the optimal acquisition method. For example, the acquisition unit can analyze the user's past heart rate data and acquire data focusing on time periods when abnormalities are likely to occur. The acquisition unit can also analyze the user's past body temperature fluctuation patterns and acquire data during seasons or time periods when abnormalities are likely to occur. Furthermore, the acquisition unit can analyze the user's past blood pressure data and acquire data in situations when abnormalities are likely to occur (such as during stress or after exercise). This allows the user's past biometric information history to be analyzed to select the optimal acquisition method and detect abnormalities early. Some or all of the above-described processing in the acquisition unit can be performed using, for example, a generation AI. For example, the acquisition unit can input the user's past biometric information data into the generation AI and have the generation AI select the optimal acquisition method.

[0086] When acquiring biometric information, the acquisition unit can perform filtering based on the user's current activity status and environment. For example, when the user is exercising, the acquisition unit can filter out temporary increases in heart rate due to exercise and not determine them as abnormal. Furthermore, when the user is relaxed, the acquisition unit can filter out minute fluctuations in heart rate and body temperature and not determine them as abnormal. Furthermore, when the user is feeling stressed, the acquisition unit can filter out temporary increases in blood pressure due to stress and not determine them as abnormal. Thus, by performing filtering based on the user's current activity status and environment, false detections can be prevented and accurate biometric information can be acquired. Some or all of the above-described processing in the acquisition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the acquisition unit can input the user's activity status data into the generation AI and have the generation AI perform filtering.

[0087] When acquiring biometric information, the acquisition unit can select the optimal acquisition means according to the user's input method. For example, if the user vocally instructs, "Measure my heart rate," the acquisition unit acquires the heart rate based on the voice input. Furthermore, if the user inputs, "Measure my body temperature" as text, the acquisition unit can acquire the body temperature based on the text input. Furthermore, if the user performs a specific gesture, the acquisition unit can acquire the blood pressure based on the gesture. This allows for selecting the optimal acquisition means according to the user's input method, thereby improving user convenience. Some or all of the above-described processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the user's input data into the generation AI and have the generation AI select the optimal acquisition means.

[0088] The acquisition unit can estimate the user's emotions and determine the priority of biometric information to be acquired based on the estimated user's emotions. For example, if the user is nervous, the acquisition unit can prioritize acquiring heart rate and blood pressure and check for any abnormalities. Furthermore, if the user is relaxed, the acquisition unit can prioritize acquiring body temperature and respiratory rate and check for any abnormalities. Furthermore, if the user is exercising, the acquisition unit can prioritize acquiring heart rate and oxygen saturation and check for any abnormalities. Thus, by determining the priority of biometric information to be acquired according to the user's emotions, important information can be acquired preferentially. Emotion estimation is realized using an emotion estimation function, for example, 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 acquisition unit can be performed using, for example, the generation AI. For example, the acquisition unit can input the user's emotion data into the generation AI and have the generation AI determine the priority.

[0089] When acquiring biometric information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, if the user is at high altitude, the acquisition unit can prioritize acquiring oxygen saturation and check for any abnormalities. Furthermore, if the user is in a cold region, the acquisition unit can prioritize acquiring body temperature and check for the risk of hypothermia. Furthermore, if the user is in an urban area, the acquisition unit can prioritize acquiring heart rate and blood pressure and check for the risk of stress or overwork. Thus, by prioritizing the acquisition of highly relevant information by taking into account the user's geographical location information, more appropriate information can be acquired. Some or all of the above-described processing in the acquisition unit may be performed, for example, using or without the generation AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant information.

