System

Abstract

To provide a system that quickly performs appropriate responses during an emergency.SOLUTION: A system according to the embodiment comprises a reception unit, a triage unit, and a dispatch unit. The reception unit confirms a situation in accordance with voice guideline. The triage unit performs triage based on the information confirmed by the reception unit. The dispatch unit dispatches an ambulance or makes a hospital appointment and provides first aid based on the result determined by the triage 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have the disadvantage that it is difficult to take appropriate and prompt action in the event of an emergency.

[0005] The system according to the embodiment aims to quickly take appropriate action in the event of an emergency. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a triage unit, and a dispatch unit. The reception unit checks the situation according to the voice guidelines. The triage unit performs triage based on the information confirmed by the reception unit. The dispatch unit dispatches an ambulance or makes a hospital appointment or provides first aid based on the result of the determination by the triage unit. Effect of the Invention

[0007] The system according to the embodiment can quickly take appropriate action in the event of an emergency. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The emergency response system according to the embodiment of the present invention is a system for determining whether to call an ambulance or perform first aid in an emergency. In this system, a user starts the system and checks the situation according to voice guidelines. Next, the system performs triage according to the situation, and if necessary, arranges an ambulance, makes an appointment to see the nearest hospital, and guides the first aid. In addition, based on information registered in advance, a faster and more appropriate response is possible. For example, if a user complains of "chest pain," the system asks a series of questions to confirm details of the symptoms. At this time, the user only needs to answer by voice. Next, the system performs triage according to the situation. For example, if the user's symptoms are determined to be serious, the system immediately arranges an ambulance. In addition, if the symptoms are mild, the system makes an appointment to see the nearest hospital. Furthermore, if first aid is required, the system guides the user by voice on specific first aid methods. In addition, based on information registered in advance, a faster and more appropriate response is possible. For example, if the user's medical history or allergy information is registered in advance, the system takes these information into consideration and performs appropriate response. This allows a faster and more appropriate response even in an emergency. For example, if a user complains of sudden chest pain, the system can quickly check the situation and dispatch an ambulance if necessary, enabling prompt medical care. This allows the emergency response system to provide prompt and appropriate responses in emergency situations.

[0029] The emergency response system according to the embodiment includes a reception unit, a triage unit, and a dispatch unit. The reception unit checks the situation according to the voice guidelines. For example, if the user complains that "my chest hurts," the reception unit asks a series of questions to check the details of the symptoms. At this time, the user only needs to answer by voice. The reception unit, for example, uses voice recognition technology to analyze the user's voice input and understands the answers to the questions. The triage unit performs triage based on the information confirmed by the reception unit. For example, if the user's symptoms are determined to be serious, the triage unit immediately dispatches an ambulance. Also, if the symptoms are mild, the triage unit makes an appointment to see the user at the nearest hospital. The triage unit, for example, uses AI to evaluate the user's symptoms and determine the urgency. The dispatch unit dispatches an ambulance, makes an appointment to see the user at the hospital, and provides first aid based on the results determined by the triage unit. For example, when dispatching an ambulance, the dispatch unit acquires the user's location information and dispatches the nearest ambulance. Furthermore, when making a reservation for a medical examination at the nearest hospital, the dispatching unit checks the availability of the hospital and makes the reservation. Furthermore, when providing first aid guidance, the dispatching unit provides voice guidance on specific first aid methods. This allows the emergency response system to provide a prompt and appropriate response in an emergency. Some or all of the above-mentioned processing in the dispatching unit may be performed using, for example, AI, or may be performed without using AI. For example, the dispatching unit can make arrangements using an AI model that inputs the results of the judgment by the triage unit and outputs arrangements for an ambulance, a hospital appointment, and first aid guidance.

[0030] The reception unit checks the situation according to the voice guidelines. For example, if the user complains that "my chest hurts," the reception unit asks a series of questions to check the details of the symptoms. At this time, the user only needs to answer by voice. The reception unit, for example, uses voice recognition technology to analyze the user's voice input and understands the answers to the questions. Specifically, the voice recognition technology converts the user's voice into text data and analyzes the text data to grasp the user's symptoms and situation. The voice recognition technology is equipped with a noise canceling function and a voice filtering function, and can remove ambient noise and clearly recognize the user's voice. Furthermore, the voice recognition technology can accurately analyze the voice by taking into account the user's pronunciation and accent differences. By using the voice recognition technology, the reception unit can quickly and efficiently collect information even in an emergency because the user can report the situation only by voice without using his or her hands. In addition, the reception unit can temporarily store the user's voice data and recheck it later. This allows the reception unit to accurately grasp the user's situation and provide basic information for appropriate response. Furthermore, when analyzing the user's voice data, the reception unit can use natural language processing technology to understand the user's intentions and emotions. For example, if the user says "it hurts a lot," the natural language processing technology analyzes the emphasis on "a lot" and determines that the degree of pain is strong. This allows the reception unit to grasp the user's situation in more detail and take appropriate action.

[0031] The triage department performs triage based on the information confirmed by the reception department. For example, if the user's symptoms are determined to be severe, the triage department immediately arranges for an ambulance. Also, if the symptoms are mild, the triage department makes an appointment to see the nearest hospital. The triage department, for example, uses AI to evaluate the user's symptoms and determine the urgency. Specifically, the AI ​​analyzes the user's voice data and text data and uses an algorithm to evaluate the severity of the symptoms. The AI ​​refers to past case data and medical databases and compares the user's symptoms to determine the urgency. For example, the AI ​​can extract information such as "chest pain" from the user's voice data and determine that there is a high possibility of myocardial infarction by comparing it with past case data. In addition, the AI ​​can take into account information such as the user's age, gender, and medical history to perform a more accurate urgency evaluation. By using AI, the triage department can quickly and accurately evaluate the user's symptoms and take appropriate measures. The triage department can also provide the results of the AI's urgency evaluation to medical experts for a final decision. This allows the triage department to combine AI and the knowledge of medical professionals to perform more reliable triage. Furthermore, if the user's symptoms change or new information is provided, the triage department can use AI to reassess and revise the response. This allows the triage department to always provide appropriate responses based on the latest information.

[0032] The arrangement unit arranges an ambulance, makes a hospital appointment, and provides first aid based on the results determined by the triage unit. For example, when arranging an ambulance, the arrangement unit acquires the user's location information and arranges the nearest ambulance. Specifically, the arrangement unit acquires location information from the user's smartphone or GPS device and links to an ambulance dispatch system. This allows the arrangement unit to accurately grasp the user's current location and arrange an ambulance that can arrive most quickly. In addition, when making an appointment to see the nearest hospital, the arrangement unit checks the availability of the hospital and makes a reservation. The arrangement unit links with the hospital's reservation system and can check the availability in real time. Furthermore, when providing first aid guidance, the arrangement unit provides audio guidance on specific first aid methods. For example, the arrangement unit provides audio guidance on first aid procedures according to the user's symptoms, and supports the user to provide appropriate first aid. A part or all of the above-mentioned processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the dispatching department can make arrangements using an AI model that inputs the results of the judgments made by the triage department and outputs information on dispatching an ambulance, making a hospital appointment, and providing first aid. The AI ​​model can suggest the optimal dispatching method based on past data and statistical information. This allows the dispatching department to make arrangements quickly and efficiently and provide appropriate responses in emergencies. Furthermore, the dispatching department can collect user feedback and use it to improve the dispatching process. For example, based on user feedback, the dispatching department can review the dispatching procedures and information, and make more effective dispatches. This allows the dispatching department to always provide optimal responses and ensure the safety and security of users in emergencies.

[0033] The emergency response system includes a management unit that manages information registered in advance. The management unit manages the information registered in advance. For example, when a user's medical history or allergy information is registered in advance, the management unit manages this information and provides it to the triage unit or the dispatch unit as necessary. The management unit manages user information, for example, using a database. The database includes the user's medical history, emergency contact information, allergy information, and the like. The management unit adds information to the database, for example, when the user registers new information. In addition, the management unit periodically updates the user's information and holds the latest information. This allows the emergency response system to provide a prompt and appropriate response based on the information registered in advance. A part or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can manage information using an AI model that inputs user information and adds information to the database.

[0034] The emergency response system includes an analysis unit that analyzes vital signs. The analysis unit analyzes the vital signs of a user. For example, the analysis unit analyzes vital signs such as the user's heart rate, blood pressure, and body temperature to evaluate the user's health condition. For example, the analysis unit measures the user's vital signs using a sensor and collects data. The collected data is analyzed by the analysis unit to evaluate the user's health condition. The analysis unit analyzes the vital sign data using, for example, AI to detect abnormalities. This allows the emergency response system to provide more accurate triage by analyzing the user's vital signs. A part 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 perform analysis using an AI model that inputs the user's vital sign data and detects abnormalities.

[0035] The emergency response system includes a reference unit that refers to a database of local medical institutions. The reference unit refers to the database of local medical institutions. For example, based on the user's location information, the reference unit identifies the nearest medical institution and makes an appointment to see the user. The reference unit, for example, uses a database to manage information on medical institutions. The database includes the location, medical department, contact information, and the like of the medical institution. For example, the reference unit acquires the user's location information and identifies the nearest medical institution. In addition, the reference unit checks the availability of the medical institution and makes an appointment. This allows the emergency response system to select the most suitable medical institution by referring to the database of local medical institutions. Part or all of the above-mentioned processing in the reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the reference unit can perform reference using an AI model that inputs the user's location information and identifies the nearest medical institution.

