system

Abstract

To provide a system that enables rapid and proper response during sudden medical emergencies.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, a communication unit, and a notification unit. The reception unit receives a condition or an image during a sudden medical emergency. The analysis unit analyzes the information received by the reception unit and presents treatment advice. The communication unit links with GPS to identify an available hospital based on the information obtained by the analysis unit and contacts the hospital. The notification unit performs automatic notification to a family member or acquaintance based on the information obtained by the communication 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] With conventional technology, there is a risk that appropriate treatment, selection of hospital, and notification to family members may not be carried out promptly in the event of a sudden illness.

[0005] The system according to the embodiment aims to provide a prompt and appropriate response in the event of an emergency illness. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a communication unit, and a notification unit. The reception unit receives the condition or images at the time of sudden illness. The analysis unit analyzes the information received by the reception unit and presents treatment advice. The communication unit, based on the information obtained by the analysis unit, selects hospitals that can accept the patient in conjunction with GPS and contacts them. The notification unit automatically notifies family members or acquaintances based on the information obtained by the communication unit. Effect of the Invention

[0007] The system according to the embodiment can respond quickly and appropriately in the event of a sudden illness. [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 the 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 hotline system according to the embodiment of the present invention is a system that utilizes a generation AI to provide an appropriate response to emergency medical care in the event of a sudden illness, even to people with little specialized knowledge. In this system, the condition and images of the sudden illness are read into the generation AI, which analyzes the information and presents appropriate treatment advice as an initial response. Next, the system works in conjunction with GPS to pick up hospitals that can accept the patient and contact them. In addition, the system is equipped with an automatic contact notification function for family and acquaintances. This series of steps prevents confusion in the event of a sudden illness and enables a quick and appropriate response. For example, the condition and images of the sudden illness are read into the generation AI. For example, if a patient suddenly experiences chest pain or is injured, the symptoms and images are photographed with a smartphone and input into the generation AI. The generation AI analyzes the input information and presents appropriate treatment advice as an initial response. For example, if the chest pain indicates a possible heart attack, the generation AI provides advice such as "sit down and rest immediately and call an ambulance." Next, the generation AI works in conjunction with GPS to pick up hospitals that can accept the patient and contact them. For example, the generation AI can identify the current location, search for the nearest emergency hospital, and contact that hospital, allowing for rapid acceptance. In addition, the generation AI is equipped with an automatic notification function to family and acquaintances. For example, if a person who suddenly falls ill is alone, the generation AI will automatically contact family and acquaintances who have been registered in advance. It will send a message such as, "Mr. / Ms. XX has suddenly fallen ill and needs emergency treatment. We are currently on our way to XX Hospital." This series of steps prevents confusion in the event of a sudden illness and enables rapid and appropriate response. By utilizing the generation AI, we provide a system that allows even people with little specialized knowledge to respond to emergencies with peace of mind. This allows the emergency hotline system to prevent confusion in the event of a sudden illness and enable rapid and appropriate response.

[0029] The emergency hotline system according to the embodiment includes a reception unit, an analysis unit, a communication unit, and a notification unit. The reception unit receives the condition and image of the sudden illness. The condition of the sudden illness includes, but is not limited to, for example, a heart attack, a stroke, and difficulty in breathing. The image includes, but is not limited to, for example, a photograph, an X-ray image, and a CT scan image. The reception unit can receive, for example, an image taken by a smartphone. The reception unit can also analyze the condition and image of the sudden illness using a generation AI. The analysis unit analyzes the information received by the reception unit using the generation AI and presents appropriate treatment advice. For example, the generation AI provides advice such as "sit down and rest immediately and call an ambulance" when there is a possibility of a heart attack based on the input information. The generation AI can generate appropriate treatment advice using a text generation AI (for example, LLM). The generation AI can also analyze images and text using a multimodal generation AI to provide appropriate treatment advice. The contact unit, based on the information obtained by the analysis unit, works in conjunction with GPS to pick up a hospital that can accept the patient and contact the hospital. For example, the generation AI identifies the current location, searches for the nearest emergency hospital, and contacts the hospital, allowing for rapid acceptance. The contact unit can use the generation AI to pick up a hospital that can accept the patient and contact the hospital. The notification unit automatically notifies family and acquaintances based on the information obtained by the communication unit. For example, if a person who has suddenly fallen ill is alone, the generation AI automatically contacts family and acquaintances who have been registered in advance. A message such as "Mr. / Ms. XX has suddenly fallen ill and needs emergency treatment. He / she is currently on his / her way to XX Hospital" is sent. The notification unit can automatically notify family and acquaintances using the generation AI. As a result, the emergency hotline system according to the embodiment can prevent confusion in the event of a sudden illness and enable rapid and appropriate response.

[0030] The reception unit receives the condition and image at the time of sudden illness. The condition at the time of sudden illness includes, but is not limited to, a heart attack, a stroke, and difficulty in breathing. The image includes, but is not limited to, a photograph, an X-ray image, a CT scan image, and the like. The reception unit can receive, for example, an image taken by a smartphone. Specifically, the camera function of the smartphone can be used to take a picture of the condition of the suddenly ill person and transmit the image to the system. This allows the reception unit to quickly grasp the condition of the suddenly ill person and start an appropriate response. The reception unit can also analyze the condition and image at the time of sudden illness using a generation AI. The generation AI can detect abnormalities from the transmitted image using, for example, image recognition technology and evaluate the condition of the suddenly ill person. For example, if there is a sign of a heart attack, the generation AI can analyze an image of the chest and detect abnormal heartbeat patterns and changes in blood flow. Furthermore, the generation AI can use text analysis technology to analyze the symptoms and condition entered by the suddenly ill person and provide appropriate treatment advice. This allows the reception unit to quickly and accurately grasp the condition of the suddenly ill person and start an appropriate response.

[0031] The analysis unit uses the generation AI to analyze the information received by the reception unit and presents appropriate treatment advice. For example, based on the input information, the generation AI provides advice such as "sit down and rest immediately and call an ambulance" if there is a possibility of a heart attack. The generation AI can generate appropriate treatment advice using a text generation AI (e.g., LLM). Specifically, the generation AI analyzes the symptoms and condition of the suddenly ill person and generates optimal treatment advice based on past data and medical knowledge. For example, if the suddenly ill person complains of chest pain, the generation AI evaluates the possibility of a heart attack and provides appropriate treatment advice. The generation AI can also analyze images and text using a multimodal generation AI to provide appropriate treatment advice. For example, it analyzes images of the suddenly ill person's chest to detect abnormal heart rate patterns and changes in blood flow, and provides appropriate treatment advice based on that. This allows the analysis unit to quickly and accurately analyze the condition of the suddenly ill person and provide appropriate treatment advice. In addition, the analysis unit can also perform long-term risk assessments and trend analysis by utilizing past data and medical knowledge. For example, based on past data on sudden illnesses, it can predict risk fluctuations for specific symptoms or conditions and devise future countermeasures. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The communication unit, based on the information obtained by the analysis unit, works in conjunction with GPS to pick up hospitals that can accept the patient and contact them. For example, the generation AI can identify the current location, search for the nearest emergency hospital, and contact that hospital, allowing for rapid acceptance. Specifically, the communication unit uses GPS to identify the current location of the person who is suddenly ill, and searches for the nearest emergency hospital based on that information. The generation AI selects the most suitable hospital, taking into consideration the hospital's acceptance situation and available medical equipment. For example, if there is a suspicion of a heart attack, the generation AI preferentially selects a hospital with specialized cardiac medical equipment. The communication unit automatically contacts the selected hospital and informs it of the condition of the person who is suddenly ill and the necessary response. This allows the hospital to quickly prepare for response, and the acceptance of the person who is suddenly ill is smooth. In addition, the communication unit can use the generation AI to pick up hospitals that can accept the patient and contact them. This allows the communication unit to quickly select the most suitable medical institution according to the condition of the person who is suddenly ill and contact them. In addition, the liaison department updates information on hospital admission status and available medical equipment in real time, allowing it to select the most suitable medical institution based on the latest information at all times. This allows the liaison department to promptly and appropriately admit patients with sudden illnesses, improving the efficiency and effectiveness of emergency responses.