[0090] When acquiring biometric information, the acquisition unit can analyze the user's social media activity and acquire related information. For example, if the user posts on social media that they are feeling stressed, the acquisition unit can prioritize acquiring their heart rate and blood pressure. Furthermore, if the user posts on social media that they are exercising, the acquisition unit can prioritize acquiring their heart rate and oxygen saturation. Furthermore, if the user posts on social media that they are feeling unwell, the acquisition unit can prioritize acquiring their body temperature and respiratory rate. This allows for the acquisition of related information and early detection of abnormalities by analyzing the user's social media activity. Some or all of the above-described processing in the acquisition unit can be performed, for example, using or without the generation AI. For example, the acquisition unit can input the user's social media data into the generation AI and cause the generation AI to acquire related information.

[0091] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring biometric information. For example, if the user has previously provided feedback to increase the frequency of heart rate acquisition, the acquisition unit can increase the frequency of heart rate acquisition. The acquisition unit can also change the body temperature acquisition method if the user has previously provided feedback to change the body temperature acquisition method. Furthermore, the acquisition unit can also adjust the timing of blood pressure acquisition if the user has previously provided feedback to adjust the timing of blood pressure acquisition. This allows the acquisition method to be customized by reflecting the user's past feedback, and optimal information can be acquired for the user. Some or all of the above-described processing in the acquisition unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0092] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can display the analysis results in a simple and easy-to-understand format. Furthermore, if the user is relaxed, the analysis unit can display detailed analysis results to make them easier for the user to understand. Furthermore, if the user is in a hurry, the analysis unit can display analysis results that focus on the main points and provide information quickly. This allows the analysis results to be provided in a format that is easy for the user to understand by adjusting the presentation method of the analysis 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 can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method.

[0093] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the biometric information. For example, if the heart rate is abnormally high, the analysis unit performs a detailed analysis to identify the cause of the abnormality. Furthermore, if the body temperature is within the normal range, the analysis unit can perform a simplified analysis to confirm that there is no abnormality. Furthermore, if the blood pressure is abnormally low, the analysis unit can perform a detailed analysis to identify the cause of the abnormality. Thus, by adjusting the level of detail of the analysis based on the importance of the biometric information, important information can be analyzed in detail. 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 input biometric information data with high importance to the generation AI and have the generation AI perform a detailed analysis.

[0094] The analysis unit can apply different analysis algorithms depending on the category of biometric information during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to analyze heart rate. The analysis unit can also apply a body temperature variability analysis algorithm to analyze body temperature. The analysis unit can also apply a blood pressure variability analysis algorithm to analyze blood pressure. By applying different analysis algorithms depending on the category of biometric information, more accurate analysis results can be obtained. Some or all of the above-mentioned 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 biometric information data for each category into the generation AI and have the generation AI apply an appropriate analysis algorithm.

[0095] 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 accuracy of the current heart rate analysis by referring to the user's past heart rate analysis results. The analysis unit can also improve the accuracy of the current body temperature analysis by referring to the user's past body temperature analysis results. Furthermore, the analysis unit can also improve the accuracy of the current blood pressure analysis by referring to the user's past blood pressure analysis results. In this way, the accuracy of the current analysis can be improved by referring to the user's past analysis results. 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 input the user's past analysis data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0096] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that is easy to understand. By adjusting the length of the analysis according to the user's emotions, the analysis result can be provided in a format that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0097] During analysis, the analysis unit can determine the priority of analysis based on the time when the biological information was acquired. For example, the analysis unit can prioritize analyzing recently acquired heart rate data to check for any abnormalities. The analysis unit can also prioritize analyzing recently acquired body temperature data to check for any abnormalities. Furthermore, the analysis unit can prioritize analyzing recently acquired blood pressure data to check for any abnormalities. In this way, by determining the priority of analysis based on the time when the biological information was acquired, it is possible to prioritize analyzing the most recent information. 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 input data on the time of acquisition into the generation AI and have the generation AI determine the priority.