[0036] The reception unit can receive the user's voice input. The reception unit, for example, uses a voice recognition technology to analyze the user's voice input and understand the answer to the question. For example, if the user complains that "my chest hurts," the reception unit receives the voice input, asks a series of questions, and confirms the details of the symptoms. At this time, the user only needs to answer by voice. The reception unit, for example, uses a voice recognition technology to convert the user's voice into text data and performs analysis. As a result, the reception unit can quickly confirm the situation by accepting the user's voice input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can analyze the voice input using an AI model that inputs the user's voice data and converts it into text data.

[0037] The triage unit can perform triage based on the user's symptoms. The triage unit can use, for example, AI to evaluate the user's symptoms and determine the urgency. For example, if the user complains of "chest pain," the triage unit asks a series of questions to confirm the details of the symptoms. At this time, the user only needs to respond by voice. The triage unit can use, for example, AI to analyze the user's symptoms and evaluate the urgency. This allows the triage unit to triage based on the user's symptoms, making it possible to take appropriate action. Some or all of the above-mentioned processing in the triage unit can be performed using, for example, AI, or can be performed without using AI. For example, the triage unit can perform triage using an AI model that inputs the user's symptom data and evaluates the urgency.

[0038] The dispatch unit can dispatch an ambulance based on the result of the triage. For example, when dispatching an ambulance, the dispatch unit acquires the user's location information and dispatches the nearest ambulance. For example, if the user complains of "chest pain" and the triage unit determines that the user's condition is serious, the dispatch unit immediately dispatches an ambulance. The dispatch unit, for example, analyzes the user's location information using AI and identifies the nearest ambulance. As a result, the dispatch unit dispatches an ambulance based on the result of the triage, enabling rapid emergency response. A part or all of the above-mentioned processing in the dispatch unit may be performed using AI, for example, or may be performed without using AI. For example, the dispatch unit can dispatch an ambulance using an AI model that inputs the user's location information and identifies the nearest ambulance.

[0039] The arrangement unit can make a reservation for a medical examination at the nearest hospital based on the result of the triage. For example, when making a reservation for a medical examination at the nearest hospital, the arrangement unit checks the availability of the hospital and makes the reservation. For example, when a user complains of "chest pain" and the triage unit judges that the pain is mild, the arrangement unit makes a reservation for a medical examination at the nearest hospital. The arrangement unit, for example, uses AI to analyze the availability of hospitals and identifies the most suitable hospital. As a result, the arrangement unit makes a reservation for a medical examination at the nearest hospital based on the result of the triage, thereby enabling prompt medical care to be provided. A part or all of the above-mentioned processing in the arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the arrangement unit can make a reservation for a medical examination using an AI model that inputs hospital availability data and identifies the most suitable hospital.

[0040] The dispatching unit can provide guidance on first aid based on the results of the triage. For example, when providing guidance on first aid, the dispatching unit provides voice guidance on specific first aid methods. For example, when a user complains of "chest pain" and the triage unit determines that first aid is necessary, the dispatching unit provides voice guidance on specific first aid methods. The dispatching unit, for example, uses AI to analyze first aid methods and provides guidance to the user. As a result, the dispatching unit provides guidance on first aid based on the results of the triage, making it possible to provide appropriate first aid. A part or all of the above-mentioned processing in the dispatching unit may be performed, for example, using AI, or may be performed without using AI. For example, the dispatching unit can provide guidance using an AI model that inputs first aid method data and provides guidance on specific first aid methods.

[0041] The reception unit can analyze the user's past emergency response history and select the optimal question. The reception unit, for example, analyzes the user's past emergency response history and selects the optimal question. For example, if the user has experienced a heart attack in the past, the AI ​​prioritizes heart-related questions. Also, if the user has had an allergic reaction in the past, the AI ​​adds questions about allergies. Furthermore, if the user has not been able to calmly respond to an emergency in the past, the AI ​​selects simple and easy-to-understand questions. As a result, the reception unit can quickly and appropriately confirm the situation by selecting the optimal question based on the past emergency response history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can ask questions using an AI model that inputs the user's past response history data and selects the optimal question.

[0042] The reception unit can analyze the background sound of the user at the time of voice input to determine the urgency. The reception unit can analyze the background sound of the user at the time of voice input, for example, to determine the urgency. For example, when an ambulance siren is heard in the background sound, the AI ​​determines that the urgency is high and responds quickly. Also, when people's screams are heard in the background sound, the AI ​​determines that the urgency is high and immediately dispatches an ambulance. Furthermore, when a quiet environmental sound is heard in the background sound, the AI ​​determines that the urgency is low and asks detailed questions. This allows the reception unit to quickly determine a situation with a high urgency by analyzing the background sound. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can determine the urgency using an AI model that uses the user's voice data as input and analyzes the background sound.

[0043] The reception unit can add region-specific questions in consideration of the geographical location information of the user when inputting voice. The reception unit, for example, adds region-specific questions in consideration of the geographical location information of the user when inputting voice. For example, if the user is in a hot and humid area, the AI ​​adds questions about heat stroke. Also, if the user is in a cold area, the AI ​​adds questions about hypothermia. Furthermore, if the user is in an urban area, the AI ​​adds questions about traffic accidents. This allows the reception unit to ask questions that correspond to risks specific to the area by taking the geographical location information into consideration. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can ask questions using an AI model that takes the geographical location information of the user as input and adds region-specific questions.

[0044] The reception unit can analyze the user's social media activity and acquire related information when the voice is input. The reception unit can analyze the user's social media activity and acquire related information when the voice is input, for example. For example, if the user has recently posted about health on social media, the AI ​​asks questions based on that information. Also, if the user mentions a specific symptom on social media, the AI ​​adds a question about that symptom. Furthermore, if the user has posted about an emergency on social media, the AI ​​judges the urgency based on that information and makes arrangements. This allows the reception unit to ask questions according to the user's situation by analyzing social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can ask questions using an AI model that inputs the user's social media data and acquires related information.

[0045] The triage unit can improve the accuracy of triage by referring to the user's past medical history at the time of triage. The triage unit can improve the accuracy of triage by referring to the user's past medical history at the time of triage, for example. For example, if the user has had heart disease in the past, the AI ​​prioritizes heart-related triage. Also, if the user has had an allergic reaction in the past, the AI ​​adds triage related to allergies. Furthermore, if the user has not been able to respond calmly in an emergency in the past, the AI ​​performs triage that is simple and easy to understand. As a result, the triage unit improves the accuracy of triage by referring to the past medical history. Part or all of the above-mentioned processing in the triage unit may be performed, for example, using AI, or may be performed without using AI. For example, the triage unit can perform triage using an AI model that uses the user's past medical history data as input and improves the accuracy of triage.

[0046] The triage unit can make a decision taking into account the user's current living situation and environment during triage. The triage unit, for example, makes a decision taking into account the user's current living situation and environment during triage. For example, if the user is in a hot and humid area, the AI ​​performs triage taking into account the risk of heat stroke. Also, if the user is in a cold area, the AI ​​performs triage taking into account the risk of hypothermia. Furthermore, if the user is in an urban area, the AI ​​performs triage taking into account the risk of traffic accidents. This allows the triage unit to perform more appropriate triage by taking into account the current living situation and environment. Some or all of the above-mentioned processing in the triage unit may be performed, for example, using AI, or may be performed without using AI. For example, the triage unit can make a decision using an AI model that uses the user's living situation and environmental data as input and performs triage.

[0047] The triage unit can perform triage taking into account the geographical distribution of users at the time of triage. The triage unit, for example, performs triage taking into account the geographical distribution of users at the time of triage. For example, when a user is in a hot and humid area, the AI ​​performs triage taking into account the risk of heat stroke. Also, when a user is in a cold area, the AI ​​performs triage taking into account the risk of hypothermia. Furthermore, when a user is in an urban area, the AI ​​performs triage taking into account the risk of traffic accidents. As a result, the triage unit can perform triage corresponding to risks specific to the area by taking into account the geographical distribution. Part or all of the above-mentioned processing in the triage unit may be performed, for example, using AI, or may be performed without using AI. For example, the triage unit can make a judgment using an AI model that uses the geographical distribution data of users as input and performs triage.

[0048] The triage unit can improve the accuracy of triage by referring to the user's related literature during triage. The triage unit, for example, improves the accuracy of triage by referring to the user's related literature during triage. For example, the latest medical literature related to the user's symptoms is referred to to improve the accuracy of triage. Furthermore, literature related to the user's medical history is referred to to improve the accuracy of triage. Furthermore, literature related to the user's current living situation is referred to to improve the accuracy of triage. As a result, the triage unit improves the accuracy of triage by referring to the related literature. Part or all of the above-mentioned processing in the triage unit may be performed, for example, using AI, or may be performed without using AI. For example, the triage unit can perform triage using an AI model that uses the user's related literature data as input and improves the accuracy of triage.

[0049] The arrangement unit can select the optimal arrangement method by referring to the user's past emergency response history when making arrangements. For example, the arrangement unit selects the optimal arrangement method by referring to the user's past emergency response history when making arrangements. For example, if the user has experienced a heart attack in the past, the AI ​​prioritizes heart-related arrangements. Also, if the user has had an allergic reaction in the past, the AI ​​adds arrangements related to allergies. Furthermore, if the user has not been able to calmly respond to an emergency in the past, the AI ​​makes arrangements that are simple and easy to understand. This allows the arrangement unit to select the optimal arrangement method based on the past emergency response history, thereby enabling a quick and appropriate response. Part or all of the above-mentioned processing in the arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the arrangement unit can make arrangements using an AI model that inputs the user's past response history data and selects the optimal arrangement method.