[0033] The notification unit automatically notifies family and acquaintances based on the information obtained by the contact unit. For example, if the suddenly ill person is alone, the generation AI automatically contacts family and acquaintances registered in advance. It sends a message such as "Mr. / Ms. XX has suddenly fallen ill and needs emergency treatment. He / she is currently on his / her way to XX Hospital." Specifically, the notification unit automatically contacts family and acquaintances registered in advance based on the condition of the suddenly ill person, his / her current location, and the hospital information of the hospital where he / she will be accepted. The generation AI refers to the contact list of the suddenly ill person, selects an appropriate means of contact (e.g., telephone, SMS, email, etc.), and sends the message. This allows family and acquaintances to quickly grasp the situation of the suddenly ill person and take the necessary measures. In addition, the notification unit can automatically notify family and acquaintances using the generation AI. For example, when the suddenly ill person arrives at the hospital, it can send a message such as "Mr. / Ms. XX has arrived at XX Hospital. He / she is currently being examined." This allows family and acquaintances to grasp the situation of the suddenly ill person in real time and respond with peace of mind. Furthermore, the notification unit can automatically notify family and acquaintances using the generation AI even if the condition of the suddenly ill person changes. For example, if the condition of a person with a sudden illness worsens, a message such as "Mr. / Ms. X's condition has worsened. Emergency treatment is currently being performed" can be sent. This allows the notification unit to inform family and acquaintances of the condition of the person with a sudden illness in real time, helping them respond quickly and appropriately.

[0034] The reception unit can load the condition or images at the time of sudden illness into the generation AI. The reception unit, for example, loads the condition and images at the time of sudden illness into the generation AI. The generation AI can analyze the condition and images at the time of sudden illness using a deep learning model, a natural language generation model, or the like. For example, the generation AI can provide appropriate treatment advice based on the condition and images at the time of sudden illness. This makes it possible to quickly collect information by loading the condition and images at the time of sudden illness into the generation AI.

[0035] The analysis unit can analyze information using the generation AI and present appropriate treatment advice as an initial action. The analysis unit can analyze information using the generation AI, for example, and present appropriate treatment advice as an initial action. The generation AI can provide appropriate treatment advice based on the input information. For example, in the case of a possible heart attack, the generation AI provides advice such as "sit down and rest immediately and call an ambulance." The generation AI can generate appropriate treatment advice using a text generation AI (for example, LLM). The generation AI can also analyze images and text using a multimodal generation AI and provide appropriate treatment advice. This enables a rapid response by analyzing information using the generation AI and presenting appropriate treatment advice as an initial action.

[0036] The communication unit can work in conjunction with GPS to pick up hospitals that can accept the patient and contact them. The communication unit can, for example, work in conjunction with GPS to pick up hospitals that can accept the patient and contact them. The generation AI can identify the current location, search for the nearest emergency response hospital, and contact that hospital, making it possible for the patient to be accepted quickly. The generation AI can pick up hospitals that can accept the patient and contact them based on the method of acquiring location information and details of the linked system. This allows the generation AI to work in conjunction with GPS to pick up hospitals that can accept the patient and contact them, making it possible for the patient to be accepted quickly.

[0037] The notification unit can automatically send contact notifications to family and acquaintances. The notification unit, for example, sends automatic contact notifications to family and acquaintances. The generation AI can automatically contact family and acquaintances registered in advance. For example, it can send a message such as "Mr. / Ms. XX has suddenly fallen ill and requires emergency treatment. He / she is currently on his / her way to XX Hospital." The generation AI can automatically send contact notifications by phone, email, SMS, or other methods. This enables rapid information sharing by automatically sending contact notifications to family and acquaintances.

[0038] The reception unit can analyze the user's past health data and select the acquisition method. The reception unit, for example, analyzes the user's past health data and selects the optimal acquisition method. The generation AI can analyze the user's past health data using electronic medical records, health check results, wearable device data, etc. For example, if the user has experienced a heart attack in the past, the generation AI preferentially acquires heart-related data. Also, if the user has had an allergic reaction in the past, the generation AI acquires allergy-related data. Furthermore, based on the user's past health data, the generation AI suggests the optimal image acquisition method. In this way, the optimal information acquisition method can be selected by analyzing the user's past health data.

[0039] The reception unit can perform filtering based on the user's current activity status and environment when acquiring the state of the user's sudden illness or the image. The reception unit performs filtering based on the user's current activity status and environment when acquiring the state of the user's sudden illness or the image, for example. The generation AI can grasp the user's current activity status and environment using GPS data, sensor data, user input information, etc. For example, when the user is exercising, the generation AI preferentially acquires data related to exercise. In addition, when the user is at home, the generation AI acquires data related to accidents in the home. In addition, when the user is out, the generation AI acquires data related to the external environment. As a result, more relevant information can be acquired by filtering information based on the user's current activity status and environment.

[0040] The reception unit can preferentially acquire highly relevant information in consideration of the geographical location information of the user when acquiring the state of the user's sudden illness or an image. For example, the reception unit preferentially acquires highly relevant information in consideration of the geographical location information of the user when acquiring the state of the user's sudden illness or an image. The generation AI can acquire the user's geographical location information using GPS data or a location information service. For example, when the user is in an urban area, the generation AI preferentially acquires emergency response information in the urban area. Also, when the user is in the suburbs, the generation AI preferentially acquires emergency response information in the suburbs. Furthermore, when the user is traveling, the generation AI preferentially acquires emergency response information at the travel destination. In this way, highly relevant information can be preferentially acquired by considering the geographical location information of the user.

[0041] The reception unit can analyze the user's social media activity when acquiring the state or image at the time of sudden illness, and acquire related information. The reception unit can analyze the user's social media activity when acquiring the state or image at the time of sudden illness, and acquire related information. The generation AI can analyze the social media posting content and activity history. For example, if the user posts about health on social media, the generation AI acquires that information. Also, if the user mentions a specific symptom on social media, the generation AI acquires that information. Furthermore, the user's social media activity can be analyzed to acquire related health information. As a result, related information can be acquired by analyzing the user's social media activity.

[0042] The analysis unit can adjust the level of detail of the advice based on the severity of the symptoms of the sudden illness during analysis. The analysis unit, for example, adjusts the level of detail of the advice based on the severity of the symptoms of the sudden illness during analysis. The generation AI can adjust the level of detail of the advice based on the type and progression of the symptoms of the sudden illness. For example, in the case of mild symptoms, the generation AI provides simple advice. In addition, in the case of moderate symptoms, the generation AI provides detailed advice. Furthermore, in the case of severe symptoms, the generation AI provides advice for emergency responses. In this way, by adjusting the level of detail of the advice based on the severity of the symptoms of the sudden illness, more appropriate advice can be provided.

[0043] The analysis unit can apply different analysis algorithms depending on the type of sudden illness during analysis. The analysis unit, for example, applies different analysis algorithms depending on the type of sudden illness during analysis. The generation AI can apply different analysis algorithms depending on the type of sudden illness, such as a heart attack, stroke, or difficulty in breathing. For example, in the case of a heart-related sudden illness, the generation AI applies an analysis algorithm specialized for the heart. In addition, in the case of a respiratory-related sudden illness, the generation AI applies an analysis algorithm specialized for the respiratory system. In addition, in the case of a trauma-related sudden illness, the generation AI applies an analysis algorithm specialized for trauma. In this way, by applying different analysis algorithms depending on the type of sudden illness, more accurate analysis is possible.

[0044] The analysis unit can determine the priority of advice based on the time of occurrence of the sudden illness during analysis. The analysis unit, for example, determines the priority of advice based on the time of occurrence of the sudden illness during analysis. The generation AI can identify the time of occurrence of the sudden illness based on a timestamp and the situation at the time of occurrence. For example, if the sudden illness occurs at night, the generation AI will preferentially provide advice for nighttime situations. Also, if the sudden illness occurs on a holiday, the generation AI will preferentially provide advice for holiday situations. Furthermore, if the sudden illness occurs on a weekday, the generation AI will preferentially provide advice for weekday situations. In this way, by determining the priority of advice based on the time of occurrence of the sudden illness, more appropriate advice can be provided.