[0098] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the biometric information. For example, the analysis unit may prioritize the analysis of the heart rate, taking into account the relevance between the heart rate and blood pressure. The analysis unit may also prioritize the analysis of the body temperature, taking into account the relevance between the body temperature and respiratory rate. Furthermore, the analysis unit may prioritize the analysis of the blood pressure, taking into account the relevance between the blood pressure and oxygen saturation. In this way, by adjusting the order of analysis based on the relevance of the biometric information, highly relevant information can be analyzed preferentially. 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 the generation AI. For example, the analysis unit may input highly relevant data into the generation AI and have the generation AI adjust the order of analysis.

[0099] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is a medical professional, the analysis unit can provide the analysis results using detailed technical terminology. Furthermore, if the user is a layperson, the analysis unit can also provide the analysis results in easy-to-understand language. Furthermore, if the user has medical knowledge, the analysis unit can also provide the analysis results using appropriate technical terminology. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, the analysis results can be provided in a format that is easy for the user to understand. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terminology.

[0100] The generation unit can estimate the user's emotions and adjust the expression method of the generated emergency measures based on the estimated user's emotions. For example, if the user is nervous, the generation unit generates emergency measures using a simple and easy-to-understand expression method. Furthermore, if the user is relaxed, the generation unit can generate emergency measures using an expression method that includes detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate emergency measures using an expression method that focuses on the main points. By adjusting the expression method of emergency measures according to the user's emotions, emergency measures can be provided in a format that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the expression method.

[0101] The generation unit can adjust the level of detail of the first aid measures to be generated based on the severity of the abnormality during generation. For example, the generation unit generates detailed first aid measures when the heart rate is abnormally high. The generation unit can also generate simplified first aid measures when the body temperature is within the normal range. Furthermore, the generation unit can generate detailed first aid measures when the blood pressure is abnormally low. In this way, by adjusting the level of detail of the first aid measures based on the severity of the abnormality, detailed first aid measures can be provided for serious abnormalities. 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 input abnormality severity data into the generation AI and cause the generation AI to adjust the level of detail.

[0102] The generation unit can apply different generation algorithms depending on the category of abnormality during generation. For example, for an abnormal heart rate, the generation unit applies an algorithm that generates a first aid method that takes into account the possibility of a heart attack. For an abnormal body temperature, the generation unit can also apply an algorithm that generates a first aid method that takes into account the possibility of fever or hypothermia. For an abnormal blood pressure, the generation unit can also apply an algorithm that generates a first aid method that takes into account the possibility of high blood pressure or low blood pressure. In this way, by applying different generation algorithms depending on the category of abnormality, more appropriate first aid methods can be provided. 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 input abnormality category data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0103] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit can improve the accuracy of generation for a current abnormal heart rate by referring to the generation results for the user's past abnormal heart rate. The generation unit can also improve the accuracy of generation for a current abnormal body temperature by referring to the generation results for the user's past abnormal body temperature. Furthermore, the generation unit can also improve the accuracy of generation for a current abnormal blood pressure by referring to the generation results for the user's past abnormal blood pressure. In this way, by referring to the user's past generation results, the accuracy of current generation can be improved. 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 input the user's past generation data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0104] The generation unit can estimate the user's emotions and adjust the length of the emergency medical procedures to be generated based on the estimated user emotions. For example, if the user is nervous, the generation unit can generate short, concise emergency medical procedures. Furthermore, if the user is relaxed, the generation unit can generate longer emergency medical procedures with detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate emergency medical procedures that are concise and easy to understand. This allows the length of the emergency medical procedures to be adjusted according to the user's emotions, thereby providing emergency medical procedures in a format that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the emergency medical procedures.

[0105] At the time of generation, the generation unit can determine the priority of the emergency measures to be generated based on the time of occurrence of the abnormality. For example, the generation unit can prioritize analyzing a recently occurring heart rate abnormality and generate emergency measures. The generation unit can also prioritize analyzing a recently occurring body temperature abnormality and generate emergency measures. Furthermore, the generation unit can prioritize analyzing a recently occurring blood pressure abnormality and generate emergency measures. In this way, by determining the priority of emergency measures based on the time of occurrence of the abnormality, it is possible to respond quickly to the latest abnormality. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the time of occurrence of the abnormality to the generation AI and have the generation AI determine the priority.