[0050] The arrangement unit can make arrangements taking into account the user's current living situation and environment when making arrangements. The arrangement unit, for example, makes arrangements taking into account the user's current living situation and environment when making arrangements. For example, if the user is in a hot and humid area, the AI ​​makes arrangements taking into account the risk of heat stroke. Also, if the user is in a cold area, the AI ​​makes arrangements taking into account the risk of hypothermia. Furthermore, if the user is in an urban area, the AI ​​makes arrangements taking into account the risk of traffic accidents. This allows the arrangement unit to make more appropriate arrangements by taking into account the current living situation and environment. Part or all of the above-mentioned processing in the arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the arrangement unit can make arrangements using an AI model that inputs the user's living situation and environmental data and makes arrangements.

[0051] The arrangement unit can select the optimal arrangement method in consideration of the user's geographical location information at the time of arrangement. The arrangement unit, for example, selects the optimal arrangement method in consideration of the user's geographical location information at the time of arrangement. For example, if the user is in a hot and humid area, the AI ​​makes arrangements in consideration of the risk of heat stroke. Also, if the user is in a cold area, the AI ​​makes arrangements in consideration of the risk of hypothermia. Furthermore, if the user is in an urban area, the AI ​​makes arrangements in consideration of the risk of traffic accidents. As a result, the arrangement unit can make arrangements in response to risks specific to the area by considering the geographical location information. Part or all of the above-mentioned processing in the arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the arrangement unit can make arrangements using an AI model that inputs the user's geographical location information and selects the optimal arrangement method.

[0052] The dispatch unit can analyze the user's social media activity and suggest a means of dispatch at the time of dispatch. The dispatch unit, for example, analyzes the user's social media activity and suggests a means of dispatch at the time of dispatch. For example, if the user has recently posted about health on social media, the AI ​​makes dispatch based on that information. Also, if the user mentions a specific symptom on social media, the AI ​​adds dispatch related to that symptom. Furthermore, if the user has posted about an emergency on social media, the AI ​​judges the urgency based on that information and makes dispatch. In this way, the dispatch unit can make dispatch according to the user's situation by analyzing social media activity. Part or all of the above-mentioned processing in the dispatch unit may be performed, for example, using AI, or may be performed without using AI. For example, the dispatch unit can make dispatch using an AI model that inputs the user's social media data and suggests a means of dispatch.

[0053] The management unit can improve the accuracy of the registered information by referring to the user's past medical history during management. The management unit, for example, improves the accuracy of the registered information by referring to the user's past medical history during management. For example, if the user has had heart disease in the past, the AI ​​prioritizes managing heart-related information. Also, if the user has had an allergic reaction in the past, the AI ​​adds information about the allergy. Furthermore, if the user has not been able to respond calmly in an emergency in the past, the AI ​​manages simple and easy-to-understand information. As a result, the management unit improves the accuracy of the registered information by referring to the past medical history. Part or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can perform management using an AI model that uses the user's past medical history data as input and improves the accuracy of the registered information.

[0054] The management unit can optimize the registration information in consideration of the user's geographical location information at the time of management. The management unit optimizes the registration information in consideration of the user's geographical location information at the time of management, for example. For example, when the user is in a hot and humid area, the AI ​​optimizes the registration information in consideration of the risk of heat stroke. Also, when the user is in a cold area, the AI ​​optimizes the registration information in consideration of the risk of hypothermia. Furthermore, when the user is in an urban area, the AI ​​optimizes the registration information in consideration of the risk of traffic accidents. As a result, the management unit can manage information in response to risks specific to the area by considering the geographical location information. Part or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can perform management using an AI model that inputs the user's geographical location information and optimizes the registration information.

[0055] The analysis unit can improve the accuracy of the analysis by referring to the user's past vital sign data during the analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past vital sign data during the analysis. For example, if the user has had heart disease in the past, the AI ​​will prioritize analyzing heart-related vital sign data. Also, if the user has had an allergic reaction in the past, the AI ​​will add vital sign data related to allergies. Furthermore, if the user has not been able to respond calmly in an emergency in the past, the AI ​​will analyze vital sign data that is simple and easy to understand. As a result, the analysis unit improves the accuracy of the analysis by referring to the past vital sign data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that uses the user's past vital sign data as input and improves the accuracy of the analysis.

[0056] The analysis unit can analyze the vital signs taking into account the geographical location information of the user during the analysis. The analysis unit, for example, can analyze the vital signs taking into account the geographical location information of the user during the analysis. For example, when the user is in a hot and humid area, the AI ​​analyzes the vital signs taking into account the risk of heat stroke. Also, when the user is in a cold area, the AI ​​analyzes the vital signs taking into account the risk of hypothermia. Furthermore, when the user is in an urban area, the AI ​​analyzes the vital signs taking into account the risk of traffic accidents. This allows the analysis unit to analyze the vital signs in accordance with the risks specific to the area by taking into account the geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that uses the geographical location information of the user as an input and analyzes the vital signs.

[0057] The reference unit can select the optimal medical institution by referring to the user's past medical institution use history at the time of reference. The reference unit, for example, selects the optimal medical institution by referring to the user's past medical institution use history at the time of reference. For example, the optimal medical institution is selected based on the medical institution used by the user in the past. In addition, a medical institution that avoids congestion is selected from the user's past medical institution use history. Furthermore, the user's past medical institution use history is analyzed and the most efficient medical institution is selected. As a result, the reference unit can select the optimal medical institution by referring to the past medical institution use history. Part or all of the above-mentioned processing in the reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the reference unit can perform reference using an AI model that inputs the user's past medical institution use history data and selects the optimal medical institution.

[0058] The reference unit can select the optimal medical institution by considering the geographical location information of the user at the time of reference. The reference unit, for example, selects the optimal medical institution by considering the geographical location information of the user at the time of reference. For example, if the user is in a hot and humid area, the AI ​​selects the optimal medical institution by considering the risk of heat stroke. Also, if the user is in a cold area, the AI ​​selects the optimal medical institution by considering the risk of hypothermia. Furthermore, if the user is in an urban area, the AI ​​selects the optimal medical institution by considering the risk of traffic accidents. As a result, the reference unit can select a medical institution corresponding to the risk specific to the area by considering the geographical location information. Part or all of the above-mentioned processing in the reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the reference unit can perform reference using an AI model that inputs the geographical location information of the user and selects the optimal medical institution.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The emergency response system may further include a monitoring unit that monitors the user's health condition in real time. The monitoring unit continuously monitors the user's vital signs, and immediately notifies the triage unit if an abnormality is detected. For example, if the user's heart rate increases suddenly, the monitoring unit transmits the information to the triage unit to prompt a reassessment of the urgency. In addition, if the user's blood pressure reaches a dangerous level, the monitoring unit may automatically dispatch an ambulance. Furthermore, if the user's body temperature is abnormally high, the monitoring unit may warn of the risk of heatstroke and guide the user on appropriate first aid. This allows the emergency response system to respond more quickly and appropriately by monitoring the user's health condition in real time.

[0061] The emergency response system may further include a behavioral analysis unit that analyzes the user's behavioral history. The behavioral analysis unit analyzes the user's past behavioral patterns and optimizes emergency response. For example, if the user has experienced an emergency during a specific time period in the past, the behavioral analysis unit may pay special attention to that time period. Also, if the user frequently experiences emergency situations in a specific location, the risk at that location may be evaluated and appropriate measures may be taken. Furthermore, the behavioral analysis unit may take into account the user's lifestyle habits and provide preventive advice. This allows the emergency response system to provide a more effective emergency response based on the user's behavioral history.

[0062] The emergency response system may further include a statistical analysis unit that collects and anonymizes the health data of users and performs statistical analysis. The statistical analysis unit evaluates the health risks for each region based on the collected data and suggests preventive measures. For example, if the risk of heart disease is high in a particular region, preventive advice is provided to users in that region. Also, if the risk of influenza is high in a particular season, vaccination can be recommended. Furthermore, the statistical analysis unit may identify areas for improvement in the emergency response system based on the collected data and improve the accuracy of the system. This allows the emergency response system to evaluate the health risks for each region and suggest preventive measures, enabling more effective health management.

[0063] The emergency response system may further include a prediction unit that analyzes the user's lifestyle habits and predicts health risks. The prediction unit analyzes data such as the user's diet, exercise, and sleep, and predicts future health risks. For example, if the user continues to eat irregularly, the prediction unit evaluates the risk and suggests measures to improve. Also, if the user is not getting enough exercise, the prediction unit may recommend exercise. Furthermore, if the user is not getting enough sleep, the prediction unit suggests measures to improve sleep. In this way, the emergency response system can analyze the user's lifestyle habits and predict health risks, enabling preventive health management.

[0064] The emergency response system may further include an advice unit that provides individualized health advice based on the user's health data. The advice unit analyzes the user's vital signs and lifestyle data and provides individualized health advice. For example, if the user's blood pressure is high, the advice unit suggests improvements in diet and exercise. If the user's weight is increasing, the advice unit provides specific advice for weight loss. Furthermore, if the user's stress level is high, the advice unit suggests relaxation methods. This allows the emergency response system to provide individualized health advice based on the user's health data, enabling more effective health management.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The reception unit checks the situation according to the voice guidelines. For example, if the user complains of "chest pain," the reception unit will ask a series of questions to check the details of the symptoms. The user only needs to respond by voice. The reception unit uses voice recognition technology to analyze the user's voice input and understand the response to the questions. Step 2: The triage department performs triage based on the information confirmed by the reception department. For example, if the user's symptoms are determined to be severe, the triage department immediately arranges for an ambulance. If the symptoms are mild, the triage department makes an appointment to see the nearest hospital. The triage department uses AI to evaluate the user's symptoms and determine the urgency. Step 3: The dispatching unit dispatches an ambulance, makes a hospital appointment, and provides first aid based on the results of the determination made by the triage unit. For example, when dispatching an ambulance, the dispatching unit acquires the user's location information and dispatches the nearest ambulance. When making an appointment at the nearest hospital, the dispatching unit checks the hospital's availability and makes the reservation. Furthermore, when providing first aid guidance, the dispatching unit provides audio guidance on specific first aid methods.