[0045] The analysis unit can adjust the order of advice based on the relevance of the sudden illness during analysis. The analysis unit, for example, adjusts the order of advice based on the relevance of the sudden illness during analysis. The generation AI can evaluate the relevance of the sudden illness based on the similarity of symptoms and past data. For example, in the case of a heart-related sudden illness, the generation AI provides heart-related advice first. Also, in the case of a respiratory-related sudden illness, the generation AI provides respiratory-related advice first. Furthermore, in the case of a trauma-related sudden illness, the generation AI provides trauma-related advice first. In this way, by adjusting the order of advice based on the relevance of the sudden illness, more appropriate advice can be provided.

[0046] The communication unit can check the hospital's acceptance status in real time at the time of contact and select the most suitable hospital. The communication unit, for example, checks the hospital's acceptance status in real time at the time of contact and selects the most suitable hospital. The generation AI can check the hospital's real-time acceptance status and available bed status. For example, the generation AI checks the hospital's real-time acceptance status and selects a hospital that can accept the patient. The generation AI also checks the hospital's available bed status and selects the most suitable hospital. Furthermore, the generation AI checks the hospital's emergency response capabilities and selects the most suitable hospital. In this way, the hospital's acceptance status can be checked in real time and the most suitable hospital can be selected.

[0047] The communication unit can select an appropriate hospital based on the hospital's specialty at the time of contact. The communication unit, for example, selects an appropriate hospital based on the hospital's specialty at the time of contact. The generation AI can select an appropriate hospital based on the hospital's specialty. For example, in the case of a heart-related sudden illness, the generation AI selects a hospital specializing in heart disease. In addition, in the case of a respiratory-related sudden illness, the generation AI selects a hospital specializing in respiratory disease. Furthermore, in the case of a trauma-related sudden illness, the generation AI selects a hospital specializing in trauma. In this way, by selecting an appropriate hospital based on the hospital's specialty, more specialized treatment can be received.

[0048] The communication unit can select the optimal hospital by taking into consideration the geographical distribution of hospitals when making a contact. For example, the communication unit selects the optimal hospital by taking into consideration the geographical distribution of hospitals when making a contact. The generation AI can consider the geographical distribution of hospitals by using map data or location information services. For example, the generation AI considers the geographical distribution of hospitals and selects the nearest hospital. In addition, the generation AI considers the geographical distribution of hospitals and selects a hospital with good access. In addition, the generation AI considers the geographical distribution of hospitals and selects a hospital with good transportation access. In this way, the optimal hospital can be selected by taking into consideration the geographical distribution of hospitals.

[0049] The communication unit can select the most suitable hospital by referring to the hospital's past acceptance record when contacting the hospital. For example, the communication unit selects the most suitable hospital by referring to the hospital's past acceptance record when contacting the hospital. The generation AI can refer to the past acceptance record based on the hospital's historical data and number of acceptances. For example, the generation AI refers to the hospital's past acceptance record and selects a hospital with a high acceptance record. In addition, the generation AI refers to the hospital's past acceptance record and selects a hospital with a track record of emergency response. In addition, the generation AI refers to the hospital's past acceptance record and selects a hospital with high expertise. In this way, the most suitable hospital can be selected by referring to the hospital's past acceptance record.

[0050] The notification unit can analyze the contact information of family members and acquaintances at the time of notification and select the optimal notification method. For example, the notification unit analyzes the contact information of family members and acquaintances at the time of notification and selects the optimal notification method. The generation AI can select the optimal notification method based on contact information such as phone numbers, email addresses, and SNS accounts. For example, the generation AI analyzes the contact information of family members and acquaintances and selects the method that allows the quickest contact. The generation AI also analyzes the contact information of family members and acquaintances and selects the optimal contact method in the event of an emergency. Furthermore, the generation AI analyzes the contact information of family members and acquaintances and combines multiple contact methods to send notifications. In this way, the optimal notification method can be selected by analyzing the contact information of family members and acquaintances.

[0051] The notification unit can provide different notification contents depending on the type of sudden illness at the time of notification. The notification unit, for example, provides different notification contents depending on the type of sudden illness at the time of notification. The generation AI can provide different notification contents depending on the type of sudden illness, such as a heart attack, stroke, or difficulty in breathing. For example, in the case of a heart-related sudden illness, the generation AI provides notification contents specialized for the heart. Also, in the case of a respiratory-related sudden illness, the generation AI provides notification contents specialized for the respiratory system. Furthermore, in the case of a trauma-related sudden illness, the generation AI provides notification contents specialized for the trauma. This allows for more appropriate notifications by providing different notification contents depending on the type of sudden illness.

[0052] The notification unit can select the optimal notification method by taking into consideration the geographical location information of family members and acquaintances at the time of notification. For example, the notification unit selects the optimal notification method by taking into consideration the geographical location information of family members and acquaintances at the time of notification. The generation AI can select the optimal notification method based on the geographical location information. For example, the generation AI takes into consideration the geographical location information of family members and acquaintances and selects the method that allows for the quickest contact. In addition, the generation AI takes into consideration the geographical location information of family members and acquaintances and selects the optimal contact method in the event of an emergency. Furthermore, the generation AI takes into consideration the geographical location information of family members and acquaintances and combines multiple contact methods to send notifications. In this way, the optimal notification method can be selected by taking into consideration the geographical location information of family members and acquaintances.

[0053] The notification unit can analyze the social media activities of family members and acquaintances at the time of notification and provide relevant notification content. For example, the notification unit can analyze the social media activities of family members and acquaintances at the time of notification and provide relevant notification content. The generation AI can provide relevant notification content based on social media post content and activity history. For example, the generation AI analyzes the social media activities of family members and acquaintances and provides the most relevant notification content. In addition, the generation AI analyzes the social media activities of family members and acquaintances and provides optimal notification content in an emergency. Furthermore, the generation AI analyzes the social media activities of family members and acquaintances and provides a combination of multiple notification contents. In this way, it is possible to provide relevant notification content by analyzing the social media activities of family members and acquaintances.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit can refer to the user's past medical history and customize the condition in the event of an emergency and the method of acquiring images. For example, if the user has had a specific illness in the past, information related to that illness will be acquired preferentially. Also, if the user has had an allergic reaction in the past, information related to allergies will be acquired. Furthermore, the generation AI can suggest the optimal image acquisition method based on the user's past medical history. This makes it possible to collect more appropriate information by taking into account the user's past medical history.

[0056] The liaison department can select the most suitable hospital by referring to the hospital's past admission records. For example, the generation AI refers to the hospital's past admission records and selects a hospital with a good admission record. The generation AI also refers to the hospital's past admission records and selects a hospital with a track record of emergency response. The generation AI further refers to the hospital's past admission records and selects a hospital with high expertise. In this way, the most suitable hospital can be selected by referring to the hospital's past admission records.

[0057] The reception unit can filter the state of the user during sudden illness or when acquiring images based on the user's current activity status and environment. For example, if the user is exercising, the generation AI will prioritize acquiring data related to exercise. Also, if the user is at home, the generation AI will acquire data related to accidents in the home. Furthermore, if the user is out, the generation AI will acquire data related to the external environment. This allows more relevant information to be acquired by filtering information based on the user's current activity status and environment.

[0058] The analysis unit can adjust the level of detail of the advice based on the severity of the symptoms of the sudden illness. For example, in the case of mild symptoms, the generation AI provides simple advice. In addition, in the case of moderate symptoms, the generation AI provides detailed advice. Furthermore, in the case of severe symptoms, the generation AI provides advice for emergency responses. In this way, by adjusting the level of detail of the advice based on the severity of the symptoms of the sudden illness, more appropriate advice can be provided.