[0106] The generation unit can adjust the order of emergency measures to be generated based on the correlation of abnormalities during generation. For example, the generation unit may consider the correlation between heart rate and blood pressure and generate emergency measures for abnormal heart rate with priority. The generation unit may also consider the correlation between body temperature and respiratory rate and generate emergency measures for abnormal body temperature with priority. Furthermore, the generation unit may consider the correlation between blood pressure and oxygen saturation and generate emergency measures for abnormal blood pressure with priority. By adjusting the order of emergency measures based on the correlation of abnormalities, it is possible to prioritize responses to abnormalities with high correlation. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input abnormality correlation data into the generation AI and cause the generation AI to adjust the order.

[0107] The generation unit can adjust the use of technical terminology in the emergency procedures to be generated according to the user's level of expertise during generation. For example, if the user is a medical professional, the generation unit can generate emergency procedures using detailed technical terminology. Furthermore, if the user is a layperson, the generation unit can also generate emergency procedures using easy-to-understand language. Furthermore, if the user has medical knowledge, the generation unit can generate emergency procedures using appropriate technical terminology. This allows emergency procedures to be provided in a format that is easy for the user to understand by adjusting the use of technical terminology in the emergency procedures according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's level of expertise into the generation AI and have the generation AI use technical terminology.

[0108] The providing unit can estimate the user's emotions and adjust the way the information is presented based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide information using a simple and easy-to-understand expression. Furthermore, if the user is relaxed, the providing unit can provide information using an expression that includes detailed explanations. Furthermore, if the user is in a hurry, the providing unit can provide information using an expression that focuses on the main points. This allows the information to be presented in a format that is easy for the user to understand by adjusting the way the information is presented according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, 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 providing unit can be performed using, for example, the generation AI, or without the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the information is presented.

[0109] The providing unit can adjust the level of detail of the information to be provided based on the importance of the information when providing the information. For example, in the case of important information, the providing unit provides information including a detailed explanation. In addition, in the case of information with low importance, the providing unit can also provide simplified information. Furthermore, in the case of information with high urgency, the providing unit can also provide information that focuses on the main points so that it can be understood quickly. In this way, by adjusting the level of detail of the information based on the importance of the information, it is possible to provide important information in detail. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail.

[0110] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, in the case of medical information, the providing unit can provide the information using detailed technical terms. In addition, in the case of general information, the providing unit can also provide information in easy-to-understand language. Furthermore, in the case of emergency information, the providing unit can also provide information that is brief so that it can be quickly understood. In this way, by applying different providing algorithms depending on the category of information, it is possible to provide information in a more appropriate format. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI, for example. For example, the providing unit can input information category data into the generation AI and cause the generation AI to apply an appropriate providing algorithm.

[0111] The providing unit can improve the accuracy of information provision by referring to the user's past information provision results when providing information. For example, the providing unit can improve the accuracy of current information provision by referring to the user's past information provision results. The providing unit can also improve the method of information provision by referring to the user's past feedback. Furthermore, the providing unit can also select the optimal information provision method by referring to the user's past usage history. In this way, the accuracy of current information provision can be improved by referring to the user's past information provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's past provided data into the generation AI and cause the generation AI to improve the accuracy of information provision.

[0112] The providing unit can estimate the user's emotions and adjust the length of the information to be provided based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide short, to-the-point information. Furthermore, if the user is relaxed, the providing unit can provide longer information with detailed explanations. Furthermore, if the user is in a hurry, the providing unit can provide information that is to the point so that the user can understand it quickly. By adjusting the length of the information according to the user's emotions, the information can be provided in a format that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, 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 providing unit can be performed using, for example, the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the information.