[0067] (Example 2) The emergency response system according to the embodiment of the present invention is a system for determining whether to call an ambulance or perform first aid in an emergency. In this system, a user starts the system and checks the situation according to voice guidelines. Next, the system performs triage according to the situation, and if necessary, arranges an ambulance, makes an appointment to see the nearest hospital, and guides the first aid. In addition, based on information registered in advance, a faster and more appropriate response is possible. For example, if a user complains of "chest pain," the system asks a series of questions to confirm details of the symptoms. At this time, the user only needs to answer by voice. Next, the system performs triage according to the situation. For example, if the user's symptoms are determined to be serious, the system immediately arranges an ambulance. In addition, if the symptoms are mild, the system makes an appointment to see the nearest hospital. Furthermore, if first aid is required, the system guides the user by voice on specific first aid methods. In addition, based on information registered in advance, a faster and more appropriate response is possible. For example, if the user's medical history or allergy information is registered in advance, the system takes these information into consideration and performs appropriate response. This allows a faster and more appropriate response even in an emergency. For example, if a user complains of sudden chest pain, the system can quickly check the situation and dispatch an ambulance if necessary, enabling prompt medical care. This allows the emergency response system to provide prompt and appropriate responses in emergency situations.

[0068] The emergency response system according to the embodiment includes a reception unit, a triage unit, and a dispatch unit. The reception unit checks the situation according to the voice guidelines. For example, if the user complains that "my chest hurts," the reception unit asks a series of questions to check the details of the symptoms. At this time, the user only needs to answer by voice. The reception unit, for example, uses voice recognition technology to analyze the user's voice input and understands the answers to the questions. The triage unit performs triage based on the information confirmed by the reception unit. For example, if the user's symptoms are determined to be serious, the triage unit immediately dispatches an ambulance. Also, if the symptoms are mild, the triage unit makes an appointment to see the user at the nearest hospital. The triage unit, for example, uses AI to evaluate the user's symptoms and determine the urgency. The dispatch unit dispatches an ambulance, makes an appointment to see the user at the hospital, and provides first aid based on the results determined by the triage unit. For example, when dispatching an ambulance, the dispatch unit acquires the user's location information and dispatches the nearest ambulance. Furthermore, when making a reservation for a medical examination at the nearest hospital, the dispatching unit checks the availability of the hospital and makes the reservation. Furthermore, when providing first aid guidance, the dispatching unit provides voice guidance on specific first aid methods. This allows the emergency response system to provide a prompt and appropriate response in an emergency. Some or all of the above-mentioned processing in the dispatching unit may be performed using, for example, AI, or may be performed without using AI. For example, the dispatching unit can make arrangements using an AI model that inputs the results of the judgment by the triage unit and outputs arrangements for an ambulance, a hospital appointment, and first aid guidance.

[0069] The reception unit checks the situation according to the voice guidelines. For example, if the user complains that "my chest hurts," the reception unit asks a series of questions to check the details of the symptoms. At this time, the user only needs to answer by voice. The reception unit, for example, uses voice recognition technology to analyze the user's voice input and understands the answers to the questions. Specifically, the voice recognition technology converts the user's voice into text data and analyzes the text data to grasp the user's symptoms and situation. The voice recognition technology is equipped with a noise canceling function and a voice filtering function, and can remove ambient noise and clearly recognize the user's voice. Furthermore, the voice recognition technology can accurately analyze the voice by taking into account the user's pronunciation and accent differences. By using the voice recognition technology, the reception unit can quickly and efficiently collect information even in an emergency because the user can report the situation only by voice without using his or her hands. In addition, the reception unit can temporarily store the user's voice data and recheck it later. This allows the reception unit to accurately grasp the user's situation and provide basic information for appropriate response. Furthermore, when analyzing the user's voice data, the reception unit can use natural language processing technology to understand the user's intentions and emotions. For example, if the user says "it hurts a lot," the natural language processing technology analyzes the emphasis on "a lot" and determines that the degree of pain is strong. This allows the reception unit to grasp the user's situation in more detail and take appropriate action.

[0070] The triage department performs triage based on the information confirmed by the reception department. For example, if the user's symptoms are determined to be severe, the triage department immediately arranges for an ambulance. Also, if the symptoms are mild, the triage department makes an appointment to see the nearest hospital. The triage department, for example, uses AI to evaluate the user's symptoms and determine the urgency. Specifically, the AI ​​analyzes the user's voice data and text data and uses an algorithm to evaluate the severity of the symptoms. The AI ​​refers to past case data and medical databases and compares the user's symptoms to determine the urgency. For example, the AI ​​can extract information such as "chest pain" from the user's voice data and determine that there is a high possibility of myocardial infarction by comparing it with past case data. In addition, the AI ​​can take into account information such as the user's age, gender, and medical history to perform a more accurate urgency evaluation. By using AI, the triage department can quickly and accurately evaluate the user's symptoms and take appropriate measures. The triage department can also provide the results of the AI's urgency evaluation to medical experts for a final decision. This allows the triage department to combine AI and the knowledge of medical professionals to perform more reliable triage. Furthermore, if the user's symptoms change or new information is provided, the triage department can use AI to reassess and revise the response. This allows the triage department to always provide appropriate responses based on the latest information.

[0071] The arrangement unit arranges an ambulance, makes a hospital appointment, and provides first aid based on the results determined by the triage unit. For example, when arranging an ambulance, the arrangement unit acquires the user's location information and arranges the nearest ambulance. Specifically, the arrangement unit acquires location information from the user's smartphone or GPS device and links to an ambulance dispatch system. This allows the arrangement unit to accurately grasp the user's current location and arrange an ambulance that can arrive most quickly. In addition, when making an appointment to see the nearest hospital, the arrangement unit checks the availability of the hospital and makes a reservation. The arrangement unit links with the hospital's reservation system and can check the availability in real time. Furthermore, when providing first aid guidance, the arrangement unit provides audio guidance on specific first aid methods. For example, the arrangement unit provides audio guidance on first aid procedures according to the user's symptoms, and supports the user to provide appropriate first aid. A part or all of the above-mentioned processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the dispatching department can make arrangements using an AI model that inputs the results of the judgments made by the triage department and outputs information on dispatching an ambulance, making a hospital appointment, and providing first aid. The AI ​​model can suggest the optimal dispatching method based on past data and statistical information. This allows the dispatching department to make arrangements quickly and efficiently and provide appropriate responses in emergencies. Furthermore, the dispatching department can collect user feedback and use it to improve the dispatching process. For example, based on user feedback, the dispatching department can review the dispatching procedures and information, and make more effective dispatches. This allows the dispatching department to always provide optimal responses and ensure the safety and security of users in emergencies.

[0072] The emergency response system includes a management unit that manages information registered in advance. The management unit manages the information registered in advance. For example, when a user's medical history or allergy information is registered in advance, the management unit manages this information and provides it to the triage unit or the dispatch unit as necessary. The management unit manages user information, for example, using a database. The database includes the user's medical history, emergency contact information, allergy information, and the like. The management unit adds information to the database, for example, when the user registers new information. In addition, the management unit periodically updates the user's information and holds the latest information. This allows the emergency response system to provide a prompt and appropriate response based on the information registered in advance. A part or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can manage information using an AI model that inputs user information and adds information to the database.

[0073] The emergency response system includes an analysis unit that analyzes vital signs. The analysis unit analyzes the vital signs of a user. For example, the analysis unit analyzes vital signs such as the user's heart rate, blood pressure, and body temperature to evaluate the user's health condition. For example, the analysis unit measures the user's vital signs using a sensor and collects data. The collected data is analyzed by the analysis unit to evaluate the user's health condition. The analysis unit analyzes the vital sign data using, for example, AI to detect abnormalities. This allows the emergency response system to provide more accurate triage by analyzing the user's vital signs. A part 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 perform analysis using an AI model that inputs the user's vital sign data and detects abnormalities.

[0074] The emergency response system includes a reference unit that refers to a database of local medical institutions. The reference unit refers to the database of local medical institutions. For example, based on the user's location information, the reference unit identifies the nearest medical institution and makes an appointment to see the user. The reference unit, for example, uses a database to manage information on medical institutions. The database includes the location, medical department, contact information, and the like of the medical institution. For example, the reference unit acquires the user's location information and identifies the nearest medical institution. In addition, the reference unit checks the availability of the medical institution and makes an appointment. This allows the emergency response system to select the most suitable medical institution by referring to the database of local medical institutions. Part or all of the above-mentioned processing in the reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the reference unit can perform reference using an AI model that inputs the user's location information and identifies the nearest medical institution.

[0075] The reception unit can receive the user's voice input. The reception unit, for example, uses a voice recognition technology to analyze the user's voice input and understand the answer to the question. For example, if the user complains that "my chest hurts," the reception unit receives the voice input, asks a series of questions, and confirms the details of the symptoms. At this time, the user only needs to answer by voice. The reception unit, for example, uses a voice recognition technology to convert the user's voice into text data and performs analysis. As a result, the reception unit can quickly confirm the situation by accepting the user's voice input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can analyze the voice input using an AI model that inputs the user's voice data and converts it into text data.