[0059] At the time of notification, the notification unit can analyze the contact information of family members and acquaintances and select the optimal notification method. For example, the generation AI analyzes the contact information of family members and acquaintances and selects the quickest method of contact. The generation AI also analyzes the contact information of family members and acquaintances and selects the optimal contact method in the event of an emergency. Furthermore, the generation AI analyzes the contact information of family members and acquaintances and combines multiple contact methods to send notifications. In this way, the optimal notification method can be selected by analyzing the contact information of family members and acquaintances.

[0060] At the time of communication, the liaison department can select the most suitable hospital by taking into account the geographic distribution of hospitals. For example, the generation AI takes into account the geographic distribution of hospitals and selects the closest hospital. The generation AI also takes into account the geographic distribution of hospitals and selects a hospital with good access. The generation AI also takes into account the geographic distribution of hospitals and selects a hospital with good transportation access. In this way, the most suitable hospital can be selected by taking into account the geographic distribution of hospitals.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The reception unit accepts the condition and images of the sudden illness. Conditions of sudden illness include heart attacks, strokes, and breathing difficulties, and images include photographs, X-ray images, and CT scan images. The reception unit can accept images taken with a smartphone and can also analyze the condition and images of the sudden illness using generative AI. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and provide appropriate treatment advice. For example, if there is a possibility of a heart attack, the advice provided is "Sit down and rest immediately, and call an ambulance." The generation AI uses text generation AI and multimodal generation AI to analyze images and text, and provide appropriate treatment advice. Step 3: Based on the information obtained by the analysis unit, the communication unit works in conjunction with GPS to pick out hospitals that can accept the patient and contact them. The generation AI identifies the patient's current location, searches for the nearest emergency hospital, and contacts that hospital, enabling rapid acceptance. Step 4: The notification unit automatically notifies family and acquaintances based on the information obtained by the contact unit. The generation AI sends messages such as "Mr. / Ms. XX has suddenly fallen ill and requires emergency treatment. He / she is currently on his / her way to XX Hospital" to pre-registered family and acquaintances.

[0063] (Example 2) The emergency hotline system according to the embodiment of the present invention is a system that utilizes a generation AI to provide an appropriate response to emergency medical care in the event of a sudden illness, even to people with little specialized knowledge. In this system, the condition and images of the sudden illness are read into the generation AI, which analyzes the information and presents appropriate treatment advice as an initial response. Next, the system works in conjunction with GPS to pick up hospitals that can accept the patient and contact them. In addition, the system is equipped with an automatic contact notification function for family and acquaintances. This series of steps prevents confusion in the event of a sudden illness and enables a quick and appropriate response. For example, the condition and images of the sudden illness are read into the generation AI. For example, if a patient suddenly experiences chest pain or is injured, the symptoms and images are photographed with a smartphone and input into the generation AI. The generation AI analyzes the input information and presents appropriate treatment advice as an initial response. For example, if the chest pain indicates a possible heart attack, the generation AI provides advice such as "sit down and rest immediately and call an ambulance." Next, the generation AI works in conjunction with GPS to pick up hospitals that can accept the patient and contact them. For example, the generation AI can identify the current location, search for the nearest emergency hospital, and contact that hospital, allowing for rapid acceptance. In addition, the generation AI is equipped with an automatic notification function to family and acquaintances. For example, if a person who suddenly falls ill is alone, the generation AI will automatically contact family and acquaintances who have been registered in advance. It will send a message such as, "Mr. / Ms. XX has suddenly fallen ill and needs emergency treatment. We are currently on our way to XX Hospital." This series of steps prevents confusion in the event of a sudden illness and enables rapid and appropriate response. By utilizing the generation AI, we provide a system that allows even people with little specialized knowledge to respond to emergencies with peace of mind. This allows the emergency hotline system to prevent confusion in the event of a sudden illness and enable rapid and appropriate response.

[0064] The emergency hotline system according to the embodiment includes a reception unit, an analysis unit, a communication unit, and a notification unit. The reception unit receives the condition and image of the sudden illness. The condition of the sudden illness includes, but is not limited to, for example, a heart attack, a stroke, and difficulty in breathing. The image includes, but is not limited to, for example, a photograph, an X-ray image, and a CT scan image. The reception unit can receive, for example, an image taken by a smartphone. The reception unit can also analyze the condition and image of the sudden illness using a generation AI. The analysis unit analyzes the information received by the reception unit using the generation AI and presents appropriate treatment advice. For example, the generation AI provides advice such as "sit down and rest immediately and call an ambulance" when there is a possibility of a heart attack based on the input information. The generation AI can generate appropriate treatment advice using a text generation AI (for example, LLM). The generation AI can also analyze images and text using a multimodal generation AI to provide appropriate treatment advice. The contact unit, based on the information obtained by the analysis unit, works in conjunction with GPS to pick up a hospital that can accept the patient and contact the hospital. For example, the generation AI identifies the current location, searches for the nearest emergency hospital, and contacts the hospital, allowing for rapid acceptance. The contact unit can use the generation AI to pick up a hospital that can accept the patient and contact the hospital. The notification unit automatically notifies family and acquaintances based on the information obtained by the communication unit. For example, if a person who has suddenly fallen ill is alone, the generation AI automatically contacts family and acquaintances who have been registered in advance. A message such as "Mr. / Ms. XX has suddenly fallen ill and needs emergency treatment. He / she is currently on his / her way to XX Hospital" is sent. The notification unit can automatically notify family and acquaintances using the generation AI. As a result, the emergency hotline system according to the embodiment can prevent confusion in the event of a sudden illness and enable rapid and appropriate response.

[0065] The reception unit receives the condition and image at the time of sudden illness. The condition at the time of sudden illness includes, but is not limited to, a heart attack, a stroke, and difficulty in breathing. The image includes, but is not limited to, a photograph, an X-ray image, a CT scan image, and the like. The reception unit can receive, for example, an image taken by a smartphone. Specifically, the camera function of the smartphone can be used to take a picture of the condition of the suddenly ill person and transmit the image to the system. This allows the reception unit to quickly grasp the condition of the suddenly ill person and start an appropriate response. The reception unit can also analyze the condition and image at the time of sudden illness using a generation AI. The generation AI can detect abnormalities from the transmitted image using, for example, image recognition technology and evaluate the condition of the suddenly ill person. For example, if there is a sign of a heart attack, the generation AI can analyze an image of the chest and detect abnormal heartbeat patterns and changes in blood flow. Furthermore, the generation AI can use text analysis technology to analyze the symptoms and condition entered by the suddenly ill person and provide appropriate treatment advice. This allows the reception unit to quickly and accurately grasp the condition of the suddenly ill person and start an appropriate response.

[0066] The analysis unit uses the generation AI to analyze the information received by the reception unit and presents appropriate treatment advice. For example, based on the input information, the generation AI provides advice such as "sit down and rest immediately and call an ambulance" if there is a possibility of a heart attack. The generation AI can generate appropriate treatment advice using a text generation AI (e.g., LLM). Specifically, the generation AI analyzes the symptoms and condition of the suddenly ill person and generates optimal treatment advice based on past data and medical knowledge. For example, if the suddenly ill person complains of chest pain, the generation AI evaluates the possibility of a heart attack and provides appropriate treatment advice. The generation AI can also analyze images and text using a multimodal generation AI to provide appropriate treatment advice. For example, it analyzes images of the suddenly ill person's chest to detect abnormal heart rate patterns and changes in blood flow, and provides appropriate treatment advice based on that. This allows the analysis unit to quickly and accurately analyze the condition of the suddenly ill person and provide appropriate treatment advice. In addition, the analysis unit can also perform long-term risk assessments and trend analysis by utilizing past data and medical knowledge. For example, based on past data on sudden illnesses, it can predict risk fluctuations for specific symptoms or conditions and devise future countermeasures. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0067] The communication unit, based on the information obtained by the analysis unit, works in conjunction with GPS to pick up hospitals that can accept the patient and contact them. For example, the generation AI can identify the current location, search for the nearest emergency hospital, and contact that hospital, allowing for rapid acceptance. Specifically, the communication unit uses GPS to identify the current location of the person who is suddenly ill, and searches for the nearest emergency hospital based on that information. The generation AI selects the most suitable hospital, taking into consideration the hospital's acceptance situation and available medical equipment. For example, if there is a suspicion of a heart attack, the generation AI preferentially selects a hospital with specialized cardiac medical equipment. The communication unit automatically contacts the selected hospital and informs it of the condition of the person who is suddenly ill and the necessary response. This allows the hospital to quickly prepare for response, and the acceptance of the person who is suddenly ill is smooth. In addition, the communication unit can use the generation AI to pick up hospitals that can accept the patient and contact them. This allows the communication unit to quickly select the most suitable medical institution according to the condition of the person who is suddenly ill and contact them. In addition, the liaison department updates information on hospital admission status and available medical equipment in real time, allowing it to select the most suitable medical institution based on the latest information at all times. This allows the liaison department to promptly and appropriately admit patients with sudden illnesses, improving the efficiency and effectiveness of emergency responses.