[0113] The providing unit can determine the priority of information to be provided based on the time when the information was acquired at the time of provision. For example, the providing unit can prioritize providing important information that was recently acquired. The providing unit can also prioritize providing general information that was recently acquired. Furthermore, the providing unit can also prioritize providing information with high urgency. In this way, by determining the priority of information based on the time when the information was acquired, it is possible to provide the latest information with priority. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input information acquisition time data to the generation AI and have the generation AI determine the priority.

[0114] The providing unit can adjust the order of information to be provided based on the relevance of the information when providing the information. For example, the providing unit can provide important information first, followed by general information. The providing unit can also provide urgent information first, followed by important information. Furthermore, the providing unit can also provide highly relevant information first, followed by general information. In this way, by adjusting the order of information based on the relevance of the information, highly relevant information can be provided first. Some or all of the above-described processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input relevance data of the information to the generation AI and cause the generation AI to adjust the order.

[0115] The providing unit can adjust the use of technical terminology in the information to be provided according to the user's level of expertise. For example, if the user is a medical professional, the providing unit can provide the information using detailed technical terminology. Furthermore, if the user is a layperson, the providing unit can also provide the information in easy-to-understand language. Furthermore, if the user has medical knowledge, the providing unit can also provide the information using appropriate technical terminology. This allows the information to be provided in a format that is easy for the user to understand by adjusting the use of technical terminology in the information according to the user's level of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's level of expertise into the generation AI and have the generation AI execute the use of technical terminology.

[0116] The navigation unit can estimate the user's emotions and adjust the navigation display method based on the estimated user emotions. For example, when the user is nervous, the navigation unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the navigation unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the navigation unit can provide a display method that focuses on the main points. By adjusting the navigation display method according to the user's emotions, navigation can be provided in a format that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the navigation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the navigation unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0117] During navigation, the navigation unit can adjust the level of detail of the navigation based on the importance of the evacuation route. For example, the navigation unit provides detailed navigation information for an important evacuation route. The navigation unit can also provide simplified navigation information for a general evacuation route. Furthermore, for a highly urgent evacuation route, the navigation unit can provide navigation information that focuses on the main points so that it can be quickly understood. In this way, by adjusting the level of detail of the navigation based on the importance of the evacuation route, detailed navigation information can be provided for an important evacuation route. Some or all of the above-mentioned processing in the navigation unit may be performed using, or without, a generation AI. For example, the navigation unit can input importance data of the evacuation route to the generation AI and have the generation AI adjust the level of detail.

[0118] During navigation, the navigation unit can apply different navigation algorithms depending on the category of the evacuation route. For example, the navigation unit can apply a navigation algorithm that prioritizes routes with less smoke to evacuation routes in the event of a fire. The navigation unit can also apply a navigation algorithm that prioritizes routes with a low risk of collapse to evacuation routes in the event of an earthquake. Furthermore, the navigation unit can also apply a navigation algorithm that prioritizes routes with a low risk of flooding to evacuation routes in the event of a flood. In this way, by applying different navigation algorithms depending on the category of the evacuation route, it is possible to provide a more appropriate evacuation route. Some or all of the above-mentioned processing in the navigation unit may be performed using, or without, a generation AI. For example, the navigation unit can input evacuation route category data into the generation AI and cause the generation AI to apply an appropriate navigation algorithm.

[0119] The navigation unit can improve the accuracy of navigation during navigation by referring to the user's past navigation results. For example, the navigation unit can improve the accuracy of navigation during a current fire by referring to the user's past navigation results during a fire. The navigation unit can also improve the accuracy of navigation during a current earthquake by referring to the user's past navigation results during an earthquake. Furthermore, the navigation unit can improve the accuracy of navigation during a current flood by referring to the user's past navigation results during a flood. In this way, the accuracy of current navigation can be improved by referring to the user's past navigation results. Some or all of the above-described processing in the navigation unit may be performed using, or without, a generation AI. For example, the navigation unit can input the user's past navigation data into the generation AI and have the generation AI improve the accuracy of navigation.