[0076] The triage unit can perform triage based on the user's symptoms. The triage unit can use, for example, AI to evaluate the user's symptoms and determine the urgency. For example, if the user complains of "chest pain," the triage unit asks a series of questions to confirm the details of the symptoms. At this time, the user only needs to respond by voice. The triage unit can use, for example, AI to analyze the user's symptoms and evaluate the urgency. This allows the triage unit to triage based on the user's symptoms, making it possible to take appropriate action. Some or all of the above-mentioned processing in the triage unit can be performed using, for example, AI, or can be performed without using AI. For example, the triage unit can perform triage using an AI model that inputs the user's symptom data and evaluates the urgency.

[0077] The dispatch unit can dispatch an ambulance based on the result of the triage. For example, when dispatching an ambulance, the dispatch unit acquires the user's location information and dispatches the nearest ambulance. For example, if the user complains of "chest pain" and the triage unit determines that the user's condition is serious, the dispatch unit immediately dispatches an ambulance. The dispatch unit, for example, analyzes the user's location information using AI and identifies the nearest ambulance. As a result, the dispatch unit dispatches an ambulance based on the result of the triage, enabling rapid emergency response. A part or all of the above-mentioned processing in the dispatch unit may be performed using AI, for example, or may be performed without using AI. For example, the dispatch unit can dispatch an ambulance using an AI model that inputs the user's location information and identifies the nearest ambulance.

[0078] The arrangement unit can make a reservation for a medical examination at the nearest hospital based on the result of the triage. For example, when making a reservation for a medical examination at the nearest hospital, the arrangement unit checks the availability of the hospital and makes the reservation. For example, when a user complains of "chest pain" and the triage unit judges that the pain is mild, the arrangement unit makes a reservation for a medical examination at the nearest hospital. The arrangement unit, for example, uses AI to analyze the availability of hospitals and identifies the most suitable hospital. As a result, the arrangement unit makes a reservation for a medical examination at the nearest hospital based on the result of the triage, thereby enabling prompt medical care to be provided. A part or all of the above-mentioned processing in the arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the arrangement unit can make a reservation for a medical examination using an AI model that inputs hospital availability data and identifies the most suitable hospital.

[0079] The dispatching unit can provide guidance on first aid based on the results of the triage. For example, when providing guidance on first aid, the dispatching unit provides voice guidance on specific first aid methods. For example, when a user complains of "chest pain" and the triage unit determines that first aid is necessary, the dispatching unit provides voice guidance on specific first aid methods. The dispatching unit, for example, uses AI to analyze first aid methods and provides guidance to the user. As a result, the dispatching unit provides guidance on first aid based on the results of the triage, making it possible to provide appropriate first aid. A part or all of the above-mentioned processing in the dispatching unit may be performed, for example, using AI, or may be performed without using AI. For example, the dispatching unit can provide guidance using an AI model that inputs first aid method data and provides guidance on specific first aid methods.

[0080] The reception unit can estimate the user's emotions and adjust the order and content of questions based on the estimated user's emotions. The reception unit, for example, estimates the user's emotions and adjusts the order and content of questions based on the estimated user's emotions. For example, if the user is in a panicked state, the AI ​​starts with simple questions in a calm voice and gradually moves to more detailed questions. Also, if the user is calm, the AI ​​quickly asks detailed questions to quickly grasp the situation. Furthermore, if the user is confused, the AI ​​repeats the questions and provides additional explanations to help understanding. This allows the reception unit to adjust the order and content of questions according to the user's emotions, thereby enabling more appropriate situation confirmation. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can adjust the order and content of questions using an AI model that uses the user's voice data as input and estimates emotions.

[0081] The reception unit can analyze the user's past emergency response history and select the optimal question. The reception unit, for example, analyzes the user's past emergency response history and selects the optimal question. For example, if the user has experienced a heart attack in the past, the AI ​​prioritizes heart-related questions. Also, if the user has had an allergic reaction in the past, the AI ​​adds questions about allergies. Furthermore, if the user has not been able to calmly respond to an emergency in the past, the AI ​​selects simple and easy-to-understand questions. As a result, the reception unit can quickly and appropriately confirm the situation by selecting the optimal question based on the past emergency response history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can ask questions using an AI model that inputs the user's past response history data and selects the optimal question.

[0082] The reception unit can analyze the background sound of the user at the time of voice input to determine the urgency. The reception unit can analyze the background sound of the user at the time of voice input, for example, to determine the urgency. For example, when an ambulance siren is heard in the background sound, the AI ​​determines that the urgency is high and responds quickly. Also, when people's screams are heard in the background sound, the AI ​​determines that the urgency is high and immediately dispatches an ambulance. Furthermore, when a quiet environmental sound is heard in the background sound, the AI ​​determines that the urgency is low and asks detailed questions. This allows the reception unit to quickly determine a situation with a high urgency by analyzing the background sound. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can determine the urgency using an AI model that uses the user's voice data as input and analyzes the background sound.

[0083] The reception unit can estimate the user's emotion and adjust the tone and speed of the question based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the tone and speed of the question based on the estimated user's emotion. For example, if the user is nervous, the AI ​​asks questions slowly in a calm tone. Also, if the user is relaxed, the AI ​​asks questions in a bright tone to quickly collect information. Furthermore, if the user is in a hurry, the AI ​​asks questions in a quick tone to quickly collect necessary information. This allows the reception unit to adjust the tone and speed of the question according to the user's emotion, making it possible to more appropriately confirm the situation. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can adjust the tone and speed of the question using an AI model that uses the user's voice data as input and estimates the emotion.

[0084] The reception unit can add region-specific questions in consideration of the geographical location information of the user when inputting voice. The reception unit, for example, adds region-specific questions in consideration of the geographical location information of the user when inputting voice. For example, if the user is in a hot and humid area, the AI ​​adds questions about heat stroke. Also, if the user is in a cold area, the AI ​​adds questions about hypothermia. Furthermore, if the user is in an urban area, the AI ​​adds questions about traffic accidents. This allows the reception unit to ask questions that correspond to risks specific to the area by taking the geographical location information into consideration. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can ask questions using an AI model that takes the geographical location information of the user as input and adds region-specific questions.

[0085] The reception unit can analyze the user's social media activity and acquire related information when the voice is input. The reception unit can analyze the user's social media activity and acquire related information when the voice is input, for example. For example, if the user has recently posted about health on social media, the AI ​​asks questions based on that information. Also, if the user mentions a specific symptom on social media, the AI ​​adds a question about that symptom. Furthermore, if the user has posted about an emergency on social media, the AI ​​judges the urgency based on that information and makes arrangements. This allows the reception unit to ask questions according to the user's situation by analyzing social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can ask questions using an AI model that inputs the user's social media data and acquires related information.

[0086] The triage unit can estimate the user's emotions and adjust the triage criteria based on the estimated user's emotions. The triage unit, for example, estimates the user's emotions and adjusts the triage criteria based on the estimated user's emotions. For example, if the user is in a panic state, the AI ​​sets the urgency high and responds quickly. Also, if the user is calm, the AI ​​collects detailed information and performs accurate triage. Furthermore, if the user is confused, the AI ​​starts with simple questions and gradually collects detailed information. This allows the triage unit to adjust the triage criteria according to the user's emotions, thereby enabling more appropriate triage. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the triage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the triage unit can perform triage using an AI model that takes a user's emotional data as input and adjusts the triage criteria.

[0087] The triage unit can improve the accuracy of triage by referring to the user's past medical history at the time of triage. The triage unit can improve the accuracy of triage by referring to the user's past medical history at the time of triage, for example. For example, if the user has had heart disease in the past, the AI ​​prioritizes heart-related triage. Also, if the user has had an allergic reaction in the past, the AI ​​adds triage related to allergies. Furthermore, if the user has not been able to respond calmly in an emergency in the past, the AI ​​performs triage that is simple and easy to understand. As a result, the triage unit improves the accuracy of triage by referring to the past medical history. Part or all of the above-mentioned processing in the triage unit may be performed, for example, using AI, or may be performed without using AI. For example, the triage unit can perform triage using an AI model that uses the user's past medical history data as input and improves the accuracy of triage.

[0088] The triage unit can make a decision taking into account the user's current living situation and environment during triage. The triage unit, for example, makes a decision taking into account the user's current living situation and environment during triage. For example, if the user is in a hot and humid area, the AI ​​performs triage taking into account the risk of heat stroke. Also, if the user is in a cold area, the AI ​​performs triage taking into account the risk of hypothermia. Furthermore, if the user is in an urban area, the AI ​​performs triage taking into account the risk of traffic accidents. This allows the triage unit to perform more appropriate triage by taking into account the current living situation and environment. Some or all of the above-mentioned processing in the triage unit may be performed, for example, using AI, or may be performed without using AI. For example, the triage unit can make a decision using an AI model that uses the user's living situation and environmental data as input and performs triage.

[0089] The triage unit can estimate the user's emotions and adjust the order in which the triage results are displayed based on the estimated user's emotions. The triage unit, for example, estimates the user's emotions and adjusts the order in which the triage results are displayed based on the estimated user's emotions. For example, if the user is nervous, the AI ​​displays the most important information first. Also, if the user is relaxed, the AI ​​displays detailed information in an orderly manner. Furthermore, if the user is in a hurry, the AI ​​quickly displays information that covers the main points. This allows the triage unit to provide more appropriate information by adjusting the order in which the triage results are displayed according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the triage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the triage unit can display using an AI model that inputs the user's emotion data and adjusts the order in which the triage results are displayed.