[0068] The notification unit automatically notifies family and acquaintances based on the information obtained by the contact unit. For example, if the suddenly ill person is alone, the generation AI automatically contacts family and acquaintances registered in advance. It sends a message such as "Mr. / Ms. XX has suddenly fallen ill and needs emergency treatment. He / she is currently on his / her way to XX Hospital." Specifically, the notification unit automatically contacts family and acquaintances registered in advance based on the condition of the suddenly ill person, his / her current location, and the hospital information of the hospital where he / she will be accepted. The generation AI refers to the contact list of the suddenly ill person, selects an appropriate means of contact (e.g., telephone, SMS, email, etc.), and sends the message. This allows family and acquaintances to quickly grasp the situation of the suddenly ill person and take the necessary measures. In addition, the notification unit can automatically notify family and acquaintances using the generation AI. For example, when the suddenly ill person arrives at the hospital, it can send a message such as "Mr. / Ms. XX has arrived at XX Hospital. He / she is currently being examined." This allows family and acquaintances to grasp the situation of the suddenly ill person in real time and respond with peace of mind. Furthermore, the notification unit can automatically notify family and acquaintances using the generation AI even if the condition of the suddenly ill person changes. For example, if the condition of a person with a sudden illness worsens, a message such as "Mr. / Ms. X's condition has worsened. Emergency treatment is currently being performed" can be sent. This allows the notification unit to inform family and acquaintances of the condition of the person with a sudden illness in real time, helping them respond quickly and appropriately.

[0069] The reception unit can load the condition or images at the time of sudden illness into the generation AI. The reception unit, for example, loads the condition and images at the time of sudden illness into the generation AI. The generation AI can analyze the condition and images at the time of sudden illness using a deep learning model, a natural language generation model, or the like. For example, the generation AI can provide appropriate treatment advice based on the condition and images at the time of sudden illness. This makes it possible to quickly collect information by loading the condition and images at the time of sudden illness into the generation AI.

[0070] The analysis unit can analyze information using the generation AI and present appropriate treatment advice as an initial action. The analysis unit can analyze information using the generation AI, for example, and present appropriate treatment advice as an initial action. The generation AI can provide appropriate treatment advice based on the input information. For example, in the case of a possible heart attack, the generation AI provides advice such as "sit down and rest immediately and call an ambulance." The generation AI can generate appropriate treatment advice using a text generation AI (for example, LLM). The generation AI can also analyze images and text using a multimodal generation AI and provide appropriate treatment advice. This enables a rapid response by analyzing information using the generation AI and presenting appropriate treatment advice as an initial action.

[0071] The communication unit can work in conjunction with GPS to pick up hospitals that can accept the patient and contact them. The communication unit can, for example, work in conjunction with GPS to pick up hospitals that can accept the patient and contact them. The generation AI can identify the current location, search for the nearest emergency response hospital, and contact that hospital, making it possible for the patient to be accepted quickly. The generation AI can pick up hospitals that can accept the patient and contact them based on the method of acquiring location information and details of the linked system. This allows the generation AI to work in conjunction with GPS to pick up hospitals that can accept the patient and contact them, making it possible for the patient to be accepted quickly.

[0072] The notification unit can automatically send contact notifications to family and acquaintances. The notification unit, for example, sends automatic contact notifications to family and acquaintances. The generation AI can automatically contact family and acquaintances registered in advance. For example, it can send a message such as "Mr. / Ms. XX has suddenly fallen ill and requires emergency treatment. He / she is currently on his / her way to XX Hospital." The generation AI can automatically send contact notifications by phone, email, SMS, or other methods. This enables rapid information sharing by automatically sending contact notifications to family and acquaintances.

[0073] The reception unit can estimate the user's emotions and adjust the state at the time of sudden illness and the timing of image acquisition based on the estimated user's emotions. The reception unit, for example, estimates the user's emotions and adjusts the state at the time of sudden illness and the timing of image acquisition based on the estimated user's emotions. The generation AI can estimate the user's emotions using facial expression recognition, voice analysis, text analysis, etc. For example, when the user is in a panicked state, the generation AI automatically acquires images and minimizes the user's operation. In addition, when the user is calm, the generation AI provides detailed instructions so that the user can acquire images at an appropriate time. Furthermore, when the user feels anxious, the generation AI guides the user to acquire images with simple operations. This makes it possible to collect more appropriate information by adjusting the state at the time of sudden illness and the timing of image acquisition based on the user's emotions.

[0074] The reception unit can analyze the user's past health data and select the acquisition method. The reception unit, for example, analyzes the user's past health data and selects the optimal acquisition method. The generation AI can analyze the user's past health data using electronic medical records, health check results, wearable device data, etc. For example, if the user has experienced a heart attack in the past, the generation AI preferentially acquires heart-related data. Also, if the user has had an allergic reaction in the past, the generation AI acquires allergy-related data. Furthermore, based on the user's past health data, the generation AI suggests the optimal image acquisition method. In this way, the optimal information acquisition method can be selected by analyzing the user's past health data.

[0075] The reception unit can perform filtering based on the user's current activity status and environment when acquiring the state of the user's sudden illness or the image. The reception unit performs filtering based on the user's current activity status and environment when acquiring the state of the user's sudden illness or the image, for example. The generation AI can grasp the user's current activity status and environment using GPS data, sensor data, user input information, etc. For example, when the user is exercising, the generation AI preferentially acquires data related to exercise. In addition, when the user is at home, the generation AI acquires data related to accidents in the home. In addition, when the user is out, the generation AI acquires data related to the external environment. As a result, more relevant information can be acquired by filtering information based on the user's current activity status and environment.

[0076] The reception unit can estimate the user's emotions and determine the priority of information to be acquired based on the estimated user's emotions. The reception unit, for example, estimates the user's emotions and determines the priority of information to be acquired based on the estimated user's emotions. The generation AI can estimate the user's emotions using facial expression recognition, voice analysis, text analysis, and the like. For example, when the user is in a panicked state, the generation AI preferentially acquires the most important information. Also, when the user is calm, the generation AI acquires detailed information. Furthermore, when the user is feeling anxious, the generation AI preferentially acquires information that gives a sense of security. In this way, by determining the priority of information based on the user's emotions, more important information can be acquired preferentially.

[0077] The reception unit can preferentially acquire highly relevant information in consideration of the geographical location information of the user when acquiring the state of the user's sudden illness or an image. For example, the reception unit preferentially acquires highly relevant information in consideration of the geographical location information of the user when acquiring the state of the user's sudden illness or an image. The generation AI can acquire the user's geographical location information using GPS data or a location information service. For example, when the user is in an urban area, the generation AI preferentially acquires emergency response information in the urban area. Also, when the user is in the suburbs, the generation AI preferentially acquires emergency response information in the suburbs. Furthermore, when the user is traveling, the generation AI preferentially acquires emergency response information at the travel destination. In this way, highly relevant information can be preferentially acquired by considering the geographical location information of the user.