[0120] The navigation unit can estimate the user's emotions and adjust the length of the navigation based on the estimated user emotions. For example, if the user is nervous, the navigation unit can provide short, to-the-point navigation. Furthermore, if the user is relaxed, the navigation unit can provide longer, more detailed navigation for quick understanding. Furthermore, if the user is in a hurry, the navigation unit can provide navigation that is concise and to the point for quick understanding. By adjusting the length of the navigation according to the user's emotions, the navigation can be provided in a format that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the navigation unit can be performed using, for example, the generation AI. For example, the navigation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the navigation.

[0121] During navigation, the navigation unit can determine the priority of navigation based on the time when the evacuation route was acquired. For example, the navigation unit prioritizes the most recently acquired evacuation route information in the navigation. The navigation unit can also prioritize the evacuation route information with high urgency in the navigation. Furthermore, the navigation unit can also prioritize the most recent information in the navigation while referring to past evacuation route information. In this way, by determining the priority of navigation based on the time when the evacuation route was acquired, the most recent evacuation route information can be provided preferentially. Some or all of the above-described processing in the navigation unit may be performed using, or without, a generation AI. For example, the navigation unit can input data on the time when the evacuation route was acquired into the generation AI and have the generation AI determine the priority.

[0122] During navigation, the navigation unit can adjust the navigation order based on the relevance of evacuation routes. For example, in the event of a fire, the navigation unit prioritizes navigation along routes with less smoke. In addition, in the event of an earthquake, the navigation unit can also prioritize navigation along routes with a low risk of collapse. Furthermore, in the event of a flood, the navigation unit can also prioritize navigation along routes with a low risk of flood damage. In this way, by adjusting the navigation order based on the relevance of evacuation routes, it is possible to provide evacuation routes with high relevance preferentially. Some or all of the above-described processing in the navigation unit may be performed using, or without, a generation AI. For example, the navigation unit can input relevance data of evacuation routes into the generation AI and have the generation AI adjust the order.

[0123] During navigation, the navigation unit can adjust the use of technical terminology in the navigation according to the user's level of expertise. For example, if the user is a disaster prevention expert, the navigation unit can provide navigation using detailed technical terminology. Furthermore, if the user is a general public, the navigation unit can also provide navigation in easy-to-understand language. Furthermore, if the user has disaster prevention knowledge, the navigation unit can also provide navigation using appropriate technical terminology. By adjusting the use of technical terminology in the navigation according to the user's level of expertise, navigation can be provided in a format that is easy for the user to understand. Some or all of the above-described processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the navigation unit can input the user's level of expertise into the generation AI and have the generation AI use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, provision unit, and navigation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit can acquire biometric information such as heart rate, body temperature, and blood pressure using a sensor of the smart device 14. The analysis unit analyzes the biometric information acquired by the specific processing unit 290 of the data processing device 12 and detects abnormalities. The generation unit generates emergency measures based on the data analyzed by the specific processing unit 290 of the data processing device 12. The provision unit displays the generated emergency measures on the display of the smart device 14. The navigation unit calculates the optimal evacuation route using a generation AI and displays it on the display of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, provision unit, and navigation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit can acquire biometric information such as heart rate, body temperature, and blood pressure using a sensor in the smart glasses 214. The analysis unit analyzes the biometric information acquired by the specific processing unit 290 of the data processing device 12 and detects abnormalities. The generation unit generates emergency measures based on the data analyzed by the specific processing unit 290 of the data processing device 12. The provision unit displays the generated emergency measures on the display of the smart glasses 214. The navigation unit calculates the optimal evacuation route using a generation AI and displays it on the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, provision unit, and navigation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit can acquire biological information such as heart rate, body temperature, and blood pressure using a sensor in the headset type terminal 314. The analysis unit analyzes the biological information acquired by the specific processing unit 290 of the data processing device 12 and detects abnormalities. The generation unit generates emergency measures based on the data analyzed by the specific processing unit 290 of the data processing device 12. The provision unit displays the generated emergency measures on the display of the headset type terminal 314. The navigation unit calculates the optimal evacuation route using a generation AI and displays it on the display of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, provision unit, and navigation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit can acquire biological information such as heart rate, body temperature, and blood pressure using sensors in the robot 414. The analysis unit analyzes the biological information acquired by the specific processing unit 290 of the data processing device 12 and detects abnormalities. The generation unit generates emergency measures based on the data analyzed by the specific processing unit 290 of the data processing device 12. The provision unit displays the generated emergency measures on the display of the robot 414. The navigation unit calculates the optimal evacuation route using a generation AI and displays it on the display of the robot 414.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The acquisition unit can analyze the user's past biometric information data and identify time periods and situations in which abnormalities are likely to occur. For example, the acquisition unit can analyze the user's past heart rate data and acquire data focusing on time periods in which abnormalities are likely to occur. The acquisition unit can also analyze the user's past body temperature fluctuation patterns and acquire data on seasons and time periods in which abnormalities are likely to occur. Furthermore, the acquisition unit can analyze the user's past blood pressure data and acquire data on situations in which abnormalities are likely to occur (such as under stress or after exercise). In this way, by analyzing the user's past biometric information data, abnormalities can be detected early.