[0090] The triage unit can perform triage taking into account the geographical distribution of users at the time of triage. The triage unit, for example, performs triage taking into account the geographical distribution of users at the time of triage. For example, when a user is in a hot and humid area, the AI ​​performs triage taking into account the risk of heat stroke. Also, when a user is in a cold area, the AI ​​performs triage taking into account the risk of hypothermia. Furthermore, when a user is in an urban area, the AI ​​performs triage taking into account the risk of traffic accidents. As a result, the triage unit can perform triage corresponding to risks specific to the area by taking into account the geographical distribution. Part or all of the above-mentioned processing in the triage unit may be performed, for example, using AI, or may be performed without using AI. For example, the triage unit can make a judgment using an AI model that uses the geographical distribution data of users as input and performs triage.

[0091] The triage unit can improve the accuracy of triage by referring to the user's related literature during triage. The triage unit, for example, improves the accuracy of triage by referring to the user's related literature during triage. For example, the latest medical literature related to the user's symptoms is referred to to improve the accuracy of triage. Furthermore, literature related to the user's medical history is referred to to improve the accuracy of triage. Furthermore, literature related to the user's current living situation is referred to to improve the accuracy of triage. As a result, the triage unit improves the accuracy of triage by referring to the related literature. Part or all of the above-mentioned processing in the triage unit may be performed, for example, using AI, or may be performed without using AI. For example, the triage unit can perform triage using an AI model that uses the user's related literature data as input and improves the accuracy of triage.

[0092] The dispatch unit can estimate the user's emotion and determine the priority of dispatch based on the estimated user's emotion. The dispatch unit, for example, estimates the user's emotion and determines the priority of dispatch based on the estimated user's emotion. For example, if the user is in a panic state, the AI ​​prioritizes dispatching an ambulance. Also, if the user is calm, the AI ​​collects detailed information and makes optimal dispatch. Furthermore, if the user is confused, the AI ​​starts with simple dispatch and gradually makes detailed dispatch. This enables the dispatch unit to respond more appropriately by determining the priority of dispatch according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dispatch unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dispatch unit can make dispatches using an AI model that inputs the user's emotion data and determines the priority of dispatches.

[0093] The arrangement unit can select the optimal arrangement method by referring to the user's past emergency response history when making arrangements. For example, the arrangement unit selects the optimal arrangement method by referring to the user's past emergency response history when making arrangements. For example, if the user has experienced a heart attack in the past, the AI ​​prioritizes heart-related arrangements. Also, if the user has had an allergic reaction in the past, the AI ​​adds arrangements related to allergies. Furthermore, if the user has not been able to calmly respond to an emergency in the past, the AI ​​makes arrangements that are simple and easy to understand. This allows the arrangement unit to select the optimal arrangement method based on the past emergency response history, thereby enabling a quick and appropriate response. Part or all of the above-mentioned processing in the arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the arrangement unit can make arrangements using an AI model that inputs the user's past response history data and selects the optimal arrangement method.

[0094] The arrangement unit can make arrangements taking into account the user's current living situation and environment when making arrangements. The arrangement unit, for example, makes arrangements taking into account the user's current living situation and environment when making arrangements. For example, if the user is in a hot and humid area, the AI ​​makes arrangements taking into account the risk of heat stroke. Also, if the user is in a cold area, the AI ​​makes arrangements taking into account the risk of hypothermia. Furthermore, if the user is in an urban area, the AI ​​makes arrangements taking into account the risk of traffic accidents. This allows the arrangement unit to make more appropriate arrangements by taking into account the current living situation and environment. Part or all of the above-mentioned processing in the arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the arrangement unit can make arrangements using an AI model that inputs the user's living situation and environmental data and makes arrangements.

[0095] The dispatch unit can estimate the user's emotion and adjust the dispatch method based on the estimated user's emotion. The dispatch unit, for example, estimates the user's emotion and adjusts the dispatch method based on the estimated user's emotion. For example, if the user is nervous, the AI ​​makes the dispatch in a calm voice. Also, if the user is relaxed, the AI ​​makes the dispatch in a bright voice. Furthermore, if the user is in a hurry, the AI ​​makes the dispatch quickly and concisely. This allows the dispatch unit to adjust the dispatch method according to the user's emotion, thereby enabling a more appropriate response. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dispatch unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the dispatch unit can make the dispatch using an AI model that inputs the user's emotion data and adjusts the dispatch method.

[0096] The arrangement unit can select the optimal arrangement method in consideration of the user's geographical location information at the time of arrangement. The arrangement unit, for example, selects the optimal arrangement method in consideration of the user's geographical location information at the time of arrangement. For example, if the user is in a hot and humid area, the AI ​​makes arrangements in consideration of the risk of heat stroke. Also, if the user is in a cold area, the AI ​​makes arrangements in consideration of the risk of hypothermia. Furthermore, if the user is in an urban area, the AI ​​makes arrangements in consideration of the risk of traffic accidents. As a result, the arrangement unit can make arrangements in response to risks specific to the area by considering the geographical location information. Part or all of the above-mentioned processing in the arrangement unit may be performed, for example, using AI, or may be performed without using AI. For example, the arrangement unit can make arrangements using an AI model that inputs the user's geographical location information and selects the optimal arrangement method.

[0097] The dispatch unit can analyze the user's social media activity and suggest a means of dispatch at the time of dispatch. The dispatch unit, for example, analyzes the user's social media activity and suggests a means of dispatch at the time of dispatch. For example, if the user has recently posted about health on social media, the AI ​​makes dispatch based on that information. Also, if the user mentions a specific symptom on social media, the AI ​​adds dispatch related to that symptom. Furthermore, if the user has posted about an emergency on social media, the AI ​​judges the urgency based on that information and makes dispatch. In this way, the dispatch unit can make dispatch according to the user's situation by analyzing social media activity. Part or all of the above-mentioned processing in the dispatch unit may be performed, for example, using AI, or may be performed without using AI. For example, the dispatch unit can make dispatch using an AI model that inputs the user's social media data and suggests a means of dispatch.

[0098] The management unit can estimate the user's emotions and adjust the management method of the registered information based on the estimated user's emotions. The management unit, for example, estimates the user's emotions and adjusts the management method of the registered information based on the estimated user's emotions. For example, when the user is nervous, the AI ​​provides a simple and highly visible interface to facilitate management of the registered information. Also, when the user is relaxed, the AI ​​provides detailed information to enhance management of the registered information. Furthermore, when the user is in a hurry, the AI ​​enables the user to quickly manage the registered information. This allows the management unit to adjust the management method of the registered information according to the user's emotions, thereby enabling more appropriate information management. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the management unit can perform management using an AI model that inputs the user's emotion data and adjusts the management method of the registered information.

[0099] The management unit can improve the accuracy of the registered information by referring to the user's past medical history during management. The management unit, for example, improves the accuracy of the registered information by referring to the user's past medical history during management. For example, if the user has had heart disease in the past, the AI ​​prioritizes managing heart-related information. Also, if the user has had an allergic reaction in the past, the AI ​​adds information about the allergy. Furthermore, if the user has not been able to respond calmly in an emergency in the past, the AI ​​manages simple and easy-to-understand information. As a result, the management unit improves the accuracy of the registered information by referring to the past medical history. Part or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can perform management using an AI model that uses the user's past medical history data as input and improves the accuracy of the registered information.

[0100] The management unit can estimate the user's emotions and adjust the update frequency of the registered information based on the estimated user's emotions. The management unit, for example, estimates the user's emotions and adjusts the update frequency of the registered information based on the estimated user's emotions. For example, when the user is nervous, the AI ​​sets the update frequency of the registered information low to reduce the burden on the user. In addition, when the user is relaxed, the AI ​​updates detailed information frequently. Furthermore, when the user is in a hurry, the AI ​​enables the registered information to be updated quickly. This enables the management unit to adjust the update frequency of the registered information according to the user's emotions, thereby enabling more appropriate information management. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the management unit can perform management using an AI model that inputs the user's emotion data and adjusts the update frequency of the registered information.

[0101] The management unit can optimize the registration information in consideration of the user's geographical location information at the time of management. The management unit optimizes the registration information in consideration of the user's geographical location information at the time of management, for example. For example, when the user is in a hot and humid area, the AI ​​optimizes the registration information in consideration of the risk of heat stroke. Also, when the user is in a cold area, the AI ​​optimizes the registration information in consideration of the risk of hypothermia. Furthermore, when the user is in an urban area, the AI ​​optimizes the registration information in consideration of the risk of traffic accidents. As a result, the management unit can manage information in response to risks specific to the area by considering the geographical location information. Part or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can perform management using an AI model that inputs the user's geographical location information and optimizes the registration information.

[0102] The analysis unit can estimate the user's emotion and adjust the analysis method of the vital signs based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the analysis method of the vital signs based on the estimated user's emotion. For example, if the user is nervous, the AI ​​quickly analyzes the vital signs and provides the results promptly. Also, if the user is relaxed, the AI ​​performs a detailed analysis and provides the results carefully. Furthermore, if the user is in a hurry, the AI ​​performs a concise and quick analysis and provides the results immediately. This enables the analysis unit to adjust the analysis method of the vital signs according to the user's emotion, thereby enabling more appropriate analysis. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can perform analysis using an AI model that inputs the user's emotion data and adjusts the analysis method of the vital signs.

[0103] The analysis unit can improve the accuracy of the analysis by referring to the user's past vital sign data during the analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past vital sign data during the analysis. For example, if the user has had heart disease in the past, the AI ​​will prioritize analyzing heart-related vital sign data. Also, if the user has had an allergic reaction in the past, the AI ​​will add vital sign data related to allergies. Furthermore, if the user has not been able to respond calmly in an emergency in the past, the AI ​​will analyze vital sign data that is simple and easy to understand. As a result, the analysis unit improves the accuracy of the analysis by referring to the past vital sign data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that uses the user's past vital sign data as input and improves the accuracy of the analysis.