[0078] The reception unit can analyze the user's social media activity when acquiring the state or image at the time of sudden illness, and acquire related information. The reception unit can analyze the user's social media activity when acquiring the state or image at the time of sudden illness, and acquire related information. The generation AI can analyze the social media posting content and activity history. For example, if the user posts about health on social media, the generation AI acquires that information. Also, if the user mentions a specific symptom on social media, the generation AI acquires that information. Furthermore, the user's social media activity can be analyzed to acquire related health information. As a result, related information can be acquired by analyzing the user's social media activity.

[0079] The analysis unit can estimate the user's emotions and adjust the way in which the treatment advice is expressed based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the way in which the treatment advice is expressed based on the estimated user's emotions. The generation AI can estimate the user's emotions using facial expression recognition, voice analysis, text analysis, and the like. For example, if the user is in a panicked state, the generation AI provides concise and clear advice. Also, if the user is calm, the generation AI provides detailed advice. Furthermore, if the user is feeling anxious, the generation AI provides advice that gives a sense of security. This allows more appropriate advice to be provided by adjusting the way in which the treatment advice is expressed based on the user's emotions.

[0080] The analysis unit can adjust the level of detail of the advice based on the severity of the symptoms of the sudden illness during analysis. The analysis unit, for example, adjusts the level of detail of the advice based on the severity of the symptoms of the sudden illness during analysis. The generation AI can adjust the level of detail of the advice based on the type and progression of the symptoms of the sudden illness. For example, in the case of mild symptoms, the generation AI provides simple advice. In addition, in the case of moderate symptoms, the generation AI provides detailed advice. Furthermore, in the case of severe symptoms, the generation AI provides advice for emergency responses. In this way, by adjusting the level of detail of the advice based on the severity of the symptoms of the sudden illness, more appropriate advice can be provided.

[0081] The analysis unit can apply different analysis algorithms depending on the type of sudden illness during analysis. The analysis unit, for example, applies different analysis algorithms depending on the type of sudden illness during analysis. The generation AI can apply different analysis algorithms depending on the type of sudden illness, such as a heart attack, stroke, or difficulty in breathing. For example, in the case of a heart-related sudden illness, the generation AI applies an analysis algorithm specialized for the heart. In addition, in the case of a respiratory-related sudden illness, the generation AI applies an analysis algorithm specialized for the respiratory system. In addition, in the case of a trauma-related sudden illness, the generation AI applies an analysis algorithm specialized for trauma. In this way, by applying different analysis algorithms depending on the type of sudden illness, more accurate analysis is possible.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. The analysis unit can, for example, estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. The generation AI can estimate the user's emotions using facial expression recognition, voice analysis, text analysis, and the like. For example, if the user is in a panic, the generation AI provides short, to-the-point advice. Also, if the user is calm, the generation AI provides detailed advice. Furthermore, if the user is feeling anxious, the generation AI provides slightly longer advice to provide a sense of security. This allows more appropriate advice to be provided by adjusting the length of the advice based on the user's emotions.

[0083] The analysis unit can determine the priority of advice based on the time of occurrence of the sudden illness during analysis. The analysis unit, for example, determines the priority of advice based on the time of occurrence of the sudden illness during analysis. The generation AI can identify the time of occurrence of the sudden illness based on a timestamp and the situation at the time of occurrence. For example, if the sudden illness occurs at night, the generation AI will preferentially provide advice for nighttime situations. Also, if the sudden illness occurs on a holiday, the generation AI will preferentially provide advice for holiday situations. Furthermore, if the sudden illness occurs on a weekday, the generation AI will preferentially provide advice for weekday situations. In this way, by determining the priority of advice based on the time of occurrence of the sudden illness, more appropriate advice can be provided.

[0084] The analysis unit can adjust the order of advice based on the relevance of the sudden illness during analysis. The analysis unit, for example, adjusts the order of advice based on the relevance of the sudden illness during analysis. The generation AI can evaluate the relevance of the sudden illness based on the similarity of symptoms and past data. For example, in the case of a heart-related sudden illness, the generation AI provides heart-related advice first. Also, in the case of a respiratory-related sudden illness, the generation AI provides respiratory-related advice first. Furthermore, in the case of a trauma-related sudden illness, the generation AI provides trauma-related advice first. In this way, by adjusting the order of advice based on the relevance of the sudden illness, more appropriate advice can be provided.

[0085] The communication unit can estimate the user's emotions and adjust the hospital pick-up criteria based on the estimated user's emotions. The communication unit, for example, estimates the user's emotions and adjusts the hospital pick-up criteria based on the estimated user's emotions. The generation AI can estimate the user's emotions using facial expression recognition, voice analysis, text analysis, and the like. For example, if the user is in a panicked state, the generation AI preferentially picks up the nearest hospital. Also, if the user is calm, the generation AI picks up a hospital with high expertise. Furthermore, if the user is feeling anxious, the generation AI picks up a hospital that gives a sense of security. In this way, by adjusting the hospital pick-up criteria based on the user's emotions, a more appropriate hospital can be selected.

[0086] The communication unit can check the hospital's acceptance status in real time at the time of contact and select the most suitable hospital. The communication unit, for example, checks the hospital's acceptance status in real time at the time of contact and selects the most suitable hospital. The generation AI can check the hospital's real-time acceptance status and available bed status. For example, the generation AI checks the hospital's real-time acceptance status and selects a hospital that can accept the patient. The generation AI also checks the hospital's available bed status and selects the most suitable hospital. Furthermore, the generation AI checks the hospital's emergency response capabilities and selects the most suitable hospital. In this way, the hospital's acceptance status can be checked in real time and the most suitable hospital can be selected.

[0087] The communication unit can select an appropriate hospital based on the hospital's specialty at the time of contact. The communication unit, for example, selects an appropriate hospital based on the hospital's specialty at the time of contact. The generation AI can select an appropriate hospital based on the hospital's specialty. For example, in the case of a heart-related sudden illness, the generation AI selects a hospital specializing in heart disease. In addition, in the case of a respiratory-related sudden illness, the generation AI selects a hospital specializing in respiratory disease. Furthermore, in the case of a trauma-related sudden illness, the generation AI selects a hospital specializing in trauma. In this way, by selecting an appropriate hospital based on the hospital's specialty, more specialized treatment can be received.

[0088] The contact unit can estimate the user's emotions and determine the priority of contacts based on the estimated user's emotions. The contact unit can, for example, estimate the user's emotions and determine the priority of contacts based on the estimated user's emotions. The generation AI can estimate the user's emotions using facial expression recognition, voice analysis, text analysis, and the like. For example, when the user is in a panicked state, the generation AI preferentially selects the most important contacts. Also, when the user is calm, the generation AI selects detailed contacts. Furthermore, when the user is feeling anxious, the generation AI preferentially selects contacts that give a sense of security. This enables more appropriate contact by determining the priority of contacts based on the user's emotions.

[0089] The communication unit can select the optimal hospital by taking into consideration the geographical distribution of hospitals when making a contact. For example, the communication unit selects the optimal hospital by taking into consideration the geographical distribution of hospitals when making a contact. The generation AI can consider the geographical distribution of hospitals by using map data or location information services. For example, the generation AI considers the geographical distribution of hospitals and selects the nearest hospital. In addition, the generation AI considers the geographical distribution of hospitals and selects a hospital with good access. In addition, the generation AI considers the geographical distribution of hospitals and selects a hospital with good transportation access. In this way, the optimal hospital can be selected by taking into consideration the geographical distribution of hospitals.

[0090] The communication unit can select the most suitable hospital by referring to the hospital's past acceptance record when contacting the hospital. For example, the communication unit selects the most suitable hospital by referring to the hospital's past acceptance record when contacting the hospital. The generation AI can refer to the past acceptance record based on the hospital's historical data and number of acceptances. For example, the generation AI refers to the hospital's past acceptance record and selects a hospital with a high acceptance record. In addition, the generation AI refers to the hospital's past acceptance record and selects a hospital with a track record of emergency response. In addition, the generation AI refers to the hospital's past acceptance record and selects a hospital with high expertise. In this way, the most suitable hospital can be selected by referring to the hospital's past acceptance record.