[0126] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, the analysis results can be displayed in a simple, easy-to-understand format. If the user is relaxed, detailed analysis results can be displayed to make it easier for the user to understand. Furthermore, if the user is in a hurry, analysis results that focus on the main points can be displayed to provide information quickly. In this way, by adjusting the way the analysis is presented according to the user's emotions, the analysis results can be presented in a format that is easy for the user to understand.

[0127] When generating appropriate emergency measures when an abnormality is detected, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit can improve the accuracy of generation for the current abnormal heart rate by referring to the generation results for the user's past abnormal heart rate. The generation unit can also improve the accuracy of generation for the current abnormal body temperature by referring to the generation results for the user's past abnormal body temperature. Furthermore, the generation unit can improve the accuracy of generation for the current abnormal blood pressure by referring to the generation results for the user's past abnormal blood pressure. In this way, the accuracy of current generation can be improved by referring to the user's past generation results.

[0128] The providing unit can estimate the user's emotions and adjust the way in which information is presented based on the estimated user's emotions. For example, if the user is nervous, the information can be presented in a simple and easy-to-understand way. If the user is relaxed, the information can be presented in a way that includes detailed explanations. Furthermore, if the user is in a hurry, the information can be presented in a way that focuses on the main points. In this way, by adjusting the way in which information is presented depending on the user's emotions, the information can be presented in a format that is easy for the user to understand.

[0129] The navigation unit can estimate the user's emotions and adjust the navigation display method based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the navigation display method according to the user's emotions, navigation can be provided in a format that is easy for the user to understand.

[0130] The acquisition unit can adjust the timing of acquiring biometric information based on the user's current activity status and environment. For example, when the user is exercising, the frequency of acquiring heart rate and blood pressure can be adjusted according to the exercise intensity to monitor for abnormalities. Also, when the user is relaxed, the heart rate and body temperature can be acquired at regular intervals to check for abnormalities. Furthermore, when the user is feeling stressed, temporary increases in blood pressure due to stress can be filtered out so that they are not determined to be abnormal. In this way, by adjusting the acquisition timing based on the user's current activity status and environment, biometric information can be acquired at more appropriate times.

[0131] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the biological information. For example, if the heart rate is abnormally high, a detailed analysis can be performed to identify the cause of the abnormality. Also, if the body temperature is within the normal range, a simplified analysis can be performed to confirm that there is no abnormality. Furthermore, if the blood pressure is abnormally low, a detailed analysis can be performed to identify the cause of the abnormality. Thus, by adjusting the level of detail of the analysis based on the importance of the biological information, important information can be analyzed in detail.