[0104] The analysis unit can estimate the user's emotion and adjust the method of displaying the analysis result of the vital signs based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the method of displaying the analysis result of the vital signs based on the estimated user's emotion. For example, if the user is nervous, the AI ​​provides a simple and highly visible display method. Also, if the user is relaxed, the AI ​​provides a display method including detailed information. Furthermore, if the user is in a hurry, the AI ​​provides a display method that focuses on the main points. This allows the analysis unit to adjust the display method of the analysis result according to the user's emotion, thereby making it possible to provide more appropriate information. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can display using an AI model that inputs the user's emotion data and adjusts the method of displaying the analysis result.

[0105] The analysis unit can analyze the vital signs taking into account the geographical location information of the user during the analysis. The analysis unit, for example, can analyze the vital signs taking into account the geographical location information of the user during the analysis. For example, when the user is in a hot and humid area, the AI ​​analyzes the vital signs taking into account the risk of heat stroke. Also, when the user is in a cold area, the AI ​​analyzes the vital signs taking into account the risk of hypothermia. Furthermore, when the user is in an urban area, the AI ​​analyzes the vital signs taking into account the risk of traffic accidents. This allows the analysis unit to analyze the vital signs in accordance with the risks specific to the area by taking into account the geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that uses the geographical location information of the user as an input and analyzes the vital signs.

[0106] The reference unit can estimate the user's emotion and adjust the reference method of the medical institution database based on the estimated user's emotion. The reference unit, for example, estimates the user's emotion and adjusts the reference method of the medical institution database based on the estimated user's emotion. For example, when the user is nervous, the AI ​​provides a simple and highly visible interface to facilitate the reference of the medical institution database. Also, when the user is relaxed, the AI ​​provides detailed information to enrich the reference of the medical institution database. Furthermore, when the user is in a hurry, the AI ​​enables the user to quickly refer to the medical institution database. As a result, the reference unit can adjust the reference method of the medical institution database according to the user's emotion, thereby enabling more appropriate information to be provided. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reference unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reference unit can perform reference using an AI model that inputs the user's emotion data and adjusts the reference method of the medical institution database.

[0107] The reference unit can select the optimal medical institution by referring to the user's past medical institution use history at the time of reference. The reference unit, for example, selects the optimal medical institution by referring to the user's past medical institution use history at the time of reference. For example, the optimal medical institution is selected based on the medical institution used by the user in the past. In addition, a medical institution that avoids congestion is selected from the user's past medical institution use history. Furthermore, the user's past medical institution use history is analyzed and the most efficient medical institution is selected. As a result, the reference unit can select the optimal medical institution by referring to the past medical institution use history. Part or all of the above-mentioned processing in the reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the reference unit can perform reference using an AI model that inputs the user's past medical institution use history data and selects the optimal medical institution.

[0108] The reference unit can estimate the user's emotion and adjust the reference frequency of the medical institution database based on the estimated user's emotion. The reference unit, for example, estimates the user's emotion and adjusts the reference frequency of the medical institution database based on the estimated user's emotion. For example, when the user is nervous, the AI ​​sets the reference frequency of the medical institution database low to reduce the burden on the user. Also, when the user is relaxed, the AI ​​frequently refers to detailed information. Furthermore, when the user is in a hurry, the AI ​​allows the user to quickly refer to the medical institution database. This allows the reference unit to provide more appropriate information by adjusting the reference frequency according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reference unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reference unit can perform reference using an AI model that inputs the user's emotion data and adjusts the reference frequency of the medical institution database.

[0109] The reference unit can select the optimal medical institution by considering the geographical location information of the user at the time of reference. The reference unit, for example, selects the optimal medical institution by considering the geographical location information of the user at the time of reference. For example, if the user is in a hot and humid area, the AI ​​selects the optimal medical institution by considering the risk of heat stroke. Also, if the user is in a cold area, the AI ​​selects the optimal medical institution by considering the risk of hypothermia. Furthermore, if the user is in an urban area, the AI ​​selects the optimal medical institution by considering the risk of traffic accidents. As a result, the reference unit can select a medical institution corresponding to the risk specific to the area by considering the geographical location information. Part or all of the above-mentioned processing in the reference unit may be performed, for example, using AI, or may be performed without using AI. For example, the reference unit can perform reference using an AI model that inputs the geographical location information of the user and selects the optimal medical institution.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The emergency response system may further include a monitoring unit that monitors the user's health condition in real time. The monitoring unit continuously monitors the user's vital signs, and immediately notifies the triage unit if an abnormality is detected. For example, if the user's heart rate increases suddenly, the monitoring unit transmits the information to the triage unit to prompt a reassessment of the urgency. In addition, if the user's blood pressure reaches a dangerous level, the monitoring unit may automatically dispatch an ambulance. Furthermore, if the user's body temperature is abnormally high, the monitoring unit may warn of the risk of heatstroke and guide the user on appropriate first aid. This allows the emergency response system to respond more quickly and appropriately by monitoring the user's health condition in real time.

[0112] The emergency response system may further include a behavioral analysis unit that analyzes the user's behavioral history. The behavioral analysis unit analyzes the user's past behavioral patterns and optimizes emergency response. For example, if the user has experienced an emergency during a specific time period in the past, the behavioral analysis unit may pay special attention to that time period. Also, if the user frequently experiences emergency situations in a specific location, the risk at that location may be evaluated and appropriate measures may be taken. Furthermore, the behavioral analysis unit may take into account the user's lifestyle habits and provide preventive advice. This allows the emergency response system to provide a more effective emergency response based on the user's behavioral history.

[0113] The emergency response system can further estimate the user's emotions and adjust the first aid guidance based on the estimated emotions. For example, if the user is in a panic, the system will explain first aid slowly in a calm voice. If the user is calm, the system will quickly provide detailed first aid procedures. If the user is confused, the system will start with simple procedures and gradually move to more detailed explanations. This allows the emergency response system to provide more effective support by adjusting the first aid guidance according to the user's emotions.

[0114] The emergency response system can further estimate the user's emotions and adjust the method of notifying the triage result based on the estimated emotions. For example, if the user is nervous, the system provides a simple and highly visible notification method. If the user is relaxed, the system provides a notification method including detailed information. Furthermore, if the user is in a hurry, the system provides a quick notification method that focuses on the main points. This allows the emergency response system to provide more appropriate information by adjusting the method of notifying the triage result according to the user's emotions.

[0115] The emergency response system can further estimate the user's emotions and adjust the communication method when dispatching an ambulance based on the estimated emotions. For example, if the user is in a panic, the system will explain the procedure until the ambulance arrives in a calm voice. If the user is calm, the system will quickly provide detailed instructions. If the user is confused, the system will start with simple instructions and gradually move to more detailed instructions. This allows the emergency response system to provide more effective support by adjusting the communication method when dispatching an ambulance according to the user's emotions.

[0116] The emergency response system may further include a statistical analysis unit that collects and anonymizes the health data of users and performs statistical analysis. The statistical analysis unit evaluates the health risks for each region based on the collected data and suggests preventive measures. For example, if the risk of heart disease is high in a particular region, preventive advice is provided to users in that region. Also, if the risk of influenza is high in a particular season, vaccination can be recommended. Furthermore, the statistical analysis unit may identify areas for improvement in the emergency response system based on the collected data and improve the accuracy of the system. This allows the emergency response system to evaluate the health risks for each region and suggest preventive measures, enabling more effective health management.

[0117] The emergency response system may further include a prediction unit that analyzes the user's lifestyle habits and predicts health risks. The prediction unit analyzes data such as the user's diet, exercise, and sleep, and predicts future health risks. For example, if the user continues to eat irregularly, the prediction unit evaluates the risk and suggests measures to improve. Also, if the user is not getting enough exercise, the prediction unit may recommend exercise. Furthermore, if the user is not getting enough sleep, the prediction unit suggests measures to improve sleep. In this way, the emergency response system can analyze the user's lifestyle habits and predict health risks, enabling preventive health management.

[0118] The emergency response system can further estimate the user's emotions and select a medical institution based on the estimated emotions. For example, if the user is nervous, the system preferentially selects a medical institution that provides a relaxing environment. If the user is relaxed, the system selects a medical institution that can respond quickly. If the user is in a hurry, the system selects the nearest medical institution. This allows the emergency response system to provide more appropriate medical care by selecting a medical institution according to the user's emotions.

[0119] The emergency response system may further include an advice unit that provides individualized health advice based on the user's health data. The advice unit analyzes the user's vital signs and lifestyle data and provides individualized health advice. For example, if the user's blood pressure is high, the advice unit suggests improvements in diet and exercise. If the user's weight is increasing, the advice unit provides specific advice for weight loss. Furthermore, if the user's stress level is high, the advice unit suggests relaxation methods. This allows the emergency response system to provide individualized health advice based on the user's health data, enabling more effective health management.

[0120] The emergency response system can further estimate the user's emotions and adjust the emergency response method based on the estimated emotions. For example, if the user is in a panic, the system will explain the response method slowly in a calm voice. If the user is calm, the system will quickly provide detailed response methods. Furthermore, if the user is confused, the system will start with simple steps and gradually move to detailed explanations. This allows the emergency response system to provide more effective support by adjusting the emergency response method according to the user's emotions.

[0121] The process flow of the second embodiment will be briefly described below.