[0091] The notification unit can estimate the user's emotions and adjust the way the notification is expressed based on the estimated user's emotions. The notification unit can, for example, estimate the user's emotions and adjust the way the notification is expressed based on the estimated user's emotions. The generation AI can estimate the user's emotions using facial expression recognition, voice analysis, text analysis, and the like. For example, if the user is in a panicked state, the generation AI provides a concise and clear notification. Also, if the user is calm, the generation AI provides a detailed notification. Furthermore, if the user is feeling anxious, the generation AI provides a notification that gives a sense of security. This enables more appropriate notifications by adjusting the way the notification is expressed based on the user's emotions.

[0092] The notification unit can analyze the contact information of family members and acquaintances at the time of notification and select the optimal notification method. For example, the notification unit analyzes the contact information of family members and acquaintances at the time of notification and selects the optimal notification method. The generation AI can select the optimal notification method based on contact information such as phone numbers, email addresses, and SNS accounts. For example, the generation AI analyzes the contact information of family members and acquaintances and selects the method that allows the quickest contact. The generation AI also analyzes the contact information of family members and acquaintances and selects the optimal contact method in the event of an emergency. Furthermore, the generation AI analyzes the contact information of family members and acquaintances and combines multiple contact methods to send notifications. In this way, the optimal notification method can be selected by analyzing the contact information of family members and acquaintances.

[0093] The notification unit can provide different notification contents depending on the type of sudden illness at the time of notification. The notification unit, for example, provides different notification contents depending on the type of sudden illness at the time of notification. The generation AI can provide different notification contents depending on the type of sudden illness, such as a heart attack, stroke, or difficulty in breathing. For example, in the case of a heart-related sudden illness, the generation AI provides notification contents specialized for the heart. Also, in the case of a respiratory-related sudden illness, the generation AI provides notification contents specialized for the respiratory system. Furthermore, in the case of a trauma-related sudden illness, the generation AI provides notification contents specialized for the trauma. This allows for more appropriate notifications by providing different notification contents depending on the type of sudden illness.

[0094] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user's emotions. The notification unit can, for example, estimate the user's emotions and determine the priority of notifications based on the estimated user's emotions. The generation AI can estimate the user's emotions using facial expression recognition, voice analysis, text analysis, and the like. For example, if the user is in a panicked state, the generation AI prioritizes the most important notification. Also, if the user is calm, the generation AI provides detailed notifications. Furthermore, if the user is feeling anxious, the generation AI prioritizes notifications that provide a sense of security. This enables more appropriate notifications by determining the priority of notifications based on the user's emotions.

[0095] The notification unit can select the optimal notification method by taking into consideration the geographical location information of family members and acquaintances at the time of notification. For example, the notification unit selects the optimal notification method by taking into consideration the geographical location information of family members and acquaintances at the time of notification. The generation AI can select the optimal notification method based on the geographical location information. For example, the generation AI takes into consideration the geographical location information of family members and acquaintances and selects the method that allows for the quickest contact. In addition, the generation AI takes into consideration the geographical location information of family members and acquaintances and selects the optimal contact method in the event of an emergency. Furthermore, the generation AI takes into consideration the geographical location information of family members and acquaintances and combines multiple contact methods to send notifications. In this way, the optimal notification method can be selected by taking into consideration the geographical location information of family members and acquaintances.

[0096] The notification unit can analyze the social media activities of family members and acquaintances at the time of notification and provide relevant notification content. For example, the notification unit can analyze the social media activities of family members and acquaintances at the time of notification and provide relevant notification content. The generation AI can provide relevant notification content based on social media post content and activity history. For example, the generation AI analyzes the social media activities of family members and acquaintances and provides the most relevant notification content. In addition, the generation AI analyzes the social media activities of family members and acquaintances and provides optimal notification content in an emergency. Furthermore, the generation AI analyzes the social media activities of family members and acquaintances and provides a combination of multiple notification contents. In this way, it is possible to provide relevant notification content by analyzing the social media activities of family members and acquaintances.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The reception unit can refer to the user's past medical history and customize the condition in the event of an emergency and the method of acquiring images. For example, if the user has had a specific illness in the past, information related to that illness will be acquired preferentially. Also, if the user has had an allergic reaction in the past, information related to allergies will be acquired. Furthermore, the generation AI can suggest the optimal image acquisition method based on the user's past medical history. This makes it possible to collect more appropriate information by taking into account the user's past medical history.

[0099] The analysis unit can estimate the user's emotions and adjust the way in which treatment advice is expressed based on the estimated user's emotions. For example, if the user is in a panicked state, the generation AI provides concise and clear advice. If the user is calm, the generation AI provides detailed advice. Furthermore, if the user is feeling anxious, the generation AI provides advice that gives a sense of security. This makes it possible to provide more appropriate advice by adjusting the way treatment advice is expressed based on the user's emotions.

[0100] The liaison department can select the most suitable hospital by referring to the hospital's past admission records. For example, the generation AI refers to the hospital's past admission records and selects a hospital with a good admission record. The generation AI also refers to the hospital's past admission records and selects a hospital with a track record of emergency response. The generation AI further refers to the hospital's past admission records and selects a hospital with high expertise. In this way, the most suitable hospital can be selected by referring to the hospital's past admission records.

[0101] The notification unit can estimate the user's emotions and adjust the way notifications are expressed based on the estimated user's emotions. For example, if the user is in a panicked state, the generation AI provides a concise and clear notification. If the user is calm, the generation AI provides a detailed notification. Furthermore, if the user is feeling anxious, the generation AI provides a notification that gives a sense of security. This allows for more appropriate notifications by adjusting the way notifications are expressed based on the user's emotions.

[0102] The reception unit can filter the state of the user during sudden illness or when acquiring images based on the user's current activity status and environment. For example, if the user is exercising, the generation AI will prioritize acquiring data related to exercise. Also, if the user is at home, the generation AI will acquire data related to accidents in the home. Furthermore, if the user is out, the generation AI will acquire data related to the external environment. This allows more relevant information to be acquired by filtering information based on the user's current activity status and environment.

[0103] The analysis unit can adjust the level of detail of the advice based on the severity of the symptoms of the sudden illness. For example, in the case of mild symptoms, the generation AI provides simple advice. In addition, in the case of moderate symptoms, the generation AI provides detailed advice. Furthermore, in the case of severe symptoms, the generation AI provides advice for emergency responses. In this way, by adjusting the level of detail of the advice based on the severity of the symptoms of the sudden illness, more appropriate advice can be provided.

[0104] The communication unit can estimate the user's emotions and adjust the hospital picking criteria based on the estimated user's emotions. For example, if the user is in a panic, the generation AI will preferentially pick the nearest hospital. If the user is calm, the generation AI will pick a hospital with high expertise. Furthermore, if the user is feeling anxious, the generation AI will pick a hospital that gives a sense of security. In this way, by adjusting the hospital picking criteria based on the user's emotions, a more appropriate hospital can be selected.

[0105] At the time of notification, the notification unit can analyze the contact information of family members and acquaintances and select the optimal notification method. For example, the generation AI analyzes the contact information of family members and acquaintances and selects the quickest method of contact. The generation AI also analyzes the contact information of family members and acquaintances and selects the optimal contact method in the event of an emergency. Furthermore, the generation AI analyzes the contact information of family members and acquaintances and combines multiple contact methods to send notifications. In this way, the optimal notification method can be selected by analyzing the contact information of family members and acquaintances.

[0106] The reception unit can estimate the user's emotions and determine the priority of information to be acquired based on the estimated user's emotions. For example, if the user is in a panicked state, the generation AI will prioritize acquiring the most important information. If the user is calm, the generation AI will acquire detailed information. Furthermore, if the user is feeling anxious, the generation AI will prioritize acquiring information that gives a sense of security. In this way, by determining the priority of information based on the user's emotions, it is possible to prioritize acquiring more important information.