[0132] During generation, the generation unit can apply different generation algorithms depending on the category of abnormality. For example, for an abnormal heart rate, an algorithm can be applied that generates a first aid method that takes into account the possibility of a heart attack. For an abnormal body temperature, an algorithm can be applied that generates a first aid method that takes into account the possibility of fever or hypothermia. Furthermore, for an abnormal blood pressure, an algorithm can be applied that generates a first aid method that takes into account the possibility of high blood pressure or low blood pressure. In this way, by applying different generation algorithms depending on the category of abnormality, more appropriate first aid methods can be provided.

[0133] The providing unit can adjust the level of detail of the information to be provided based on the importance of the information when providing the information. For example, in the case of important information, information including a detailed explanation can be provided. In addition, in the case of information with low importance, simplified information can be provided. Furthermore, in the case of information with high urgency, information that focuses on the main points so that it can be understood quickly can be provided. In this way, by adjusting the level of detail of the information based on the importance of the information, it is possible to provide important information in detail.

[0134] During navigation, the navigation unit can apply different navigation algorithms depending on the category of the evacuation route. For example, a navigation algorithm that prioritizes routes with less smoke can be applied to evacuation routes in the event of a fire. Also, a navigation algorithm that prioritizes routes with a low risk of collapse can be applied to evacuation routes in the event of an earthquake. Furthermore, a navigation algorithm that prioritizes routes with a low risk of flooding can be applied to evacuation routes in the event of a flood. In this way, by applying different navigation algorithms depending on the category of the evacuation route, it is possible to provide more appropriate evacuation routes.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The acquisition unit acquires biological information. For example, the acquisition unit acquires biological information such as heart rate, body temperature, and blood pressure using a sensor, and collects this data in real time. Step 2: The analysis unit analyzes the biological information acquired by the acquisition unit. For example, the analysis unit analyzes fluctuations in heart rate to detect abnormalities. The analysis unit can also analyze body temperature and blood pressure data to detect abnormalities. Step 3: The generation unit generates first aid measures based on the data analyzed by the analysis unit. For example, if the heart rate suddenly rises, it can determine the possibility of a heart attack and generate appropriate first aid measures. Also, if the body temperature is abnormally high, it can determine the possibility of heatstroke and generate appropriate countermeasures. Step 4: The providing unit provides the information generated by the generating unit. For example, the provided first aid information may be notified to the user and displayed on the display of the AR glasses. Step 5: The navigation unit navigates based on the information provided by the provision unit. For example, the generation AI calculates the optimal evacuation route and displays it on the AR glasses' display. It can also guide the user to a safe evacuation shelter using audio guidance.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 AI 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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 AI 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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 AI 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.

[0188] 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.

[0189] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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).

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Explanation of symbols]

[0209] 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. an acquisition unit for acquiring biometric information; an analysis unit that analyzes the biometric information acquired by the acquisition unit; a generation unit that generates emergency measures based on the data analyzed by the analysis unit; a providing unit that provides the information generated by the generating unit; a navigation unit that performs navigation based on the information provided by the providing unit. A system characterized by:

2. The acquisition unit Acquire at least one of the following vital signs: heart rate, body temperature, and blood pressure 2. The system of claim 1.

3. The analysis unit Analyze acquired biometric information and detect abnormalities 2. The system of claim 1.

4. The generation unit Generate appropriate emergency measures when an abnormality is detected 2. The system of claim 1.

5. The providing unit Provide the generated first aid to the user 2. The system of claim 1.

6. The navigation unit Generative AI calculates the appropriate evacuation route and displays it on the AR glasses screen.

2. The system of claim 1.

7. The navigation unit Audio guides guide users to safe evacuation shelters 2. The system of claim 1.

8. The acquisition unit The system estimates the user's emotions and adjusts the timing of acquiring biometric information based on the estimated user emotions.

2. The system of claim 1.

9. The acquisition unit Analyze the user's past biometric information history and select the optimal acquisition method 2. The system of claim 1.

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