[0122] Step 1: The reception unit checks the situation according to the voice guidelines. For example, if the user complains of "chest pain," the reception unit will ask a series of questions to check the details of the symptoms. The user only needs to respond by voice. The reception unit uses voice recognition technology to analyze the user's voice input and understand the response to the questions. Step 2: The triage department performs triage based on the information confirmed by the reception department. For example, if the user's symptoms are determined to be severe, the triage department immediately arranges for an ambulance. If the symptoms are mild, the triage department makes an appointment to see the nearest hospital. The triage department uses AI to evaluate the user's symptoms and determine the urgency. Step 3: The dispatching unit dispatches an ambulance, makes a hospital appointment, and provides first aid based on the results of the determination made by the triage unit. For example, when dispatching an ambulance, the dispatching unit acquires the user's location information and dispatches the nearest ambulance. When making an appointment at the nearest hospital, the dispatching unit checks the hospital's availability and makes the reservation. Furthermore, when providing first aid guidance, the dispatching unit provides audio guidance on specific first aid methods.

[0123] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0124] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0125] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements including the reception unit, triage unit, dispatch unit, management unit, analysis unit, and reference unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and analyzes the voice input of the user. The triage unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the user's symptoms. The dispatch unit is realized, for example, by the control unit 46A of the smart device 14, and arranges an ambulance and makes a hospital appointment. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and manages the user's information. The analysis unit is realized, for example, by the control unit 46A of the smart device 14, and analyzes vital signs. The reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and refers to a database of a local medical institution. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0130] 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.

[0131] 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 the voice according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0133] 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 performed in a secure state.

[0134] Fig. 4 shows an example of 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.

[0135] 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.

[0136] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0139] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0140] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements including the reception unit, triage unit, dispatch unit, management unit, analysis unit, and reference unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and analyzes the user's voice input. The triage unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the user's symptoms. The dispatch unit is realized, for example, by the control unit 46A of the smart glasses 214, and arranges an ambulance and makes a hospital appointment. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and manages the user's information. The analysis unit is realized, for example, by the control unit 46A of the smart glasses 214, and analyzes vital signs. The reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and refers to the database of a local medical institution. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0146] 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.

[0147] 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 the voice according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0149] 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 performed in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] 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.

[0152] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0153] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0154] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0155] 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 voice indicating a user input for 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.

[0156] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0157] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements including the reception unit, triage unit, dispatch unit, management unit, analysis unit, and reference unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and analyzes the voice input of the user. The triage unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the user's symptoms. The dispatch unit is realized by the control unit 46A of the headset type terminal 314, and arranges an ambulance and makes a hospital appointment. The management unit is realized by the specific processing unit 290 of the data processing device 12, and manages the user's information. The analysis unit is realized by the control unit 46A of the headset type terminal 314, and analyzes vital signs. The reference unit is realized by the specific processing unit 290 of the data processing device 12, and refers to the database of a local medical institution. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0162] 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.

[0163] 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 the voice according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0165] 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 performed in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0167] 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.

[0168] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0170] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0172] 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 a voice indicating a user input for 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.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0174] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0175] Each of the multiple elements including the reception unit, triage unit, dispatch unit, management unit, analysis unit, and reference unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and analyzes the voice input of the user. The triage unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the user's symptoms. The dispatch unit is realized, for example, by the control unit 46A of the robot 414, and arranges an ambulance and makes a hospital appointment. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and manages the user's information. The analysis unit is realized, for example, by the control unit 46A of the robot 414, and analyzes vital signs. The reference unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and refers to the database of a local medical institution. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0177] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0179] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0181] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0183] 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.

[0184] 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 Universal Serial Bus (USB) 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.

[0185] In addition, 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 upon request from the data processing device 12.

[0186] 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.

[0187] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0188] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0189] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0190] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0192] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0194] (Appendix 1) A receptionist confirms the situation according to the voice guidelines, a triage unit that performs triage based on the information confirmed by the reception unit; and a dispatch unit that dispatches an ambulance, makes a hospital appointment, or provides first aid based on the result of the judgment made by the triage unit. A system characterized by: (Appendix 2) Equipped with a management section that manages pre-registered information 2. The system of claim 1, (Appendix 3) Equipped with an analysis unit that analyzes vital signs 2. The system of claim 1, (Appendix 4) Equipped with a reference section that can be used to access databases of local medical institutions 2. The system of claim 1, (Appendix 5) The reception unit is Accepts user voice input 2. The system of claim 1, (Appendix 6) The triage section includes: Triage users based on their symptoms 2. The system of claim 1, (Appendix 7) The arrangement department Arrange for an ambulance based on the triage results 2. The system of claim 1, (Appendix 8) The arrangement department Based on the triage results, appointments are made at the nearest hospital. 2. The system of claim 1, (Appendix 9) The arrangement department Providing first aid guidance based on triage results 2. The system of claim 1, (Appendix 10) The reception unit is Infer user sentiment and adjust question order and content based on the inferred sentiment 2. The system of claim 1, (Appendix 11) The reception unit is Analyze the user's past emergency response history and select the most appropriate questions 2. The system of claim 1, (Appendix 12) The reception unit is When inputting voice, the system analyzes the user's background sounds to determine the urgency of the situation. 2. The system of claim 1, (Appendix 13) The reception unit is Infer user sentiment and adjust the tone and pace of questions based on the inferred sentiment 2. The system of claim 1, (Appendix 14) The reception unit is Add location-specific questions to voice input by taking into account the user's geographic location 2. The system of claim 1, (Appendix 15) The reception unit is Analyzes your social media activity and retrieves relevant information when you speak 2. The system of claim 1, (Appendix 16) The triage section includes: Infer user sentiment and adjust triage criteria based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The triage section includes: During triage, the user's past medical history can be referenced to improve the accuracy of triage. 2. The system of claim 1, (Appendix 18) The triage section includes: When triaging, make decisions based on the user’s current living situation and environment. 2. The system of claim 1, (Appendix 19) The triage section includes: Infer user sentiment and adjust the order in which triage results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 20) The triage section includes: When triaging, take into account the geographical distribution of users. 2. The system of claim 1, (Appendix 21) The triage section includes: During triage, improve the accuracy of triage by referring to the user's related literature 2. The system of claim 1, (Appendix 22) The arrangement department To estimate a user's emotion and determine a priority of dispatches based on the estimated user's emotion. 2. The system of claim 1, (Appendix 23) The arrangement department When making arrangements, the system selects the optimal arrangement method by referring to the user's past emergency response history. 2. The system of claim 1, (Appendix 24) The arrangement department When making arrangements, the arrangements are made taking into account the user's current living situation and environment. 2. The system of claim 1, (Appendix 25) The arrangement department To estimate a user's emotion and adjust a method of dispatch based on the estimated user's emotion. 2. The system of claim 1, (Appendix 26) The arrangement department When making a delivery, the optimal delivery method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 27) The arrangement department When making a delivery, the system analyzes the user's social media activity to suggest delivery methods. 2. The system of claim 1, (Appendix 28) The management unit To estimate a user's emotion and adjust a method of managing registration information based on the estimated user's emotion. 3. The system of claim 2, (Appendix 29) The management unit During administration, the accuracy of registered information is improved by referencing the user's past medical history. 3. The system of claim 2, (Appendix 30) The management unit Inferring a user's emotions and adjusting the frequency of updating registration information based on the estimated user's emotions 3. The system of claim 2, (Appendix 31) The management unit During administration, optimize registration information by taking into account the geographic location of the user 3. The system of claim 2, (Appendix 32) The analysis unit is Inferring a user's emotions and adjusting a vital sign analysis method based on the estimated user's emotions 4. The system of claim 3. (Appendix 33) The analysis unit is During analysis, the accuracy of the analysis is improved by referencing the user's past vital sign data. 4. The system of claim 3. (Appendix 34) The analysis unit is Infer a user's emotions and adjust how vital signs analysis results are displayed based on the inferred user emotions 4. The system of claim 3. (Appendix 35) The analysis unit is During analysis, the user's geographic location is taken into account when analyzing vital signs. 4. The system of claim 3. (Appendix 36) The reference portion is To estimate a user's emotion and adjust a method of referring to a medical institution database based on the estimated user's emotion. 5. The system of claim 4. (Appendix 37) The reference portion is At the time of reference, the most suitable medical institution is selected by referring to the user's past medical institution usage history. 5. The system of claim 4. (Appendix 38) The reference portion is To estimate a user's emotion and adjust a frequency of reference to a medical institution database based on the estimated user's emotion. 5. The system of claim 4. (Appendix 39) The reference portion is At the time of referral, the most suitable medical institution is selected taking into account the user's geographic location information. 5. The system of claim 4. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A receptionist confirms the situation according to the voice guidelines, a triage unit that performs triage based on the information confirmed by the reception unit; and a dispatch unit that dispatches an ambulance, makes a hospital appointment, or provides first aid based on the result of the judgment made by the triage unit. A system characterized by:

2. Equipped with a management section that manages pre-registered information 2. The system of claim 1 .

3. Equipped with an analysis unit that analyzes vital signs 2. The system of claim 1 .

4. Equipped with a reference section that can be used to access databases of local medical institutions 2. The system of claim 1 .

5. The reception unit is Accepts user voice input 2. The system of claim 1 .

6. The triage section includes: Triage users based on their symptoms 2. The system of claim 1 .

7. The arrangement department Arrange for an ambulance based on the triage results 2. The system of claim 1 .

8. The arrangement department Based on the triage results, appointments are made at the nearest hospital.

2. The system of claim 1 .

9. The arrangement department Providing first aid guidance based on triage results 2. The system of claim 1 .

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