[0107] At the time of communication, the liaison department can select the most suitable hospital by taking into account the geographic distribution of hospitals. For example, the generation AI takes into account the geographic distribution of hospitals and selects the closest hospital. The generation AI also takes into account the geographic distribution of hospitals and selects a hospital with good access. The generation AI also takes into account the geographic distribution of hospitals and selects a hospital with good transportation access. In this way, the most suitable hospital can be selected by taking into account the geographic distribution of hospitals.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The reception unit accepts the condition and images of the sudden illness. Conditions of sudden illness include heart attacks, strokes, and breathing difficulties, and images include photographs, X-ray images, and CT scan images. The reception unit can accept images taken with a smartphone and can also analyze the condition and images of the sudden illness using generative AI. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and provide appropriate treatment advice. For example, if there is a possibility of a heart attack, the advice provided is "Sit down and rest immediately, and call an ambulance." The generation AI uses text generation AI and multimodal generation AI to analyze images and text, and provide appropriate treatment advice. Step 3: Based on the information obtained by the analysis unit, the communication unit works in conjunction with GPS to pick out hospitals that can accept the patient and contact them. The generation AI identifies the patient's current location, searches for the nearest emergency hospital, and contacts that hospital, enabling rapid acceptance. Step 4: The notification unit automatically notifies family and acquaintances based on the information obtained by the contact unit. The generation AI sends messages such as "Mr. / Ms. XX has suddenly fallen ill and requires emergency treatment. He / she is currently on his / her way to XX Hospital" to pre-registered family and acquaintances.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements including the reception unit, analysis unit, contact unit, and notification 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 receives the state and images of the patient at the time of sudden illness using the camera 42 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the received information using a generation AI, and presents appropriate treatment advice. The contact unit is realized by the specific processing unit 290 of the data processing device 12, and picks up hospitals that can accept the patient in conjunction with GPS and contacts them. The notification unit is realized by the control unit 46A of the smart device 14, and automatically notifies family and acquaintances. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements including the reception unit, analysis unit, contact unit, and notification 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 receives the state and image of the patient at the time of sudden illness using the camera 42 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the received information using a generation AI, and presents appropriate treatment advice. The contact unit is realized by the specific processing unit 290 of the data processing device 12, and picks up hospitals that can accept the patient in conjunction with GPS and contacts them. The notification unit is realized by the control unit 46A of the smart glasses 214, and automatically notifies family and acquaintances. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements including the above-mentioned reception unit, analysis unit, contact unit, and notification unit 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 receives the state and images of the sudden illness using the camera 42 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the received information using a generation AI, and presents appropriate treatment advice. The contact unit is realized by the specific processing unit 290 of the data processing device 12, and picks up hospitals that can accept the patient in conjunction with GPS and contacts them. The notification unit is realized by the control unit 46A of the headset type terminal 314, and automatically notifies family and acquaintances. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements including the reception unit, analysis unit, communication unit, and notification 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 receives the state and images of the patient at the time of sudden illness using the camera 42 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the received information using a generation AI, and presents appropriate treatment advice. The communication unit is realized by the specific processing unit 290 of the data processing device 12, and picks up hospitals that can accept the patient in conjunction with GPS and makes contact with them. The notification unit is realized by the control unit 46A of the robot 414, and automatically notifies family and acquaintances. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Appendix 1) A reception unit that receives a state or an image at the time of sudden illness; an analysis unit that analyzes the information received by the reception unit and presents treatment advice; a contact unit that, based on the information obtained by the analysis unit, picks up hospitals that can accept the patient in cooperation with a GPS and contacts the hospitals; A notification unit that automatically notifies family members or acquaintances based on the information obtained by the notification unit. A system characterized by: (Appendix 2) The reception unit is The condition or image at the time of sudden illness is input into the generation AI 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze information using generative AI and provide appropriate initial advice 2. The system of claim 1, (Appendix 4) The communication unit includes: Linked with GPS, it picks up hospitals that can accept patients and contacts them 2. The system of claim 1, (Appendix 5) The notification unit is Automatically notify family and friends 2. The system of claim 1, (Appendix 6) The reception unit is Estimating the user's emotions and adjusting the state of the user during sudden illness and the timing of image acquisition based on the estimated user emotions 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past health data and select the acquisition method 2. The system of claim 1, (Appendix 8) The reception unit is Filtering the status of emergencies and image acquisition based on the user's current activity status and environment 2. The system of claim 1, (Appendix 9) The reception unit is Estimate the user's emotions and determine the priority of information to be acquired based on the estimated user's emotions. 2. The system of claim 1, (Appendix 10) The reception unit is When acquiring information about the state of an emergency or images, the system prioritizes obtaining information that is highly relevant based on the user's geographic location. 2. The system of claim 1, (Appendix 11) The reception unit is When acquiring images or recording a sudden illness, the system analyzes the user's social media activity to obtain relevant information. 2. The system of claim 1, (Appendix 12) The analysis unit is To estimate a user's emotion and adjust a method of expressing treatment advice based on the estimated user's emotion. 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, the level of detail in advice is adjusted based on the severity of the acute illness symptoms. 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, different analysis algorithms are applied depending on the type of acute illness. 2. The system of claim 1, (Appendix 15) The analysis unit is Estimate the user's emotions and adjust the length of the advice based on the estimated user emotions. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, advice is prioritized based on the timing of the acute illness 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the order of advice based on acute illness relevance 2. The system of claim 1, (Appendix 18) The communication unit includes: Estimating user sentiment and adjusting hospital pick-up criteria based on the estimated user sentiment 2. The system of claim 1, (Appendix 19) The communication unit includes: When contacting the hospital, check the hospital's acceptance status in real time and select the hospital. 2. The system of claim 1, (Appendix 20) The communication unit includes: When contacting, select the appropriate hospital based on the hospital's specialty 2. The system of claim 1, (Appendix 21) The communication unit includes: To estimate a user's emotion and determine a priority of contacts based on the estimated user's emotion. 2. The system of claim 1, (Appendix 22) The communication unit includes: When contacting, select the most suitable hospital, taking into account the geographical distribution of hospitals. 2. The system of claim 1, (Appendix 23) The communication unit includes: When contacting the hospital, the most suitable hospital will be selected based on the hospital's past acceptance records. 2. The system of claim 1, (Appendix 24) The notification unit is Infer user emotions and adjust notification presentation based on the inferred user emotions 2. The system of claim 1, (Appendix 25) The notification unit is When notifying, the contact information of family and acquaintances is analyzed to select the most appropriate notification method. 2. The system of claim 1, (Appendix 26) The notification unit is When notifying, provide different notification contents depending on the type of emergency illness 2. The system of claim 1, (Appendix 27) The notification unit is Infer user emotions and prioritize notifications based on the inferred user emotions 2. The system of claim 1, (Appendix 28) The notification unit is When notifying users, the system selects the optimal notification method based on the geographic location information of family and friends. 2. The system of claim 1, (Appendix 29) The notification unit is When notifying you, analyze the social media activities of your family and friends to provide you with relevant notification content 2. The system of claim 1, [Explanation of symbols]

[0182] 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 reception unit that receives a state or an image at the time of sudden illness; an analysis unit that analyzes the information received by the reception unit and presents treatment advice; a contact unit that, based on the information obtained by the analysis unit, picks up hospitals that can accept the patient in cooperation with a GPS and contacts the hospitals; A notification unit that automatically notifies family members or acquaintances based on the information obtained by the notification unit. A system characterized by:

2. The reception unit is The condition or image of the patient at the time of sudden illness is read into the AI 2. The system of claim 1 .

3. The analysis unit is Analyze the information using generative AI and provide appropriate initial advice on how to proceed 2. The system of claim 1 .

4. The communication unit includes: Linked with GPS, it picks up hospitals that can accept patients and contacts them 2. The system of claim 1 .

5. The reception unit is Estimating the user's emotions and adjusting the state of the user during sudden illness and the timing of image acquisition based on the estimated user emotions 2. The system of claim 1 .

6. The reception unit is Analyze the user's past health data and select the acquisition method 2. The system of claim 1 .

7. The reception unit is Filtering the status of emergencies and image acquisition based on the user's current activity status and environment 2. The system of claim 1 .

Images